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Journal of General Management
Vol. 26 No.2 Winter 2000
A Reappraisal ofHRM
Models in Britain
by
Pawan s. Budhwar
Human Resource Management is still struggling to find a
strategic role.
For a better understanding ofthe subj ect, both management
practitioners
and scholars need to study human resource management (HRM)
in
context [1]. The dynamics of both the local/regional and
international/
global business context in which the firm operates should be
given a
serious consideration. Similarly, there is a need to use multiple
levels of
analysis when studying HRM: the external social, political,
cultural, and
economic environment; and the industry. Examining HRM out-
of-context
could be misleading and fail to advance understanding. A key
question is
how to examine HRM in context? One way is by examining the
main
models of HRM in different settings. However, there is no
existing
framework that can enable such an evaluation to take place. An
attempt
has been made in this paper to provide such a framework and
empirically
examine it in the British context.
This paper is divided into three parts. Initially, it summarises
the
main developments in the field of HRM. Then, it highlights the
key
emphasis of five models of HRM (namely, the 'Matching
model'; the
'Harvard model'; the 'Contextual model'; the '5-P model'; and
the
'European model' ofHRM). Lastly, we will address the
operationalisation
of the key issues and emphases of the aforementioned models by
examining their applicability in six industries ofthe British
manufacturing
sector. The evaluation highlights the context specific nature of
British
HRM.
This introduction looks at the need to identify the core emphasis
of
the main HRM models that could be used to examine their
applicability in
different national contexts. Developments in the field of HRM
are now
well documented in the literature [2, 3]. The debate relating to
the nature
ofHRM continues today, although the focus of the debate has
changed
over a period of time. At present, the contribution ofHRM i n
improving
Pawan S. Budhwar is Lecturer in Organizational Behaviour and
HRM at CardiffBusiness School, UK.
Journal of General Management
Vol. 26 No.2 Winter 2000
the firm's performance and the overall success of any
organization
(alongside other factors) is being highlighted in the literature
[4, 5].
Alongside these debates, a number of important theoretical
developments have taken place in the field of HRM. For
example, a
number ofmodels ofHRM have been developed over the last 15
years or
so. Some of the main models are: the 'Matching model'; the
'Harvard
model'; the 'Contextual model'; the '5-P model'; and the
'European
model' ofHRM [6, 7]. All these models have been developed in
the US
and the UK. These models ofHRM are proj ected to be useful
for analysis
both between and within nations. However, the developers of
these
models do not provide clear guidelines regarding their
operationalisation
in different contexts. Moreover, it is interesting to note that,
although a
large number of scholars refer to these models, very few have
tested their
practical applicability (exceptions being Benkhoff [8]; Monks
[9]; Truss
et al. [10]). For the development of relevant management
practices there
is then a clear need not only to highlight the main emphasis of
the HRM
models but also to show their operationalisation. Such an
analysis will help
to examine the applicability of these models in other parts of
the world.
With the increasing levels ofglobalisation ofbusiness such
investigations
have become an imperative.
Moreover, although the present literature shows an emphasis on
themes such as 'strategic HRM' (SHRM), the majority of
researchers
persist in examining only the traditional 'hard' and' soft' models
ofHRM
[11]. For the growth and development of SHRM, there is a
strong need
to examine the applicability of those models ofHRM which can
help to
assess the extent to which it has really become strategic in
different parts
of the world, and the main factors and variables which
determine HRM
in different settings. This will not only test the applicability of
HRM
approaches in different regions, but will also help to highlight
the context
specific nature of HRM practices.
The aims of this paper are twofold. First, to identify the core
emphasis offive main models ofHRM which can be used to
examine their
applicability in different national contexts. Second, to test
empirically the
applicability of these models of HRM in the British context.
Before
answering why this investigation is being conducted in the UK,
the main
models of HRM are briefly analysed.
Models of HRM
Five models ofHRM, which are widely documented in the
literature are
chosen for analysis. They are: the 'Matching model'; the
'Harvard
model'; the 'Contextual model'; the '5-P model'; and the
'European
model' ofHRM [12,13, 14]. The reason for the selection and
analysis of
thesemodelsis two-fold.First, it will help to highlight their main
contribution
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Journal of General Management
Vol. 26 No.2 Winter 2000
to the development of SHRM as a distinct discipline. Second, it
will help
to identify the main research questions suitable for examining
these
models in different national settings. The analysis begins with
one of the
traditional models ofHRM.
The strategic fit of HRM
The main contributors to the 'Matching model' ofHRM come
from the
Michigan and New York schools. Fombrun et al. 's [15] model
highlights
the 'resource' aspect ofHRM and emphasises the efficient
utilisation of
human resources (like otherresources) to meet organizational
objectives.
The matching model is mainly based on Chandler's [16]
argument that an
organization's structure is an outcome of its strategy. Fombrun
et al.
expanded this premise and developed the matching model of
strategic
RRM, which emphasises a 'tight fit' between organizational
strategy,
organizational structure and HRM system, where both structure
and
HRM are dependent on the organization strategy. The main aim
of the
matching model is therefore to develop an appropriate 'Human
Resource
System' that will characterise those HRM strategies that
contribute to the
most efficient implementation ofbusiness strategies. The
Schuler group
made further developments to the matching model and its core
theme of
'strategic fit' in the late 19?Os [17]. The core issues emerging
from the
matching models are:
1. Do organizations show a 'tight fit' between their HRM and
organization strategy where the former is dependent on the
latter? Do personnellHR managers believe they should
develop HRM systems only for the effective implementation
of their organization strategies?
.2. Do organizations consider their HRs as a cost and use them
sparingly? Or, do they devote resources to the training of
their HRs to make the best use of them?
3. Do HRM strategies vary across different levels of
employees?
The soft variant of HRM
Beer et al. [18] articulated the 'Harvard Model' of HRM. It is
also
denoted as the 'Soft' variant ofHRM [19], mainly because it
stresses the
'human' aspect of HRM and is more concerned with the
employer-
employee relationship. The model highlights the interests of
different
stakeholders in the organization (such as shareholders,
management,
employee groups, government, community and unions) and how
their
interests are related to the objectives of management. It also
recognises
the influence ofsituational factors (such as the market situation)
on HRM
policy choices. According to this model, the actual content of
HRM is
described in relation to four policy areas i.e. human resource
flows,
Journal of General Management
Vol. 26 No.2 Winter 2000
reward systems, employees' influence and work systems. Each
of the
four policy areas is characterised by a series of tasks to which
managers
must attend. The outcomes that these four HR policies need to
achieve
are commitment, competence, congruence, and cost
effectiveness. The
model allows for analysis of these outcomes at both
organizational and
societal levels. As this model acknowledges the role ofsocietal
outcomes,
it can provide a useful basis for comparative analysis of HRM
[20]. The
key issues emerging from this model which can be used for
examining its
applicability in different contexts are:
1. What is the influence ofdifferent stakeholders and situational
and contingent variables on HRM policies?
2. To what extent is communication with employees used as a
means to maximise commitment?
3. What level of emphasis is given to employee development
through involvement, empowerment and devolution?
The contextual model of HRM
Researchers at the Centre for Corporate Strategy and Change at
the
Warwick Business School developed this model. They examined
strategy
making in complex organizations and related this to the ability
to transform
HRM practices [21,22]. Hendry and associates argue that HRM
should
not be labelled as a single form of activity. Organizations may
follow a
number of different pathways in order to achieve the same
results. This
is mainly due to the existence of a number of linkages between
the outer
environmental context (socio-economic, technological,
political-legal and
competitive)and inner organizationalcontext (culture, structure,
leadership,
task-technology and business output). These linkages directly
contribute
to forming the content of an organization's HRM. The core
issues
emerging from this model are:
1. What is the influence of economic (competitive conditions,
ownership and control, organization size and structure,
organizational growth path or stage in the life cycle and the
structure of the industry), technological (type of production
systems) and socio-political (national education and training
set-up) factors on HRM strategies?
2. What are the linkages between organizational contingencies
(such as size, nature, positioning ofHR, and HR strategies)
and HRM strategies?
Strategic integration of HRM
The existing literature reveals a trend in which HRM is
becoming an
integral part of business strategy - hence, the emergence of the
term
SHRM. It is largely concerned with 'integration' and
'adaptation'. The
purpose of SHRM is to ensure that [23]:
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Journal of General Management
VoL 26 No.2 Winter2000
1. HRM is fully integrated with the strategy and strategic needs
of the firm;
2. HR policies are coherent both across policy areas and across
hierarchies; and
3. HR practices are adjusted, accepted, and used by line
managers and employees as part of their every day work.
Based on such premises, Schuler [24] developed a 5-P model of
SHRM that melds five HR activities (philosophies, policies,
programs,
practices and processes) with strategic needs. This model, to a
great
extent, explains the significance ofthese five SHRM activities in
achieving
the organization's strategic needs, and shows the inter-
relatedness of
activities that are often treated separately in the literature. This
is helpful
in understanding the complex interaction between
organizational strategy
and SHRM activities.
The model raises two important issues (also suggested by many
other authors in the field) for SHRM comparisons. These are:
1. What is the level of integration of HRM into the business
strategy?
2. What is the level ofresponsibility for HRM devolved to line
managers?
European model of HRM
Based on the growing importance of HRM and its contribution
towards
economic success and the drive towards Europeanisation,
Brewster [25]
proposes a 'European model ofHRM'. His model is based on the
premise
that European organizations operate with restricted autonomy.
They are
constrained at both the international (European Union) and
national levels
by national culture and legislation, at the organization level by
patterns of
ownership, and at the HRM level by trade union involvement
and
consultative arrangements [26, p. 3]. Brewster suggests the need
to
accommodate such constraints when forming a model ofHRM.
He also
talks about 'outer' (legalistic framework, vocational training
programs,
social security provisions and the ownership patterns) and
'internal' (such
as union influence and employee involvement in decision
making) constraints
on HRM. Based on such constraints, Brewster's model
highlights the
influence of factors such as national culture, ownership
structures, the
role of the state and trade unions on HRM, in different national
settings.
The European model shows an interaction between HR
strategies,
business strategy and HR practice and their interaction with an
external
environment constituting national culture, power systems,
legislation,
education, employee representation and the constraints
previously
mentioned. It places HR strategies in close interaction with the
relevant
Journal of General Management
Vol. 26 No.2 Winter 2000
organizational strategy and external environment. One
important aim of
this model is to show factors external to the organization as a
part of the
HRM model, rather than as a set of external influences upon it.
From the above analyses, it can be seen that there is an element
of
both the contextual and 5-P models of HRM present in
Brewster's
European model. Apart from the emphasis on 'strategic HRM',
one main
issue important for cross-national HRM comparisons emerges
from
Brewster's model. This is:
• What is the influence of international institutions, national
factors (such as culture, legal set up, economic environment
and ownership patterns), and national institutions (such as the
educational and vocational set-up, labour markets and trade
unions) on HRM strategies and HRM practices?
Recently, Budhwar and associates [27, 28,29,30] have proposed
a framework for examining cross-national HRM. They have
identified
three levels of factors and variables that are known to influence
HRM
policies and practices and which are worth considering for
cross-national
HRM examinations. These are national factors (such as national
culture,
national institutions, business sectors and dynamic of the
business
environment), contingent variables (such as the age, size,
nature, ownership,
and life cycle stage of the organization, the presence of trade
unions and
HR strategies, and the interests of different stakeholders) and
organizational strategies and policies (related to primary HR
functions,
internal labour markets, levels ofintegration and devolvement,
and nature
ofwork). This framework is used to examine the applicability
ofthe issues
arising from the five HRM models in British organizations. But
why
conduct this form of investigation, and in the British context?
As mentioned already, there is a scarcity of this type of
research.
So far, only Truss et al. [31] have examined the applicability of
some of
the models of HRM in a few UK case companies. Apart from
their
research, there is scarcely any study that conducts the type
ofinvestigation
described here. There are, then, two main reasons for
conducting this
investigation in British companies. First, a UK sample
possesses the
characteristics suitable to test the operationalisation ofthe main
emphases
and critical issues ofthe five models ofHRM. Second, the HRM
function
in the UK is under intense pressure due to competitive
conditions, and the
restructuring and rightsizing programmes going on in British
organizations,
as well as the pressure on British firms from EU and other
international
players to stay competitive and meet the EU regulation
regarding the
management ofhuman resources. In such dynamic business
conditions it
is worth examining the HRM function in context. Moreover,
since the five
models have been developed among Anglo-Saxon nations, it is
sensible to
test them initially in these countries before recommending their
testing in
others parts of the world.
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Journal of General Management
Vol. 26 No.2 Winter 2000
The Research Methodology
Sample and data collection
A mixed methodology, using a questionnaire survey and in-
depth interviews,
was adopted. During the first phase of the research, a
questionnaire
survey was conducted between August 1994 and December 1994
in
British firms having 200 or more employees in six industries in
the
manufacturing sector (food processing, plastics, steel, textiles,
pharmaceuticals and footwear). The respondents were the top
personnel
specialist (one each) from each firm. The response rate ofthe
questionnaire
survey was approximately 19 per cent (93 out of 500
questionnaires). The
items for the questionnaire were constructed from existing
sources, such
as those developed by Cranfield researchers in their study
ofcomparative
European HRM [32] and other studies (see for example [33,
34]). The
questionnaire consisted of 13 sections. These were: HR
department
structure, role of the HR function in corporate strategy,
recruitment and
selection, pay and benefits, training and development,
performance
appraisal, employee relations, HRM strategy, influence
ofnational culture,
national institutions, competitive pressures and business sector
on HRM,
organizational details. Public limited companies represented
approximately
one-third of the sample, with the remainder from the private
sector. The
industry-wide distribution of respondents is shown in Table 1.
Table 1: Sample Industry Distribution
Indtitry Percentage .
Food Processing 17.2
Plastics 17.2
Steel 16.1
Textiles 17.2
Pharmaceuticals 21.5
Footwear 10.8
Analysis of the demographic features of the sample suggests
that
the sample was representative ofthe total population. Sixty-two
per cent
of sample organizations were medium-sized and employed 200-
499
employees, 14 per cent employed 500-999 employees, 15 per
cent 1000-
4999 employees, and 8 per cent employed 5000 or more
employees.
In the second phase of the research, 24 in-depth interviews were
conducted with personnel specialists representative of those
firms which
participated in the first phase of the research. The interviews
examined
six themes, viz. the nature of the personnel function, integration
ofHRM
into the corporate strategy, devolvement ofHRM to line
managers, and
the influences of national culture, national institutions and
business
environment dynamic on HRM.
Journal of General Management
Vol. 26 No.2 Winter 2000
Measures
Multiple regression analysis and descriptive statistics are used
to analyse
questionnaire data. Table 1 in the Appendix shows the main
dependent
and independent variables used for multiple regression analysis.
Table 2
in the Appendix presents the mean scores of respondents
regarding the
influence of different aspects of national factors (culture,
institutions,
business environment dynamic and business sector) and HR
strategies on
HRM policies and practices. The qualitative data is content
analysed. In
the discussion, survey results are complemented by key
messages coming
from the qualitative interviews.
Findings of the Study
The matching models suggest a strong dependence ofHRM on
organization
strategy, i.e, HRM is mainly developed for the effective
implementation
of organization strategies. The results show that in 34.6 per cent
of the
organizations under study personnel is involved from the outset
in the
formation of corporate strategy, and 42 per cent of
organizations actively
involve HRM during the implementation stage of their
organizational
strategies. Such a trend of 'active' personnel management is
further
evident from 55 per cent of sample organizations having
personnel
representation at board level. Moreover, 81.1 per cent ofthe
respondents
believe that their HRM has become proactive over the last five
years (i.e.
more involved in decision making).
Such results reflect the growing strategic and proactive nature
of
the British personnel function. There is support for such
findings in the
existing literature [35, 36].
The second reason to examine the matching models in a cross-
national context is to assess whether human resources are
considered as
a cost ('use them sparingly') or as an asset (spend on training to
'make
their best use '). The results suggest that British organizations
claim to be
spending variable though reasonable proportions oftheir annual
salaries
on human resource development (HRD) related activities (see
Table 2).
Table 2: Proportion of Annual Salaries and Wages Currently
Spent on Training and Development
Value(%) Percentage of Sample
Nil -
0.1- 2.00 41.3
2.01-4.00 7.6
4.01- 6.00 3.3
6.01 or more 1.1
Don't know 46.7
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Journal of General Management
Vol. 26 No.2 Winter2000
A similar pattern characterizes the number ofdays training
provided
to different levels ofemployees (see Table 3). The substantial
majority of
British firms have increased (rather than maintained or reduced)
their
training spend across all categories of staff over the last five
years (see
Table 4). There is evidence that this investment has been
directed
particularly in the areas of performance appraisal,
communication,
delegation, motivation and team building.
Table 3: Average Number of Days Training and Development
Given to Staff Categories Per Year
Different Cat~ories of Staff
Number ofDays Mana}!erial(%) Prof,/Technical(%) Clerical(%)
Manual(%)
Nil 1.2 1.1 2.3 1.2
0.1-3.00 24.4 22.8 35.6 24.7
3.01-5.00 20.9 21.7 13.8 11.7
5.01-10.00 7.0 14.7 4.6 11.8
10.1 and above 5.8 4.6 3.5 9.4
Don't know 40.7 40.9 40.2 41.2
These developments in the British HRD scene appear to be
consistent with the increased realisation by both business and
government
that the development of human resources has been neglected for
too long
[37].
Table 4: Nature of Change in Amount of Money Spent on
Training Per Employee
Different Categories of Staff
Nature ofChange Mana}!erial("/o) Prof,/Technical("/o)
Clerical(%) Manual(%)
Increased 59.8 63.0 53.3 60.9
Same 21.7 18.5 28.3 20.7
Decreased 7.6 8.7 7.6 7.6
Don't know 10.9 9.8 10.9 10.9
Another key emphasis of the matching model suggests a
variation
in HRM strategies across different levels of employees. This is
clearly
evident from the results as the nature and type of approach to
the
management of different levels of employees vary significantly
(see for
example, Tables 3 and 4). This aspect is further highlighted
later in this
paper. Based on the above evidence, it seems that the British
personnel
function still plays an implementationist role rather than being
actively
involved in strategy formulation. On the other hand, there is a
strong
emphasis on training and development.
Important Situational Determinants
One of the basic assumptions of the Harvard model of HRM is
the
influence of a number of situational factors (such as work force
Journal of General Management
Vol. 26 No.2 Winter 2000
characteristics, unions, labour legislation and business strategy)
and
different stakeholders (such as unions, government and
community) on
HRM policies. The impact of a few of the situational factors
and
stakeholders (proposed by Beer et al. [38D was examined during
the
multiple regressions, analysis of means scores and the analysis
of
interview results.
Taking the number of employees as a characteristic of the work
force [39, 40], the regression results show that small British
organizations
(those having less than 499 employees) are likely to recruit
their managerial
staff by advertising externally. Medium size organizations
(those having
500 to 999 employees) are likely to recruittheirclerical staffas
apprentices.
Large organizations (those having 1000 to 4999 employees) are
more
likely to use assessment centres to train their human resources.
Lastly,
very large firms (having over 5000 employees) are less likely to
recruit
their managerial staff by advertising internally and their manual
staff
through the use of word of mouth method. These firms are
likely,
however, to recruit their professional staff with the help of
consultants.
Moreover, large UK firms are more likely to adopt formal
career plans,
succession plans and planned job rotation to develop their
human resources
(for details see Table 1 in Appendix).
Support for these findings can be found in the literature (see for
example, [41D. The size ofan organization has a positive
relation with the
formalism of their HRM policies [42]. Therefore, as the size of
the firm
becomes large, logically, the degree offormalism ofits personnel
function
increases and the organization obtains the help ofrecruitment
agencies to
recruit its professional employees.
The results show a strong impact of labour laws, educational
and
vocational training set up (highlighting government policy) and
unions on
British HRM policies (see Table 2 in Appendix). Unions in the
UK are
now playing a more supportive role [43]. The implementation of
labour
legislation is also having significant influence on UK HRM
policies.
Various pressures groups also contribute in this regard (for
example,
against age discrimination). Over the last decade or so, the
education and
vocational set-up in the UK has initiated a number of
programmes and
qualifications such as the national vocational qualifications
(NVQs),
investors in people (IIP) and' opportunity 2000' . These are now
significantly
influencing HRM in British organizations [44].
The results also show a number of significant regressions
regarding
the impact of HR strategies on British HRM. Results in Table 1
in the
Appendix show that organizations pursuing a cost reduction
strategy are
more likely to recruit their clerical and manual staffas
apprentices. These
organizations are likely to adopt an effective resource allocation
HR
strategy. Organizations pursuing a talent improvement HR
strategy are
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Journal of General Management
Vol. 26 No.2 Winter 2000
less likely to recruit their manual staff by word of mouth
method.
However, sample firms pursuing a talent acquisition HR
strategy are
likely to use consultants to recruit their managerial staff and
recruitment
agencies for manual staff. These organizations are also likely to
adopt
assessment centres to train their staff.
Most of the above results seem to be logical. For example, by
recruiting employees as apprentices organizations not only pay
them less
but also train and prepare them for working in the long run in
their
organizations. Hence, it helps to reduce the costs. Similarly, by
recruiting
employees externally, organizations increase the opportunit y to
improve
their talent base.
The second key emphasis of the Harvard model of HRM
suggests
extensive use of communication with employees as a mechanism
to
maximise commitment [45, p. 63]. Ninety-one per cent of
British
organizations share information related to both strategy and
financial
performance with their managerial staff. However, this
percentage is
significantly lower for other categories of employees (see Table
5).
Table 5: Employees Formally Briefed about Strategy or
Financial Performance
Different Categmes of Staff
Tvoe ofInformation Managerial(%) Prof/Technical(%)
Clerical(%) Manual(%)
Strategy - 8.0 8.6 6.4
Financial Performance 6.5 14.8 39.5 38.5
Both 91.3 65.7 42.0 23.6
Neither 2.2 11.6 9.9 31.5
There can be a number of explanations for the difference in the
sharing of strategic and financial information with different
levels of
employees in British organizations. Whilst noting that top
personnel
specialists are now more and more involved in strategy making,
it seems
that top management continue to be reluctant to devolve
responsibility to
line managers for the dissemination offinancial and strategic
information.
These issues are further examined when discussing the 5-P
model.
The above discussion suggests applicability of the Harvard
model
ofHRM in British organizations. The results showed an impact
oflabour
laws, education vocational set-up, unions, work force
characteristics and
HR strategies on HRM policy choices. There are encouraging
results on
the communication of information with different levels of
employees
regarding sharing strategic and financial performance and on
employee
development through their involvement and training.
Contextual Factors
The main issue against which the relevance of the contextual
model can
be evaluated is the impact on HRM policies and practices of
economic
Journal of General Management
Vol. 26 No.2 Winter 2000
(characterized by competitive pressures, ownership and life
cycle stage),
technological (type ofproduction system)and socio-political
(characterised
by national education and training set-up) factors and
organizational
contingencies (such as size, age and nature of organization).
The results show a strong influence of competitive pressures on
British HRM policies and practices (see Table 2 in Appendix).
To achieve
a competitive edge in such situations, they are focusing
particularly on
total customer satisfaction and the restructuring oftheir
organizations. As
competitive pressures are also forcing British organizations to
enter into
new business arrangements (such as alliances), so these are
having direct
influence on HRM policies and practices.
The results also show the impact of increasingly sophisticated
informationand communications technology on HRM policies
and practices
(see Table 2 in the Appendix). Further evidence indicates that
the
majority of respondents suggest these technologies mainly
influence
training, appraisal and transfer functions. Why? Because with
the change
in technology, employees need to be trained to handle it. To see
if they
have achieved the required competence they are appraised and if
required, transferred to suitable positions.
Finally, we summarise the relevance of the contextual model of
HRM in terms ofthe impact of organizational contingencies.
Contingent
variables such as size of the organization, presence of HR
strategy and
presence of unions were examined above, as were the impacts of
ownership and organizational life cycle stage. These variables
do not
seem significantly to impact HRM in British organizatio ns.
Nevertheless, there is significant evidence overall regarding the
applicability of the contextual model ofHRM in British
organizations.
Strategic Integration and Devolvement of HRM in Britain
Our discussion now focuses on the relevance of the '5 P' model
ofHRM
in British organizations. To achieve this, results regarding the
integration
of HRM into corporate strategy and the devolution of
responsibility for
HRM to line managers are examined. The detailed results are
presented
elsewhere [46], but are summarized below.
In brief, the level of integration is measured on the basis of the
following four scales:
a) representation of Personnel on the board;
b) presence of a written Personnel strategy;
c) consultation ofPersonnel (from the outset) in the
development
of corporate strategy; and
d) translation ofPersonnel/HR strategy into a clear set of work
programmes.
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Journal of General Management
Vol. 26 No. 2 Winter 2000
The level of devolvement is measured on the basis of the
following
three scales:
a) primary responsibility with line managers for HRM decision
making (regarding pay and benefits, recruitment and selection,
training and development, industrial relations, health and
safety, and workforce expansion and reduction);
b) change in the responsibility of line managers for HRM
(regarding pay and benefits, recruitment and selection, training
and development, industrial relations, health and safety, and
workforce expansion and reduction); and
c) percentage ofline managers trained in performance appraisal,
communication, delegation, motivation, team building and
foreign language.
High integration is the result of personnel representation at
board
level, the personnel function being consulted about corporate
strategy
from the outset, the presence of a written personnel strategy,
and the
translation of such a strategy into a clear set of work
programmes. As
mentioned earlier, the personnel function is represented at board
level in
the majority (55 per cent of organizations). For our sample
companies,
87.4 per cent have corporate strategies. Of these, 34.6 per cent
consult
the personnel function at the outset, 42 per cent involve
personnel in early
consultation, and only 13.6 per cent involve personnel during
the
implementation stage. Over a quarter (26.4 per cent) of sample
organizations did not have a personnel strategy, 29.9 per cent
had an
unwritten strategy and 43.7 per cent had a written personnel
strategy. A
clear majority (57.4 per cent) of organizations felt that their
personnel
strategy was translated into clear work programmes.
High devolvement is the result of: primary responsibility for
pay,
recruitment, training, industrial relations, health and safety and
expansion/
reduction decisions lying with the line (see Table 6); line
responsibility for
these six areas on an increasing trend (see Table 7); and,
evidence of
devolved competency with at least 33 per cent of the workforce
being
trained in appraisals, communications,delegation, motivation,
team building
and foreign languages.
Budhwar's [47] analysis shows that when the four measures of
integration are summated and divided into a single scale of high
and low
type, 50.5 per cent of the sample organizations would be
categorised as
having high integration and 49.5 per cent fall into the low
integration
category. The average score of the summated integration scale
for a1193
organizations is .50. These results show a moderate level of
integration
being practised in the sample industries. On the other hand, the
summated
scales demonstrate a low level of devolvement. Sixty-one per
cent of the
sample practise low levels of devolvement of HRM to line
managers.
Journal of General Management
Vol. 26 No.2 Winter 2000
Table 6: Primary Responsibility for Major Decisions on
Personnel Issues
Personnel Issues Line Line Mgt in IIR Dilpt. inHRDept.
Consultation COllSuJtationRelated to: Mgt. wi!il1lB.l)llUt.
withLineMat.
Pay andBenefits 48.3 14.3 11.0 26.4
Recruitment and Selection 17.2 12.9 34.4 35.5
Training andDevelopment 15.1 18.3 22.5 44.1
Performance Aonraisal 17.5 6.9 30.4 45.2
Industrial Relations 36.3 13.2 25.3 25.2
Health and Safety 18.5 32.6 19.6 29.3
Workforce
19.4 19.4 44.1 17.1Expansion/Reduction
WorkSystem/Job Design 7.6 33.7 40.2 18.5
Figures in the above cells represent valid percentage, calculated
after excluding the missing
values.
Table 7: Change in Responsibility of Line
Management for Different Personnel Issues
PellSonnelIssues Increased (%) Same(%) Decreased (%)
Pay andBenefits 27.2 65.2 7.6
Recruitment and Selection 43.5 48.9 7.6
Training and Development 69.6 23.9 6.5
Performance Appraisal 60.0 37.8 2.2
Industrial Relations 28.9 63.3 7.8
Healthand Safety 61.5 35.2 3.3
Workforce
38.9 54.4 6.7Expansion/Reduction
WorkSystem/Job Design 43.3 53.3 3.3
The results confirm the relevance of the 5-P model of HRM in
British organizations. They also help to examine the main
emphasis of
Brewster's [48] European model of HRM, i.e, the linkages
between
corporate strategy and HRM strategy.
Conclusion
Overall, the results show a mixed picture, i.e. from strong to
moderate
applicability of the mentioned HRM models in Britain. The
study aimed to
examine HRM in context, and the findings should be useful for
relevant
policy makers. In particular, it seems that the sample firms are
practising
a relatively low level of devolvement in comparison to the
integration
function. Ifthe HRM function is to become more strategic, then
the level
of practice of both these concepts has to increase. Such
demands are
likely to increase in future as more and more firms restructure
and
become lean in order to respond to competitive and other
pressures [49].
The study has two main limitations. First, it is restricted to six
industries ofthe UK manufacturing sector. Second, the views of
only top
..
Journal of General Management
Vol. 26 No. 2 Winter 2000
personnel specialists were examined. In order, therefore, to
obtain a more
comprehensive picture, research needs to be extended to other
business
sectors and to the views of other key actors (such as line
managers).
Future research could also build upon this study by
investigating other
models ofHRM and their applicability in different national
contexts.
Appendix
Table 1: Factors Determining HRM Practices in
British Organizations
Independent. lJependentVariables If BiJta . t·valueVarin/J/es
Training and development
0.2102 0.2984* 2.3790
Introductory through planned iob rotation
lifecycle stage Communication through
0.1629 -0.2663* -2.0720
immediate superior
Turnaround Recruiting managerial staff by
0.3695 -0.3038* -2.6170
lifecycle stage advertising externally
Recruiting managerial staff by
0.3695 0.3658** 3.0590
Less than 499 advertising externally
employees Recruiting clerical staff from 0.1014 -0.3184* -
2.4220
recruitment agencies
Between 500- Recruiting clerical staff as
0.3337 0.2891* 2.4600
599 employees apprentices
Between 1000- Training and development
4999 through assessment centres 0.2607 0.3547** 2.8530
employees
Recruiting managerial staff by
0.1563 -0.2835* -2.1800
advertising internally
Recruiting
professionals/technical staff by
0.1039 0.3223* 2.4550
use of search/selection
More than
consultants
5000
Recruiting manual staffby
0.3698 -0.4529** -3.9340
employees
word of mouth
Training and development
through formal career plans
0.1406 0.375** 2.9170
Training and development
0.1685 0.4105** 3.2460
through succession plans
Training and development
0.2102 0.3873** 3.0880
though planned job rotation
Public Limited Recruiting managerial staff by
0.3695 0.4436** 3.8050Company advertising externally
Recruiting managerial staff
0.0830 -0.2881* -2.1700
from current employees
State-owned Recruiting clerical staff from
0.2842 -0.2583* -2.0650
organization current emnlovees
Recruiting manual staff by
0.3698 -0.3342** -2.9100
word of mouth
Organizations
incorporated Commnnication through trade
0.7445 -0.216** -3.0370
between 1869- unions or work councils
1899
Organizations
incorporated Recruiting manual staff from
0.1557 0.2609* 2.0240
between 1900- current employees
1947
Continued ...
Journal of General Management
Vol. 26 No.2 Winter 2000
Table 1 Continued:
Independent lJepen4ent Variables .Jf Beta tvalueVariable
Recruitingclericalstaffby 0.2465 -0.3931** -3.2110advertising
externally
Recruitingmanualstaffby 0.1974 -0.2767* -2.1550advertising
externally
Organizations Trainingand development 0.2607 0.4364**
3.3780incorporated throughassessment centres
between 1948- Communication through 0.1629 -0.3255* -
2.53201980 immediatesuperior
No formal communication 0.3517 0.3265** 2.7370methods
Communication through 0.0858 0.2929* 2.2090suggestion
box(es)
Recruitingclericalstaff from 0.2842 -0.3019* -2.4240current
employees
Cost reduction Recruitingclericalstaff as 0.3337 0.4182**
2.9450HRstrategy apprentices
Recruiting manualstaff as 0.1330 0.3646** 2.8240apprentices
Talent Recruitingmanualstaff by 0.3698 -0.3655** -
3.2440improvement word of mouthHRstrategy
Recruiting managerial staffby
0.0777 0.2787* 2.0930use of search/selection
Talent consultants
acquisition HR Recruitingmanualstaff from 0.0914 0.3024*
2.2880strategy recruitmentagencies
Trainingand development 0.2607 0.2857*
2.2090throughassessment centres
Effective Recruitingclericalstaff as
0.3337 0.2882* 2.0300resourceHR apprenticesstrategy
Recruitingmanagerial staff by 0.3695 0.3593**
2.9750advertising externally
Recruitingmanualstaff by 0.1226 0.3502** 2.6960Unionised
advertising internally
firms Communication through 0.3517 -0.255* -2.1820attitude
survey
Communication throughtrade 0.7445 0.5656** 6.4000unions or
work councils
* Significance at .05 level; **Significance at .01 level
-
..
Journal of General Management
Vol. 26 No.2 Winter 2000
Table 2: Influence of Different Aspects of
National Factors on HRM
Aspectsoff"lational (;ultttre No. of Cases Mean
1 Way in which managers are socialised 84 18.07
2 Common values, norms of behaviour and customs 81 20.28
3 The influence of pressure groups 58 10.47
4
Assumptions that shape the way managers perceive and
84 25.98
think: about the organization
5
The match to the organization's culture and 'the way we
86 35.58
do things around here'
N(ltif.}1Inl T- o '011.6
1 National Labour Laws 82 40.91
2 Trade Unions 61 21.72
3 Professional Bodies 56 15.11
4 Educational and Vocational training set-up 84 27.62
5 International Institutions 54 20.07
A~l1ects QflIusinessEnvironment
1
Increased national/international competition -
72 27.56
Globalisation of corporate business structure
Growth of new business arrangements, e.g. business
2 alliances, joint ventures and foreign direct investment 66
19.01
through mergers and acquisitions
3
More sophisticated information/communication
70 19.62
technology or increased reliance on automation
4
Changing composition of the workforce with respect to
48 12.39
gender, age, ethnicity and changing employee values
5
Downsizing of the workforce and business re-
69 23.13
engineering
6
Heightened focus on total management or customer
78 26.92
satisfaction
Aspects qfBusinessSector
1
Common strategies, business logic and goals being
71 22.95
pursued by firms across the sector
2
Regulations and standards (e.g. payments, training,
79 20.35
health and safety) specific to your industrial sector
Specific requirement/needs of customers or suppliers
3 that characterise your sector (i.e. supply chain 82 28.96
management)
4 The need for sector-specific knowledge in order to 56 15.35
provide similar goods/services in the sector
5
Informal or formal benchmarking across competitors in
61 16.39the sector (e.g, best practices of market leaders)
Cross-sector co-operative arrangements, e.g, common
6 technological innovations followed by all firms in the 37
10.54
sector
7
Common developments in business operations and work
49 14.40
practices dictated by the nature of the business
8
A labour market or skill requirement that tends to be
39 13.10used by your business sector only
Respondentswere asked to allocate a totalof100points to the
different aspects ofthe above
nationalfactors.
Journal of General Management
Vol. 26 No.2 Winter 2000
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[19] Legge, K., 1995. op. cit.
[20] Poole, M., 1990. op. cit.
..
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[24]
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Schuler, R. S., 'Linking the People with the Strategic Needs of
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Brewster, C., 1995, op. cit.
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Budhwar, P., 'Taking Human Resource Management Research
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The Next Millennium: Need For An Integrated Framework',
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Budhwar, P. and Debrah, Y., 'Rethinking Comparative and
Cross
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Determining Cross National Human Resource Management
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Determining
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P.,
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K. (ed.),
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and
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[40] Tayeb, M., Organizations and National Culture, London:
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[41] Sisson, K. and Storey, J., 2000, op. cit.
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-
110
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California Management Review
2019, Vol. 61(4) 110 –134
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Special Issue on AI
Demystifying AI:
What Digital transformation
leaDers Can teaCh You about
realistiC artifiCial intelligenCe
Jürgen Kai-Uwe Brock1 and Florian von Wangenheim2
SUMMARY
Recent years have seen a reemergence of interest in artificial
intelligence (AI) among
both managers and academics. Driven by technological
advances and public interest,
AI is considered by some as an unprecedented revolutionary
technology with the
potential to transform humanity. But, at this stage, managers are
left with little
empirical advice on how to prepare and use AI in their firm’s
operations. Based on
case studies and the results of two global surveys among senior
managers across
industries, this article shows that AI is typically implemented
and used with other
advanced digital technologies in firms’ digital transformation
projects. The digital
transformation projects in which AI is deployed are mostly in
support of firms’
existing businesses, thereby demystifying some of the
transformative claims made
about AI. This article then presents a framework for
successfully implementing AI in
the context of digital transformation, offering specific guidance
in the areas of data,
intelligence, being grounded, integrated, teaming, agility, and
leadership.
KeYwoRDS: artificial intelligence, polls and surveys,
managers, management,
management skills
I
n 2014, Dr. Julio Mayol wondered, “We have access to a vast
quantity of
data but it’s hard to extract meaningful information that helps
us improve
the quality of the care we provide.” Dr. Mayol, Medical
Director and Director
of Innovation at the Carlos Clinical Hospital in Madrid, Spain,
founded in
1787, found the answer after consulting with an external group
of technology advi-
sors: artificial intelligence (AI). About six months later, the
innovation unit of the
hospital, under his leadership, embarked on a project to apply
AI. Rather than opt-
ing for an off-the-shelf generic solution, the team worked
closely with a technology
1Fujitsu, Tokyo, Japan
2ETH Zurich, Zurich, Switzerland
865226CMRXXX10.1177/1536504219865226California
Management ReviewDemystifying AI: What Digital
Transformation Leaders Can Teach You
research-article2019
https://us.sagepub.com/en-us/journals-permissions
https://journals.sagepub.com/home/cmr
Demystifying AI: What Digital Transformation Leaders Can
Teach You 111
provider of AI solutions and co-created an innovative new AI
application tailored
to their specific needs. After one year, the system was ready for
field testing. Six
months later, first results showed that the diagnostic and
patient’s risks assessment
solution can cut the time in half for preliminary assessment of
patient records,
with a 95% accuracy compared with eight domain experts who
were psychiatrists
with more than 20 years of experience. An unanticipated
positive side effect of this
immense increase in efficiency was that the medical staff had
much more time for
consultations and patient care, thereby increasing customer
satisfaction.1
This exemplary case of a successful AI2 application stands in
stark contrast to
mounting evidence of AI failures,3 gaps between firms’ AI
ambition and execu-
tion,4 and a general “post-AI-hype sobering.”5 Given the mixed
evidence and the
paucity of empirical insights related to the successes and
failures of AI implementa-
tion projects, we embarked on a global research project with the
aim of understand-
ing managers’ perceptions and evaluations of AI. This research
was informed by
insights derived from the opening case as well as publicly
available case study mate-
rial.6 All these cases were excluded from the survey
investigation. Between 2016
and 2018, more than 3,000 executives and managers were
surveyed globally from
across industries with a total of nearly 7,000 projects, including
the application of AI
and other advanced digital technologies. We do not necessarily
assume that manag-
ers know best, but they are important information sources about
current and future
AI potential for various reasons. First, given their involvement
and business inter-
est, they are the decision makers about future projects. Second,
their perceptions
and experiences will influence future implementation success.
Based on the above assessment (with high expectations and a
few success
stories on the one side, but frustrating experiences and stopped
projects on the
other side), we first establish the current prevalence of AI in
business (study 1) and
then explore key dimensions of successful AI implementations
(study 2). Specifically,
study 1 focused on the following research question (RQ1; see
Figure 1):
Research Question 1 (RQ1): To what extent has the application
of AI
diffused in business?
In study 2, we explored AI in business in more detail.
Specifically, we were
interested in the following three research questions (RQ2-RQ4;
see Figure 1):
Research Question 2 (RQ2): What are the anticipated perceived
business
impacts of AI? More specifically, to what extent are managers
expecting
impacts in the area of operations, offerings, and customer
interactions?7
Research Question 3 (RQ3): Assuming differences concerning
the per-
ceived business impact of AI, what explains those differences,
for example,
on a country, industry, firm level, executive (respondent), and
skill level?
Research Question 4 (RQ4): Given the opening case of AI
success,
can we identify leaders, firms that are experienced and
successful in
CALIFORNIA MANAGEMENT REVIEW 61(4) 112
implementing AI-related projects?8 What makes them different
from lag-
gards, firms that have not yet moved beyond the planning phase,
in terms
of country-, industry-, firm-level factors, executive (respondent)
factors,
skills, and organizational traits? Furthermore, could these
leaders real-
ize more positive business impacts in areas such as operational
efficiency,
organizational agility, revenue growth, competitiveness, and
customer
experience? And how long does it usually take from the start of
an AI
project to achieving business impacts? In addition, we
investigate whether
leaders differ in their perception of AI implementation
challenges and key
success factors.
Research Structure and Guiding Frameworks
Figure 1 illustrates our research structure and the guiding
frameworks
we used. Phase 1 of our research, the exploration phase, was
informed by ini-
tial case study research (e.g., the opening case) and a first
digital transformation
Figure 1. Structure and guiding frameworks of the research.
Sources: Framework references (in alphabetical order): John
Hagel III and Marc Singer, “Unbundling the Corpo-
ration,” Harvard Business Review, 77/2 (March/April 1999):
133-141; E. T. Penrose, The Theory of the Growth of
the Firm (London: Wiley, 1959); Lynn W. Phillips, “Assessing
Measurement Error in Key Informant Reports: A
Methodological Note on Organizational Analysis in Marketing,”
Journal of Marketing Research, 18/4 (November
1981): 395-415; Everett M. Rogers, Diffusion of Innovations,
4th ed. (New York, NY: The Free Press, 1995); B.
Wernerfeld, “A Resource-based View of the Firm,” Strategic
Management Journal, 5/2 (April-June 1984): 171-
180; Robert K. Yin, Case Study Research: Design and Methods,
4th ed. (Los Angeles, CA: Sage, 2009).
Demystifying AI: What Digital Transformation Leaders Can
Teach You 113
and AI survey that aimed at understanding the extent of AI
diffusion across firms
worldwide. Given that AI is a recently adopted technology for
most firms, this
stage of our research was informed by diffusion of innovation
theory in general
and organizational innovation adoption implementation research
in particular.
From this body of past research, plus managerial interviews, we
developed our
stages of AI implementation model (see Figure 2). Phase 2 of
our research, the
descriptive phase, aimed at a broad yet deeper understanding of
AI in firms’ digi-
tal transformation worldwide. As the differential analysis
emerged in phase 2 of
our research, this phase was guided by three related
organizational frameworks:
the theory of the growth of the firm, the resource-based view of
the firm, and the
pragmatic firm conceptualization proposed by Hagel and
Singer.9 All three have
Figure 2. Status of AI implementation in firms (study 1).
Source: We derived these five stages from interviews with
managers and the organizational stages-of-
innovation-adoption-implementation literature (e.g., G. W.
Downs Jr., and L. B. Mohr, “Conceptual Issues in
the Study of Innovations,” Administrative Science Quarterly,
21/4 (December 1976): 700-714; M. A. Scheirer,
“Approaches to the Study of Implementation,” IEEE
Transactions on Engineering Management, 30/2 (1983): 76-
82; L. G. Tornatzky and B. H. Klein, “Innovation
Characteristics and Innovation Adoption-implementation: A
Meta-analysis of Findings,” IEEE Transactions on Engineering
Management, 29/1 (1982): 28-45; Everett M. Rogers,
Diffusion of Innovations, 4th ed. (New York, NY: The Free
Press, 1995).
Note: n = 1,614 firms, worldwide, 2016-2017: “Which best
describes the progress of your firm’s AI (Artificial
Intelligence) implementation?” AI = artificial intelligence; DX
= digital transformation.
CALIFORNIA MANAGEMENT REVIEW 61(4) 114
an internal resources focus in common and have guided our
assessments in terms
of firms’ experience, capabilities, challenges, and success
factors. As a multistage,
mixed-methods research design, research phase 1 informed
research phase 2.
Data Collection
Data for this study were obtained in two waves (see Figure 1).
Following a
set of digital transformation AI case studies, the first
exploratory survey was con-
ducted in 2016-2017, addressing the first research question. The
survey was con-
ducted globally, online, utilizing a database of executives and
senior managers
provided by an international market research firm. The second
survey was con-
ducted in 2017-2018, addressing RQs 2 to 4. The second survey
used a similar
methodology. It was conducted globally, online, and the same
market research
firm provided samples of executive respondents. Sampling was
guided by the
following:
• Global coverage: firms from key countries (in terms of
economy/GDP) in the
Triad.10
• A minimum country quota of n = 50 where possible (with the
exception of
New Zealand with n = 29 in study 2, this was achieved).
• North American Industry Classification System (NAICS)
industry sampling:
focus on manufacturing (NAICS code 23, 31, 32, 33),
information (NAICS
code 51), transportation (NAICS code 48, 49), retail (NAICS
code 41, 42, 44,
45), financial services (NAICS code 52), healthcare (NAICS
code 62).
• Minimum industry quota: n = 50.
• Focus on medium to large firms in terms of revenue and
employees.
• Senior respondents: focus on key informants of firms at C- or
VP-level or
above.
In order to assess response bias, we followed the logic of
Armstrong and
Overton11 and found no statistically significant differences at
the .05 level in
regard to any of the variables that we are reporting below. In
terms of common
method bias, we applied the marker variable approach and found
no significant
effects.12 Overall, we are confident that our findings can be
generalized to medium
to large Triad enterprises in the industries sampled and that our
results are not the
result of the instrument/sample used in our analysis. Sample
details are provided
in the endnote.13
AI in Business
To what extent are firms already using AI in their business? In
2016-2017,
the time of the first survey, AI applications had already diffused
quite broadly,
with only 15% of firms not yet having any AI plans and 20%
already having
delivered results (see Figure 2).
Demystifying AI: What Digital Transformation Leaders Can
Teach You 115
Interestingly, the application of AI was typically an integral
part of a firm’s
digital transformation project. With the exception of isolated
experimentation
with specific AI techniques such as deep learning, AI was not
used in isolation, but
as one technological element of several technologies aimed at
enhancing a firm’s
present and future business.14 It emerged that digital
transformation is often the
context for AI projects, such as call center transformation using
advanced analyt-
ics and AI or transforming operations using Internet of things
(IoT), advanced
analytics, and AI.
The Business Impact of AI
In the second survey, which was executed in 2017-2018, we
built on the
insights from the first survey and explored the role of AI in
firms more deeply.
What are the anticipated perceived business impacts of AI? AI
can impact the
internal operations of a firm, its offerings (in the form of smart
products and
services15), and how it interacts with its customers. The survey
data largely con-
firm this (Figure 3). The surveyed executives foresee AI to
impact their firms’
offerings. More specifically, they foresee AI to impact the
creation of smart ser-
vices, to automate operations and manufacturing, to support
decision-making
and knowledge management, and to automate customer
interfaces. Interestingly,
the strength of the anticipated impacts does not vary too much
(average range:
3.7-3.8 on a 5-point scale). This we interpret as the typical
fairly undifferentiated
perception by businesses of a new technology prior to wider and
deeper diffusion
and the emergence of standard business cases and applications.
Despite the largely undifferentiated perception of AI’s business
impact—
the perception of AI business impacts was also largely similar
across countries and
industries—some differences emerged. The impact on smart
services is more
Figure 3. Anticipated DX/AI impacts on business (study
2).Note: n = 1,218 firms,
worldwide, 2017-2018: “In terms of business impact of AI, to
what extent will AI make an
impact in each of following areas?”
Note: AI = artificial intelligence.
*Knowledge management refers to decision-making and
knowledge management support.
CALIFORNIA MANAGEMENT REVIEW 61(4) 116
pronounced in financial services and less in healthcare, and the
impact on manu-
facturing is more pronounced in the manufacturing sector.
However, these signifi-
cant differences only exhibited effects that are rather small.16
While, on average, AI’s anticipated business impact is seen as
moderate to high
across all the business impact categories investigated, 3% of the
ratings anticipate no
impact and 21% anticipate a high impact.17 What explains those
differences?
Of the 10+ factors at the country, industry, firm, and executive
(respon-
dent) level we examined,18 only digital skills have a strong
impact. Firms with
stronger digital skills anticipate stronger AI-induced business
impacts compared
with firms with weaker digital skills. This observation is stable
across industries
and regions (see Figure 4).
These digital skills comprised four, interrelated organizational
capabilities:
• Strategic capabilities: digital strategy and digital business
development skills.
• Technology capabilities: skills in new digital technologies
such as AI or IoT.
• Data capabilities: data science skills.
• Security capabilities: cybersecurity skills.
These results suggest that, just like with other technological
innovations in
the past, to realize the potential of the new digital technology,
AI requires specific
organizational capabilities as the firm and the new technology
align for best appli-
cation and impact.19 AI requires new information technology
(IT) skills that are
both AI-specific, such as machine learning skills, and generic,
such as understand-
ing of modern programming languages (e.g., Python),
application development
techniques (e.g., agile software development), and modern IT
architecture skills
(e.g., edge computing).20 In addition, data management and
analytical skills are
required. AI thrives on massive amounts of data requiring the
existence of digital
data, its management, and its analysis and synthesis. Given that
most data are
network generated (e.g., websites, sensor data from IoT
devices), security skills—
generic as well as AI-empowered—become vital to ensure
access rights, intrusion
detection, and data integrity.21 Last, these skills have to be
embedded in a coher-
ent and suitable strategic framework to ensure a guided
implementation and
wider organizational alignment and support.22
We find that firms that have already implemented and delivered
business
outcomes through three or more digital transformation projects
(stage 4; see
Figure 2) exhibit particularly strong digital capabilities.23
These firms we label
digital transformation leaders (DX leaders for short); this group
consists of about
8% of all firms surveyed.
Digital Transformation Leader Analysis
What makes these digital transformation (DX) leaders different?
In order
to find out, we compared them with those firms in our sample
that either had
Demystifying AI: What Digital Transformation Leaders Can
Teach You 117
no plans yet or were still in the planning phase (implementation
stage 0 and 1;
see Figure 2). We term these firms “laggards.” Following this
classification, we
compared 114 DX leaders with 424 laggards. We examined
organizational traits
Figure 4. Anticipated business impact of AI and firms’ digital
skills (a) across industry
(study 2)a and (b) across regions (study 2)a.
Note: AI = artificial intelligence.
aAnticipated business impact and digital skills based on
separate summated scales, combining the individual
impact and skills items.
CALIFORNIA MANAGEMENT REVIEW 61(4) 118
in the area of strategy, leadership, data management, agility,
organizational pro-
cesses, and innovation, as well as country-, industry-, and firm-
level factors.
The DX leaders we identify came from across all countries
surveyed and are
as prevalent among traditional firms as among digital natives.24
They also did not
differ significantly with regard to the reported duration from
project start to busi-
ness impact.25 The weak industry differences we observe are a
reflection of the
different extent of digital transformation initiatives in the
industries surveyed,
with financial services leading and healthcare lagging.26 With
regard to traditional
organizational measures (such as size in revenue or the number
of employees),
DX leaders tend to be larger. They report higher revenues and
more employees,
but these differences are not very pronounced.27 Besides their
noted stronger digi-
tal capabilities, we identify organizatio nal characteristics where
these leaders
excelled.
Organizational Characteristics
When compared with laggards, DX leaders differ significantly
and
strongly28 in seven organizational traits. In order of size of the
difference (effect
size) between the two groups, these were integrated data
management, CEO pri-
ority, security strategy, digital processes, digital strategy,
agility, and open innova-
tion ecosystem.
Integrated data management, the most pronounced difference
between lead-
ers and laggards, refers to the organizational capability of
managing customer and
organizational data in a holistic and integrated fashion, avoiding
data silos and
incompatible data formats. This aspect goes hand in hand with
AI’s dependence
on data. CEO priority refers to a firm’s leader prioritizing and
leading the firm’s
digital transformation efforts, which include the application of
advanced digital
technologies such as AI. An organization-wide security strategy
refers to the defini-
tion and execution of a cybersecurity strategy across the whole
organization.
Given the importance of data, a strategic approach to data
security—including the
management of access rights, intrusion detection, and disaster
recovery mecha-
nisms—is critical. Digital processes refer to the digitalization
of a firm’s core pro-
cesses such as sourcing, production, performance reviews, or
travel management
and expense claims. Digital processes are often the outcome of
digital transforma-
tion projects. Organization-wide digital strategy refers to the
development and exe-
cution of a strategic approach to digital transformation, an
approach that is
contrasted to unplanned or tactical approaches. Organizational
agility refers to a
firm’s ability to rapidly and flexibly respond to customers’
needs, adapt produc-
tion/service delivery to demand fluctuations, and implement
decisions in the face
of market changes. Agile organizations continuously search for
ways to reinvent
or redesign their organization and they can do so in a fast and
flexible manner as
they learn and adapt in the process. Innovation ecosystem refers
to the establishment
of an open ecosystem for innovation, beyond the boundaries of
the firm. Such
ecosystems tie into the resources, capabilities, and strength of a
firm’s network of
business relationships, such as ties with suppliers, alliance
partners, and custom-
ers (Figure 5).
Demystifying AI: What Digital Transformation Leaders Can
Teach You 119
Business Impact
The seven organizational aspects enable the leaders to achieve
much
greater business impacts compared with laggards. Leaders
report significantly
stronger actual business impacts in their projects compared with
laggards. We
find significantly higher levels of impact on transformations of
existing business
models, improvements in operational efficiency, increase in
revenue, strengthen-
ing of offerings’ competitiveness, and customer experience
enhancements. All of
the observed differences are strong (see Figure 6). This moves
beyond the mere
anticipation of business impact (Figure 3) to managers’
perception of real busi-
ness impacts actually achieved.
Challenges
Reflecting the importance of digital skills, the main challenge
for all firms
is lack of skilled staff and knowledge in digital technologies,
which was men-
tioned as an implementation challenge by more than half of the
firms combined.
Lack of organizational agility, internal resistance to change,
security risks, lack
of leadership and sufficient funding, as well as the challenge of
integrating new
digital technology with existing technology were stated as
challenges by about
a quarter of the firms each. Unavailability of suitable
technology partners and
Figure 5. Organizational characteristics of DX leaders (study 2).
Note: n = 538 firms (114 leaders, 424 laggards), worldwide,
2017-2018: “To what extent do you agree with
the following statements?” (from 1 = strongly agree to 5 =
strongly disagree; statements randomly rotated):
(1) Digital transformation is the top priority of our CEO; (2) We
are executing an organization-wide digital
transformation strategy; (3) We have achieved organizational
agility; (4) We have established an open ecosys-
tem for innovation; (5) We have digital business processes; (6)
We are managing customer and organizational
data in an integrated manner; (7) We are executing an
organization-wide cybersecurity strategy. DX = digital
transformation.
CALIFORNIA MANAGEMENT REVIEW 61(4) 120
unstable technology are mentioned as challenges by 19% and
13% of firms,
respectively (Figure 7).
Contrary to our expectations, we uncover few differences in
terms of chal-
lenges perceived by leaders versus laggards. Only
organizational agility, security
risks, and lack of leadership were challenges that the leaders did
perceive as less of
a challenge in their AI projects. However, the effect of these
differences was fairly
small.29 We interpret this rather surprising finding as follows.
Although the barri-
ers or challenges that firms perceive are similar, the DX leaders
have more experi-
ence and a stronger resource base to overcome them. This
becomes most obvious
when looking at the three challenges the DX leaders perceived
as significantly less
of a challenge and comparing those with challenges that are
perceived similar. For
example, lack of leadership: DX leaders stated lack of
leadership significantly less
often as an implementation barrier compared with laggards.
Given that DX lead-
ers had significantly more CEOs prioritizing digital
transformation, perceptions of
lack of leadership support should be lower. On the contrary,
both DX leaders and
laggards perceive lack of skilled staff as a key barrier. This is
despite the finding
that DX leaders have a stronger digital skills resource base.
Taken together, this
points to the view that perceived challenges are similar, but that
in some cases
(e.g., lack of leadership), the DX leaders have already
developed to a degree that
the challenges are less of an actual implementation success
barrier compared with
the laggards.
Figure 6. DX/AI business impact (study 2).
Note: n = 538 firms (114 leaders, 424 laggards), worldwide,
2017-2018: “To what extent have you delivered
outcomes specified in each of following statements?” (from 1 =
not at all to 5 = to a great extent; statements
randomly rotated): (1) Increase in revenue; (2) Improvement in
customer experience; (3) Strengthening of
competitiveness of products or services; (4) Efficiency
improvements; (5) Improvement of business agility; (6)
Transformation of business models. AI = artificial intelligence.
Demystifying AI: What Digital Transformation Leaders Can
Teach You 121
Success Factors
In contrast to the implementation challenges, which were
similar across
the firms surveyed, leaders differed compared with laggards in
terms of the
importance they attributed to factors contributing to digital
transformation out-
come success. The following eight success factors turn out to be
significantly dif-
ferent among the AI leaders: organizational agility, engagement
of skilled staff,
leadership, support from technology partners, investment,
culture, alignment of
new digital technologies with existing IT, and learning from
failed projects.
The biggest difference between the leaders and the laggards is
organiza-
tional agility. Leaders attribute much more importance to
organizational agility as
a factor of project success. Second, leaders have more engaged
staff with the
required digital skills and leadership support. Support from
technology partners,
sufficient funding, a supportive culture, alignment of new
digital technologies
with a firm’s existing technology, and learning from failure
were also rated much
higher as contributing factors of project success by the leaders
as compared with
the laggards (Figure 8).
Figure 7. DX/AI implementation challenges (study 2).
Note: n = 3,557 answers by 1,218 firms, worldwide, 2017-2018:
“Which of the following statements describes
your key challenges? Please select up to three” (categories
randomly rotated): (1) Lack of skilled staff**; (2)
Lack of knowledge of digital technology**; (3) Lack of
organizational agility; (4) Lack of leadership; (5) Fear of
change or internal resistance; (6) Unavailability of a right
technology partner; (7) Lack of funds; (8) Integrat-
ing digital technologies with existing IT; (9) Cybersecurity
risks; (10) Adoption of digital technology too early,
before it is robust and stable; (11) Others. AI = artificial
intelligence; IT = information technology.
**Combined in the figure.
CALIFORNIA MANAGEMENT REVIEW 61(4) 122
DIGITAL: Guidelines for Successful AI Applications
The present research departed from the tension between AI
success sto-
ries on one hand and failures and frustrations on the other hand.
Based on our
research, we now identify seven areas for managerial action and
implementa-
tion. We use the acronym “D-I-G-I-T-A-L” to describe our
implications, where
“D” stands for data, “I” for intelligence, “G” for grounded, “I”
for integral, “T”
for teaming, “A” for agile, and “L” for leadership. We explore
these areas below.
The more DIGITAL a company is, the higher the likelihood that
their digital
transformation–embedded AI projects will succeed. For easier
comprehension,
Figure 9 illustrates the links between the empirical evidence
presented and the
elements of the DIGITAL implementation framework. Some of
the conclusions
that we draw from our analyses, obviously, refer to other
change projects within
the digital transformation of companies as well. With DIGITAL,
we also give
credit to this notion and remind that implementation of AI, in
its present con-
dition, is typically linked to digital transformation of
corporations in general.
For each of the elements of DIGITAL, we provide managers
with a few action-
inducing discovery questions in order to guide their AI
applications. Answering
any of those questions with a clear “No” should alert managers
to act accord-
ingly (see Figure 9).
Figure 8. DX/AI success factors (study 2).
Note: n = 538 firms (114 leaders, 424 laggards), worldwide,
2017-2018: “To what extent have the following
factors contributed to the overall outcomes you reported?”
(from 1 = not at all to 5 = to a great extent; state-
ments randomly rotated): (1) Engagement of skilled staff; (2)
Having the right organization and processes; (3)
Leadership by management; (4) Development of an enabling
culture; (5) Support from technology partners; (6)
Investment by the business; (7) Alignment of new digital
technologies with existing IT; (8) Learning from failed
projects. AI = artificial intelligence; IT = information
technology.
Demystifying AI: What Digital Transformation Leaders Can
Teach You 123
Data
Just like Dr. Mayol alluded to in his opening quote, the
fundamental basis
for AI success is data. AI requires data, digital data, and in high
quality. The AI
machine learning technique of deep learning is particularly data
hungry. For
Figure 9. Proposed AI implementation success framework,
empirical evidence, and
action-inducing discovery questions.
Note: AI = artificial intelligence.
CALIFORNIA MANAGEMENT REVIEW 61(4) 124
example, Google needed some 10 million images to teach its
Google brain sys-
tem to identify human faces, human bodies, and cats.30 In a
more recent appli-
cation, the number of images used increased to 300 million
images.31 Without
data available for training, AI cannot create value for firms, and
without skills to
acquire, manage, and analyze the data, valuable and actionable
insights cannot
be generated. Our research confirms this. Firms with strong data
capabilities are
expecting to derive more value from AI, and our DX leader
analysis showed that
integrated data management practices and digital processes,
which generate digi-
tal data, separate the leaders from the laggards significantly and
strongly.
Managers are, therefore, advised to start with a data inventory
check before
embarking on AI projects. The data inventory check should
address questions
such as “Do we own or have access to data that are relevant to
analytically solve
the business problem we are addressing?” “Are the data
available in the right digi-
tal format?” “Are the data sets sufficiently large to be efficient
and effective?” “Are
the data sufficiently complete, consistent, accurate, and
timely?”
Be Intelligent
Data are the necessary foundation for AI success, but data alone
are not
sufficient. We identified lack of skilled staff and knowledge in
digital technolo-
gies as the top AI implementation challenge and engaged skilled
staff as one of
the key AI implementation success factors. Therefore, managers
need to develop
digital intelligence in the form of suitable human skills within
their organization.
This intelligence extends beyond the necessary data-related data
science skills
to include the strategic-, technological-, and security-related
capabilities that
we discussed earlier. In fact, AI requires organizations to
develop human intel-
ligence. How shall managers develop this intelligence,
especially considering that
AI-specific technical talent is scarce?32
First, it is important to realize that AI success is not just a
function of tech-
nical skills such as data science capabilities and skills in new
digital technologies
and cybersecurity. Managerial skills in the form of strategic
capabilities are vital.
Our results showed that firms with stronger capabilities in the
area of digital strat-
egies and digital business development skills are expecting to
derive more value
from AI compared with firms with a weaker skills base. At the
heart of these
managerial skills is awareness and understanding. This implies
awareness of the
possibilities and requirements of AI and related new digital
technologies and an
understanding of how to best leverage this technology in the
idiosyncratic context
of the firm. Questions such as “How can AI help defend, grow,
or transform our
business?” or “How can AI improve operational efficiencies?”
are indicative.
Answering such questions does not require an in-depth how-to
technical under-
standing of AI, but does require managerial curiosity and
interest paired with
firm, customer, and industry knowledge.
Second, the required technical AI skills need to be attained. In
principle,
managers have two options. Develop technical AI skills
internally or acquire these
skills from outside the firm. Interestingly, we found no
difference in how leaders
Demystifying AI: What Digital Transformation Leaders Can
Teach You 125
approached skills scarcity compared with laggards.33 We
recommend a dual-
sourcing strategy. Managers should develop existing internal
skills and source
external talent at the same time in order to build the necessary
technical skill base
to ensure efficient and effective application of AI technologies.
Finally, digital intelligence has to do with patience. AI is not an
instant
panacea. Our research unearthed rather lengthy, multiyear
processes from
project start to impactful execution. Just like the successful case
that opened
this article, AI projects are usually not deterministic from start
to finish, but
emerge as the project participants learn, and the system
provides feedback. This
also distinguishes AI projects from many other IT projects,
where the end goal
is the successful implementation and use of a system. Especially
when embark-
ing on an AI project for the first time, managers should allow
the AI project
team to experiment and provide them with a generous timeline
to deliver
results and sufficient funding, one of the main barriers we
identified. This
includes allowing for a “failure culture” as the team learns. DX
leaders excelled
at learning from failure and it helped them to reap more benefits
from their AI
projects.
In summary, managers are advised to start with an internal
resources check
before embarking on AI projects. This check should address
questions such as “Do
we have a digital strategy in place?” “Do we have the
managerial and technical
skills required to support successful digital transformation with
AI? If not, how do
we develop or acquire these skills?” “Are we willing and able to
tolerate investing
in an emerging rather than deterministic AI digital
transformation journey, includ-
ing accepting failure?”
Be Grounded
Following the insights derived from more than 7,000 projects
world-
wide, we conclude that firms are mainly applying the new
digital technology
to improve their existing business(es) (see Figure 3). The
reported business
impacts of the DX leaders also suggest a grounded approach
with impacts such
as improving the existing offering, increasing revenue, or
enhancing operational
efficiency (see Figure 6). Managers embarking on AI projects
should take this
insight as suggestive of a rather grounded approach to AI, at
least initially. Rather
than pursuing high-flying “pie-in-the-sky” projects, firms
should “start small”
with AI and base the project in their existing core business(es).
Our opening case
illustrated this: a focused application area and a relatively small
project size. The
setup allowed for early results, and the project setup was not
made overly com-
plicated. Again, this also points to the need for using AI for
solving concrete busi-
ness problems, rather than viewing it as radical innovation and
business model
disruption from the start—this may happen eventually, but later.
AI, just as other
technologies, is ultimately not about technology but business
opportunities and
capabilities. Only when enough experience has been
accumulated should firms
proceed to more difficult and complex projects involving
innovation and new
business models. This grounded approach also signifies that
adopting AI is like
adopting other new technology successfully. Start small, test,
learn, and then
CALIFORNIA MANAGEMENT REVIEW 61(4) 126
apply more widely. As we noted earlier in this article, this
suggests that realizing
the potential of AI requires the firm and the new technology to
co-align for best
application and impact, and it rejects the notion of
technological or organiza-
tional imperatives.34
Before embarking on AI projects, managers should conduct a
reality check
in terms of scope and intent of the project(s). This check should
address questions
such as “Are we experienced enough and resourced properly for
the scope of the
project?” “Are we following an incremental, current business
focused approach
with our DX/AI project(s)?” “Do we have a DX/AI projects
roadmap?”
Be Integral
Successful firm-wide AI implementations require an integral,
holistic
approach. Being integral comes in six flavors: strategy,
processes, data manage-
ment, technology alignment, employee engagement, and culture.
As soon as AI leaves the experimental, feasibility-testing lab
environment
and is applied to a real business case, managers should first
make sure it is embed-
ded in and supportive of the firms’ digital strategy. The
existence of a digital strat-
egy separated the DX leaders from the laggards and signals the
importance of
viewing AI in a broader context. A firm’s digital strategy,
which, in essence, out-
lines and documents how a firm wants to achieve its strategic
objectives with the
help of digital technologies—including but not exclusive to
AI—channels its activ-
ities and provides for a guiding purpose.
Executing a digital strategy implies the “digitalization” of a
firm’s core
processes: from procurement processes to internal operations to
customer
engagement.35 AI cannot augment analog processes. Managers
should ask them-
selves how digital their firms’ core processes are. Our DX
leaders’ analysis showed
that digital processes significantly and strongly distinguished
them from laggards.
AI requires data. As firms digitize their operations, thereby
creating more
digital data, the need for an integrated data management
approach becomes
vital. It is, therefore, not surprising that integrated data
management was the
number one organizational characteristic differentiating DX
leaders from lag-
gards, because the mere existence of a lot of data is good but
not good enough.
Data, even if sufficiently large, complete, consistent, accurate,
and timely, are
limited if they “live” in isolation and are not connected with
other relevant data.
Subscribing to the view that firms are essentially consumers,
producers, manag-
ers, and distributors of information,36 all their data should be
connected and
integrated to allow for maximum value capture and knowledge
generation. To
address this challenging task, some innovative firms have
recently started to set
up so-called data lakes, a centralized repository that allows
them to store all their
structured and unstructured data and access in a unified way. As
firms employ AI
for more complex, broader tasks and processes (say, enhancing
customer experi-
ences), integrated data management becomes more important.
Enhancing cus-
tomer experiences, for example, requires tapping into data from
the firm’s ERP
(Enterprise Resource Planning), CRM (Customer Relationship
Management),
Demystifying AI: What Digital Transformation Leaders Can
Teach You 127
CMS (Content Management System), SMM (Social Media
Monitoring), and
other systems in order to ensure an integral approach to the
customer journey.
Integrated data management requires technology alignment.
Lack of it was
one of the key barriers to AI success and successful alignment
one of the key suc-
cess factors we identified. Technology alignment specifically
refers to the integra-
tion of new digital technologies, including AI, with a firm’s
existing technologies,
and it is all about the question of whether the new and the old
can “speak”
together and “understand” each other in terms of data. For
example, can the
firm’s legacy system provide the data required for an AI
application in a format
that it can compute? Managers should ensure that technology
alignment is looked
after and instruct experts to ensure seamless integration of the
old with the new.
Last, integral implies managers should ensure employee
engagement and a
supportive culture. Successful AI thrives with engaged skilled
staff and an enabling
culture, both of which help to overcome internal resistance and
lack of skills and
knowledge, two of the key barriers we identified. Engagement is
particularly
important for the employees that will be impacted by AI. As
firms seem to be par-
ticularly interested in applying AI in the creation of smart
services (see Figure 3),
for example, engaging and working with the service frontline
employees will be
instrumental to the success in smart services. Even though our
research supports
the augmentation view of AI, managers should be aware that
frontline employees
might fear displacement and should address this fear
proactively. Research on
how frontline employees’ roles, responsibilities, and actions are
likely to change
due to AI-automated customer interfaces is in its infancy, but it
is safe to assume
that managers who address such inherent concerns proactively
are more likely to
achieve employee engagement.
In conclusion, managers are advised to think about AI as a
broad means to
support its company-wide digital transformation efforts. To
ensure an integral
approach, managers should address questions such as “Have our
firm’s core busi-
ness processes been digitalized?” “Has our firm analyzed what
existing/new offer-
ings can benefit from DX/AI?” “Has our firm integrated all data
into one single
data repository?” “Is our firm’s existing IT compatible with the
DX/AI technology
we plan to adopt?”
Be Teaming
Our opening case illustrated that going for AI alone is unlikely
to lead
to success. Just as Dr. Mayol and his colleagues collaborated
with a technol-
ogy adviser and provider, DX leaders stated support by
technology partners as
a key success factor, and the unavailability of support by a
technology partner
was stated as an implementation challenge by nearly 20% of
firms. Teaming
with one or several technology partners—which include
technology generalists
such as IBM or Fujitsu, software firms such as Microsoft or
SAP, or consultancies
such as Accenture or Deloitte37—provides firms with two main
advantages. First,
it provides them with early access to new technology in a field
that is still ill-
defined and emergent. Second, it allows them to tap into the
economies of scope
CALIFORNIA MANAGEMENT REVIEW 61(4) 128
and project experiences that both technology firms and
technology consultancies
have accumulated over the past years as AI projects have
mushroomed. For most
firms, AI will, for the foreseeable future, not be an off-the-shelf
product. Hence,
teaming with technology partners to co-create a tailored AI
solution is going to
be the main approach for now.
The importance of teaming extends beyond the confines of
working with
technology partners and includes a firm’s business ecosystem,38
which includes
suppliers, competitors, customers, and alliance partners from
different industries.
Having established an open innovation ecosystem was one of
the organizational
characteristics that set the DX leaders apart from the laggards.
Open innovation
ecosystems are a means to develop innovative offerings
(products and services)
beyond the internal capacity and capabilities of the firms. This
approach recog-
nizes that great talent often resides outside of a firm. Given the
discussed AI tech-
nical skills scarcity, an open innovation ecosystems approach is
particularly fruitful
in the case of AI, and the DX leaders demonstrated this. Having
established an
Open Innovation Ecosystem was one of their defining
characteristics. Generally,
managers have two options: establishing their own ecosystem39
or joining an
existing one.40 Often, as was the case with Dr. Mayol and his
colleagues, the route
to take is shaped by the nature of the collaboration with the
technology partner(s).
Managers are advised to think about AI as an opportunity to
partner and
develop powerful ecosystems. To understand the teaming
options, managers
should address questions such as “Does our firm know with
whom to partner in
support of our DX/AI success?” “Does our firm know with
whom competitors
partner in their DX/AI projects?” “Did our firm develop or join
an ecosystem to
enhance its offerings?”
Be Agile
Organizational agility is both a key barrier and a key success
factor accord-
ing to our empirical analyses. Lack of it was the second most
important AI imple-
mentation challenge, and great levels of organizational agility
was the number
one AI success factor. We found agility to also be one of the
main business
impacts of AI. Agility is, therefore, both a central AI success
antecedent as well as
an outcome of successful AI implementations, thereby
reinforcing its importance
as an antecedent. How can managers foster organizational
agility? Organizational
agility research suggests that a firm’s ability to sense change
and to respond read-
ily to it by reconfiguring its resources, processes, and strategies
is at the core of
organizational agility.41 In the context of AI projects, this
relates to flexibility in
the way the project is approached and managed throughout its
life cycle, as AI
projects tend to be emergent rather than determinis tic, as we
noted earlier (cf.
Be Intelligent).
Managers are advised to assess their company’s agility in a
realistic manner
and implement corrective actions if needed. The following
questions, guided by
the logic of Singh et al.,42 are instructive: “Compared to our
competition, how
quickly and frequently are we adapting our processes and
offerings to stay
Demystifying AI: What Digital Transformation Leaders Can
Teach You 129
competitive?” “Compared to our competition, how flexible are
we to accommo-
dating small, medium, and large changes to our processes and
offerings?”
Lead
Managers should lead and actively endorse the firm’s AI
project(s), just
like Dr. Mayol at the Carlos Clinical Hospital in Madrid, and
not relegate leader-
ship to the project management level. We identified executive
leadership as a
key success factor and lack of leadership support as a key
barrier. The DX leaders
we analyzed demonstrated this fact forcefully. One of their
defining organiza-
tional characteristics was a CEO who prioritized the firm’s
digitalization efforts,
including AI and other advanced digital technologies.
If data are the foundation for impactful AI, leadership provides
the trans-
formational energy for firms to be DIGITAL and, as a
consequence, successful with
AI. Notably, AI has several similarities with other technologies
to be implemented
in firms, and some of the aspects of our DIGITAL framework
would relate to other
change management projects as well. However, the broad nature
of AI (requiring
data, analyses, interdisciplinary teams) makes for some specific
requirements,
such as data, agility, and the teaming aspect.
Managers are advised to reflect honestly, “Is our executive team
and mid-
dle management comfortable and supportive of the changes that
DX/AI will bring
to our firm?” “Is our executive team and middle management
actively endorsing
and continuously communicating the status and progress of our
DX/AI activities
to all stakeholders?”
Conclusion and Outlook
AI certainly holds a lot of promise but it is not a panacea. In
order to reap
its benefits, we developed DIGITAL as a guideline for AI
success grounded in the
empirical insights of close to 7,000 DX projects that involved
new digital technol-
ogies such as AI. The basic approach of this article was to learn
from today’s DX
leader how to become the AI leader of tomorrow. At the same
time, in line with
the title of this article, we believe we can demystify a few
aspects of AI.
The results of this study imply that, in many ways, AI is similar
to other
technologies companies adopt and implement. It certainly is
typically deployed in
digital transformation projects, and, as such, shares many
similarities with other
digital projects. At the same time, the focus on self-learning
projects and long-run
scaling comes with several interesting findings, such as the
focus being integral,
teaming, and agile. In contrast to press reports and also some
academic papers,
our approach to AI is a contemporary and realistic one. Before
visions of “AI tak-
ing over everything” will become true, “realistic AI” will take
place for a long
time. It is and will be a competitive advantage to be quick and
effective in AI, and
our DIGITAL framework and the associated questions to
managers should help
overcome the barriers, but also some of the illusions, so that
“realistic AI” will
become real.
CALIFORNIA MANAGEMENT REVIEW 61(4) 130
A question that we also addressed in the survey was the future
role of
humans in AI projects, which we do not report here in detail due
to the contro-
versial and nonconclusive nature of the responses, and the
vastness of the topic.
But overall, it is interesting that firms are positive about AI as a
technology and
the role of humans. First, they expect that AI and humans will
work together in
the future, rather than working against each other or replacing
humans, for at
least quite some time. Furthermore, managers assume that we
will even see a
human premium emerging in the future, in the sense that people
will be paying
more to get personal, human-to-human services rather than AI
technology–
mediated services. Interestingly, leaders and laggards are united
in this view and
showed no significant differences.
Acknowledgments
The authors wish to thank Fujitsu, Corporate Executive Officer
and former Chief
Marketing Officer Yoshiteru Yamada, and Manager Noriaki
Tanaka for support-
ing the research project.
Author Biographies
Jürgen Kai-Uwe Brock is the former CMO of Fujitsu Americas,
currently work-
ing as a senior assignee at Fujitsu’s HQ in Tokyo, Japan (email:
[email protected]
fujitsu.com).
Florian von Wangenheim is Professor of Technology Marketing,
Department of
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-Journal of General ManagementVol. 26 No.2 Winter 2000

  • 1. - Journal of General Management Vol. 26 No.2 Winter 2000 A Reappraisal ofHRM Models in Britain by Pawan s. Budhwar Human Resource Management is still struggling to find a strategic role. For a better understanding ofthe subj ect, both management practitioners and scholars need to study human resource management (HRM) in context [1]. The dynamics of both the local/regional and international/ global business context in which the firm operates should be given a serious consideration. Similarly, there is a need to use multiple levels of analysis when studying HRM: the external social, political, cultural, and economic environment; and the industry. Examining HRM out- of-context could be misleading and fail to advance understanding. A key question is how to examine HRM in context? One way is by examining the main models of HRM in different settings. However, there is no
  • 2. existing framework that can enable such an evaluation to take place. An attempt has been made in this paper to provide such a framework and empirically examine it in the British context. This paper is divided into three parts. Initially, it summarises the main developments in the field of HRM. Then, it highlights the key emphasis of five models of HRM (namely, the 'Matching model'; the 'Harvard model'; the 'Contextual model'; the '5-P model'; and the 'European model' ofHRM). Lastly, we will address the operationalisation of the key issues and emphases of the aforementioned models by examining their applicability in six industries ofthe British manufacturing sector. The evaluation highlights the context specific nature of British HRM. This introduction looks at the need to identify the core emphasis of the main HRM models that could be used to examine their applicability in different national contexts. Developments in the field of HRM are now well documented in the literature [2, 3]. The debate relating to the nature ofHRM continues today, although the focus of the debate has changed over a period of time. At present, the contribution ofHRM i n improving
  • 3. Pawan S. Budhwar is Lecturer in Organizational Behaviour and HRM at CardiffBusiness School, UK. Journal of General Management Vol. 26 No.2 Winter 2000 the firm's performance and the overall success of any organization (alongside other factors) is being highlighted in the literature [4, 5]. Alongside these debates, a number of important theoretical developments have taken place in the field of HRM. For example, a number ofmodels ofHRM have been developed over the last 15 years or so. Some of the main models are: the 'Matching model'; the 'Harvard model'; the 'Contextual model'; the '5-P model'; and the 'European model' ofHRM [6, 7]. All these models have been developed in the US and the UK. These models ofHRM are proj ected to be useful for analysis both between and within nations. However, the developers of these models do not provide clear guidelines regarding their operationalisation in different contexts. Moreover, it is interesting to note that, although a large number of scholars refer to these models, very few have tested their practical applicability (exceptions being Benkhoff [8]; Monks
  • 4. [9]; Truss et al. [10]). For the development of relevant management practices there is then a clear need not only to highlight the main emphasis of the HRM models but also to show their operationalisation. Such an analysis will help to examine the applicability of these models in other parts of the world. With the increasing levels ofglobalisation ofbusiness such investigations have become an imperative. Moreover, although the present literature shows an emphasis on themes such as 'strategic HRM' (SHRM), the majority of researchers persist in examining only the traditional 'hard' and' soft' models ofHRM [11]. For the growth and development of SHRM, there is a strong need to examine the applicability of those models ofHRM which can help to assess the extent to which it has really become strategic in different parts of the world, and the main factors and variables which determine HRM in different settings. This will not only test the applicability of HRM approaches in different regions, but will also help to highlight the context specific nature of HRM practices. The aims of this paper are twofold. First, to identify the core emphasis offive main models ofHRM which can be used to examine their applicability in different national contexts. Second, to test
  • 5. empirically the applicability of these models of HRM in the British context. Before answering why this investigation is being conducted in the UK, the main models of HRM are briefly analysed. Models of HRM Five models ofHRM, which are widely documented in the literature are chosen for analysis. They are: the 'Matching model'; the 'Harvard model'; the 'Contextual model'; the '5-P model'; and the 'European model' ofHRM [12,13, 14]. The reason for the selection and analysis of thesemodelsis two-fold.First, it will help to highlight their main contribution - .. Journal of General Management Vol. 26 No.2 Winter 2000 to the development of SHRM as a distinct discipline. Second, it will help to identify the main research questions suitable for examining these models in different national settings. The analysis begins with one of the traditional models ofHRM.
  • 6. The strategic fit of HRM The main contributors to the 'Matching model' ofHRM come from the Michigan and New York schools. Fombrun et al. 's [15] model highlights the 'resource' aspect ofHRM and emphasises the efficient utilisation of human resources (like otherresources) to meet organizational objectives. The matching model is mainly based on Chandler's [16] argument that an organization's structure is an outcome of its strategy. Fombrun et al. expanded this premise and developed the matching model of strategic RRM, which emphasises a 'tight fit' between organizational strategy, organizational structure and HRM system, where both structure and HRM are dependent on the organization strategy. The main aim of the matching model is therefore to develop an appropriate 'Human Resource System' that will characterise those HRM strategies that contribute to the most efficient implementation ofbusiness strategies. The Schuler group made further developments to the matching model and its core theme of 'strategic fit' in the late 19?Os [17]. The core issues emerging from the matching models are: 1. Do organizations show a 'tight fit' between their HRM and
  • 7. organization strategy where the former is dependent on the latter? Do personnellHR managers believe they should develop HRM systems only for the effective implementation of their organization strategies? .2. Do organizations consider their HRs as a cost and use them sparingly? Or, do they devote resources to the training of their HRs to make the best use of them? 3. Do HRM strategies vary across different levels of employees? The soft variant of HRM Beer et al. [18] articulated the 'Harvard Model' of HRM. It is also denoted as the 'Soft' variant ofHRM [19], mainly because it stresses the 'human' aspect of HRM and is more concerned with the employer- employee relationship. The model highlights the interests of different stakeholders in the organization (such as shareholders, management, employee groups, government, community and unions) and how their interests are related to the objectives of management. It also recognises the influence ofsituational factors (such as the market situation) on HRM policy choices. According to this model, the actual content of HRM is described in relation to four policy areas i.e. human resource flows,
  • 8. Journal of General Management Vol. 26 No.2 Winter 2000 reward systems, employees' influence and work systems. Each of the four policy areas is characterised by a series of tasks to which managers must attend. The outcomes that these four HR policies need to achieve are commitment, competence, congruence, and cost effectiveness. The model allows for analysis of these outcomes at both organizational and societal levels. As this model acknowledges the role ofsocietal outcomes, it can provide a useful basis for comparative analysis of HRM [20]. The key issues emerging from this model which can be used for examining its applicability in different contexts are: 1. What is the influence ofdifferent stakeholders and situational and contingent variables on HRM policies? 2. To what extent is communication with employees used as a means to maximise commitment? 3. What level of emphasis is given to employee development through involvement, empowerment and devolution? The contextual model of HRM Researchers at the Centre for Corporate Strategy and Change at the Warwick Business School developed this model. They examined
  • 9. strategy making in complex organizations and related this to the ability to transform HRM practices [21,22]. Hendry and associates argue that HRM should not be labelled as a single form of activity. Organizations may follow a number of different pathways in order to achieve the same results. This is mainly due to the existence of a number of linkages between the outer environmental context (socio-economic, technological, political-legal and competitive)and inner organizationalcontext (culture, structure, leadership, task-technology and business output). These linkages directly contribute to forming the content of an organization's HRM. The core issues emerging from this model are: 1. What is the influence of economic (competitive conditions, ownership and control, organization size and structure, organizational growth path or stage in the life cycle and the structure of the industry), technological (type of production systems) and socio-political (national education and training set-up) factors on HRM strategies? 2. What are the linkages between organizational contingencies (such as size, nature, positioning ofHR, and HR strategies) and HRM strategies? Strategic integration of HRM The existing literature reveals a trend in which HRM is becoming an
  • 10. integral part of business strategy - hence, the emergence of the term SHRM. It is largely concerned with 'integration' and 'adaptation'. The purpose of SHRM is to ensure that [23]: - .. Journal of General Management VoL 26 No.2 Winter2000 1. HRM is fully integrated with the strategy and strategic needs of the firm; 2. HR policies are coherent both across policy areas and across hierarchies; and 3. HR practices are adjusted, accepted, and used by line managers and employees as part of their every day work. Based on such premises, Schuler [24] developed a 5-P model of SHRM that melds five HR activities (philosophies, policies, programs, practices and processes) with strategic needs. This model, to a great extent, explains the significance ofthese five SHRM activities in achieving the organization's strategic needs, and shows the inter- relatedness of activities that are often treated separately in the literature. This is helpful in understanding the complex interaction between
  • 11. organizational strategy and SHRM activities. The model raises two important issues (also suggested by many other authors in the field) for SHRM comparisons. These are: 1. What is the level of integration of HRM into the business strategy? 2. What is the level ofresponsibility for HRM devolved to line managers? European model of HRM Based on the growing importance of HRM and its contribution towards economic success and the drive towards Europeanisation, Brewster [25] proposes a 'European model ofHRM'. His model is based on the premise that European organizations operate with restricted autonomy. They are constrained at both the international (European Union) and national levels by national culture and legislation, at the organization level by patterns of ownership, and at the HRM level by trade union involvement and consultative arrangements [26, p. 3]. Brewster suggests the need to accommodate such constraints when forming a model ofHRM. He also talks about 'outer' (legalistic framework, vocational training programs, social security provisions and the ownership patterns) and 'internal' (such
  • 12. as union influence and employee involvement in decision making) constraints on HRM. Based on such constraints, Brewster's model highlights the influence of factors such as national culture, ownership structures, the role of the state and trade unions on HRM, in different national settings. The European model shows an interaction between HR strategies, business strategy and HR practice and their interaction with an external environment constituting national culture, power systems, legislation, education, employee representation and the constraints previously mentioned. It places HR strategies in close interaction with the relevant Journal of General Management Vol. 26 No.2 Winter 2000 organizational strategy and external environment. One important aim of this model is to show factors external to the organization as a part of the HRM model, rather than as a set of external influences upon it. From the above analyses, it can be seen that there is an element of both the contextual and 5-P models of HRM present in Brewster's European model. Apart from the emphasis on 'strategic HRM',
  • 13. one main issue important for cross-national HRM comparisons emerges from Brewster's model. This is: • What is the influence of international institutions, national factors (such as culture, legal set up, economic environment and ownership patterns), and national institutions (such as the educational and vocational set-up, labour markets and trade unions) on HRM strategies and HRM practices? Recently, Budhwar and associates [27, 28,29,30] have proposed a framework for examining cross-national HRM. They have identified three levels of factors and variables that are known to influence HRM policies and practices and which are worth considering for cross-national HRM examinations. These are national factors (such as national culture, national institutions, business sectors and dynamic of the business environment), contingent variables (such as the age, size, nature, ownership, and life cycle stage of the organization, the presence of trade unions and HR strategies, and the interests of different stakeholders) and organizational strategies and policies (related to primary HR functions, internal labour markets, levels ofintegration and devolvement, and nature ofwork). This framework is used to examine the applicability ofthe issues arising from the five HRM models in British organizations. But why conduct this form of investigation, and in the British context?
  • 14. As mentioned already, there is a scarcity of this type of research. So far, only Truss et al. [31] have examined the applicability of some of the models of HRM in a few UK case companies. Apart from their research, there is scarcely any study that conducts the type ofinvestigation described here. There are, then, two main reasons for conducting this investigation in British companies. First, a UK sample possesses the characteristics suitable to test the operationalisation ofthe main emphases and critical issues ofthe five models ofHRM. Second, the HRM function in the UK is under intense pressure due to competitive conditions, and the restructuring and rightsizing programmes going on in British organizations, as well as the pressure on British firms from EU and other international players to stay competitive and meet the EU regulation regarding the management ofhuman resources. In such dynamic business conditions it is worth examining the HRM function in context. Moreover, since the five models have been developed among Anglo-Saxon nations, it is sensible to test them initially in these countries before recommending their testing in others parts of the world. -
  • 15. .. Journal of General Management Vol. 26 No.2 Winter 2000 The Research Methodology Sample and data collection A mixed methodology, using a questionnaire survey and in- depth interviews, was adopted. During the first phase of the research, a questionnaire survey was conducted between August 1994 and December 1994 in British firms having 200 or more employees in six industries in the manufacturing sector (food processing, plastics, steel, textiles, pharmaceuticals and footwear). The respondents were the top personnel specialist (one each) from each firm. The response rate ofthe questionnaire survey was approximately 19 per cent (93 out of 500 questionnaires). The items for the questionnaire were constructed from existing sources, such as those developed by Cranfield researchers in their study ofcomparative European HRM [32] and other studies (see for example [33, 34]). The questionnaire consisted of 13 sections. These were: HR department structure, role of the HR function in corporate strategy,
  • 16. recruitment and selection, pay and benefits, training and development, performance appraisal, employee relations, HRM strategy, influence ofnational culture, national institutions, competitive pressures and business sector on HRM, organizational details. Public limited companies represented approximately one-third of the sample, with the remainder from the private sector. The industry-wide distribution of respondents is shown in Table 1. Table 1: Sample Industry Distribution Indtitry Percentage . Food Processing 17.2 Plastics 17.2 Steel 16.1 Textiles 17.2 Pharmaceuticals 21.5 Footwear 10.8 Analysis of the demographic features of the sample suggests that the sample was representative ofthe total population. Sixty-two per cent of sample organizations were medium-sized and employed 200- 499 employees, 14 per cent employed 500-999 employees, 15 per cent 1000- 4999 employees, and 8 per cent employed 5000 or more employees. In the second phase of the research, 24 in-depth interviews were conducted with personnel specialists representative of those
  • 17. firms which participated in the first phase of the research. The interviews examined six themes, viz. the nature of the personnel function, integration ofHRM into the corporate strategy, devolvement ofHRM to line managers, and the influences of national culture, national institutions and business environment dynamic on HRM. Journal of General Management Vol. 26 No.2 Winter 2000 Measures Multiple regression analysis and descriptive statistics are used to analyse questionnaire data. Table 1 in the Appendix shows the main dependent and independent variables used for multiple regression analysis. Table 2 in the Appendix presents the mean scores of respondents regarding the influence of different aspects of national factors (culture, institutions, business environment dynamic and business sector) and HR strategies on HRM policies and practices. The qualitative data is content analysed. In the discussion, survey results are complemented by key messages coming from the qualitative interviews.
  • 18. Findings of the Study The matching models suggest a strong dependence ofHRM on organization strategy, i.e, HRM is mainly developed for the effective implementation of organization strategies. The results show that in 34.6 per cent of the organizations under study personnel is involved from the outset in the formation of corporate strategy, and 42 per cent of organizations actively involve HRM during the implementation stage of their organizational strategies. Such a trend of 'active' personnel management is further evident from 55 per cent of sample organizations having personnel representation at board level. Moreover, 81.1 per cent ofthe respondents believe that their HRM has become proactive over the last five years (i.e. more involved in decision making). Such results reflect the growing strategic and proactive nature of the British personnel function. There is support for such findings in the existing literature [35, 36]. The second reason to examine the matching models in a cross- national context is to assess whether human resources are considered as a cost ('use them sparingly') or as an asset (spend on training to 'make their best use '). The results suggest that British organizations
  • 19. claim to be spending variable though reasonable proportions oftheir annual salaries on human resource development (HRD) related activities (see Table 2). Table 2: Proportion of Annual Salaries and Wages Currently Spent on Training and Development Value(%) Percentage of Sample Nil - 0.1- 2.00 41.3 2.01-4.00 7.6 4.01- 6.00 3.3 6.01 or more 1.1 Don't know 46.7 - .. Journal of General Management Vol. 26 No.2 Winter2000 A similar pattern characterizes the number ofdays training provided to different levels ofemployees (see Table 3). The substantial majority of British firms have increased (rather than maintained or reduced) their training spend across all categories of staff over the last five years (see
  • 20. Table 4). There is evidence that this investment has been directed particularly in the areas of performance appraisal, communication, delegation, motivation and team building. Table 3: Average Number of Days Training and Development Given to Staff Categories Per Year Different Cat~ories of Staff Number ofDays Mana}!erial(%) Prof,/Technical(%) Clerical(%) Manual(%) Nil 1.2 1.1 2.3 1.2 0.1-3.00 24.4 22.8 35.6 24.7 3.01-5.00 20.9 21.7 13.8 11.7 5.01-10.00 7.0 14.7 4.6 11.8 10.1 and above 5.8 4.6 3.5 9.4 Don't know 40.7 40.9 40.2 41.2 These developments in the British HRD scene appear to be consistent with the increased realisation by both business and government that the development of human resources has been neglected for too long [37]. Table 4: Nature of Change in Amount of Money Spent on Training Per Employee Different Categories of Staff Nature ofChange Mana}!erial("/o) Prof,/Technical("/o) Clerical(%) Manual(%) Increased 59.8 63.0 53.3 60.9
  • 21. Same 21.7 18.5 28.3 20.7 Decreased 7.6 8.7 7.6 7.6 Don't know 10.9 9.8 10.9 10.9 Another key emphasis of the matching model suggests a variation in HRM strategies across different levels of employees. This is clearly evident from the results as the nature and type of approach to the management of different levels of employees vary significantly (see for example, Tables 3 and 4). This aspect is further highlighted later in this paper. Based on the above evidence, it seems that the British personnel function still plays an implementationist role rather than being actively involved in strategy formulation. On the other hand, there is a strong emphasis on training and development. Important Situational Determinants One of the basic assumptions of the Harvard model of HRM is the influence of a number of situational factors (such as work force Journal of General Management Vol. 26 No.2 Winter 2000 characteristics, unions, labour legislation and business strategy) and different stakeholders (such as unions, government and
  • 22. community) on HRM policies. The impact of a few of the situational factors and stakeholders (proposed by Beer et al. [38D was examined during the multiple regressions, analysis of means scores and the analysis of interview results. Taking the number of employees as a characteristic of the work force [39, 40], the regression results show that small British organizations (those having less than 499 employees) are likely to recruit their managerial staff by advertising externally. Medium size organizations (those having 500 to 999 employees) are likely to recruittheirclerical staffas apprentices. Large organizations (those having 1000 to 4999 employees) are more likely to use assessment centres to train their human resources. Lastly, very large firms (having over 5000 employees) are less likely to recruit their managerial staff by advertising internally and their manual staff through the use of word of mouth method. These firms are likely, however, to recruit their professional staff with the help of consultants. Moreover, large UK firms are more likely to adopt formal career plans, succession plans and planned job rotation to develop their human resources (for details see Table 1 in Appendix).
  • 23. Support for these findings can be found in the literature (see for example, [41D. The size ofan organization has a positive relation with the formalism of their HRM policies [42]. Therefore, as the size of the firm becomes large, logically, the degree offormalism ofits personnel function increases and the organization obtains the help ofrecruitment agencies to recruit its professional employees. The results show a strong impact of labour laws, educational and vocational training set up (highlighting government policy) and unions on British HRM policies (see Table 2 in Appendix). Unions in the UK are now playing a more supportive role [43]. The implementation of labour legislation is also having significant influence on UK HRM policies. Various pressures groups also contribute in this regard (for example, against age discrimination). Over the last decade or so, the education and vocational set-up in the UK has initiated a number of programmes and qualifications such as the national vocational qualifications (NVQs), investors in people (IIP) and' opportunity 2000' . These are now significantly influencing HRM in British organizations [44]. The results also show a number of significant regressions regarding the impact of HR strategies on British HRM. Results in Table 1
  • 24. in the Appendix show that organizations pursuing a cost reduction strategy are more likely to recruit their clerical and manual staffas apprentices. These organizations are likely to adopt an effective resource allocation HR strategy. Organizations pursuing a talent improvement HR strategy are - Journal of General Management Vol. 26 No.2 Winter 2000 less likely to recruit their manual staff by word of mouth method. However, sample firms pursuing a talent acquisition HR strategy are likely to use consultants to recruit their managerial staff and recruitment agencies for manual staff. These organizations are also likely to adopt assessment centres to train their staff. Most of the above results seem to be logical. For example, by recruiting employees as apprentices organizations not only pay them less but also train and prepare them for working in the long run in their organizations. Hence, it helps to reduce the costs. Similarly, by recruiting employees externally, organizations increase the opportunit y to improve
  • 25. their talent base. The second key emphasis of the Harvard model of HRM suggests extensive use of communication with employees as a mechanism to maximise commitment [45, p. 63]. Ninety-one per cent of British organizations share information related to both strategy and financial performance with their managerial staff. However, this percentage is significantly lower for other categories of employees (see Table 5). Table 5: Employees Formally Briefed about Strategy or Financial Performance Different Categmes of Staff Tvoe ofInformation Managerial(%) Prof/Technical(%) Clerical(%) Manual(%) Strategy - 8.0 8.6 6.4 Financial Performance 6.5 14.8 39.5 38.5 Both 91.3 65.7 42.0 23.6 Neither 2.2 11.6 9.9 31.5 There can be a number of explanations for the difference in the sharing of strategic and financial information with different levels of employees in British organizations. Whilst noting that top personnel specialists are now more and more involved in strategy making, it seems that top management continue to be reluctant to devolve responsibility to line managers for the dissemination offinancial and strategic
  • 26. information. These issues are further examined when discussing the 5-P model. The above discussion suggests applicability of the Harvard model ofHRM in British organizations. The results showed an impact oflabour laws, education vocational set-up, unions, work force characteristics and HR strategies on HRM policy choices. There are encouraging results on the communication of information with different levels of employees regarding sharing strategic and financial performance and on employee development through their involvement and training. Contextual Factors The main issue against which the relevance of the contextual model can be evaluated is the impact on HRM policies and practices of economic Journal of General Management Vol. 26 No.2 Winter 2000 (characterized by competitive pressures, ownership and life cycle stage), technological (type ofproduction system)and socio-political (characterised by national education and training set-up) factors and organizational
  • 27. contingencies (such as size, age and nature of organization). The results show a strong influence of competitive pressures on British HRM policies and practices (see Table 2 in Appendix). To achieve a competitive edge in such situations, they are focusing particularly on total customer satisfaction and the restructuring oftheir organizations. As competitive pressures are also forcing British organizations to enter into new business arrangements (such as alliances), so these are having direct influence on HRM policies and practices. The results also show the impact of increasingly sophisticated informationand communications technology on HRM policies and practices (see Table 2 in the Appendix). Further evidence indicates that the majority of respondents suggest these technologies mainly influence training, appraisal and transfer functions. Why? Because with the change in technology, employees need to be trained to handle it. To see if they have achieved the required competence they are appraised and if required, transferred to suitable positions. Finally, we summarise the relevance of the contextual model of HRM in terms ofthe impact of organizational contingencies. Contingent variables such as size of the organization, presence of HR strategy and presence of unions were examined above, as were the impacts of ownership and organizational life cycle stage. These variables
  • 28. do not seem significantly to impact HRM in British organizatio ns. Nevertheless, there is significant evidence overall regarding the applicability of the contextual model ofHRM in British organizations. Strategic Integration and Devolvement of HRM in Britain Our discussion now focuses on the relevance of the '5 P' model ofHRM in British organizations. To achieve this, results regarding the integration of HRM into corporate strategy and the devolution of responsibility for HRM to line managers are examined. The detailed results are presented elsewhere [46], but are summarized below. In brief, the level of integration is measured on the basis of the following four scales: a) representation of Personnel on the board; b) presence of a written Personnel strategy; c) consultation ofPersonnel (from the outset) in the development of corporate strategy; and d) translation ofPersonnel/HR strategy into a clear set of work programmes. -
  • 29. Journal of General Management Vol. 26 No. 2 Winter 2000 The level of devolvement is measured on the basis of the following three scales: a) primary responsibility with line managers for HRM decision making (regarding pay and benefits, recruitment and selection, training and development, industrial relations, health and safety, and workforce expansion and reduction); b) change in the responsibility of line managers for HRM (regarding pay and benefits, recruitment and selection, training and development, industrial relations, health and safety, and workforce expansion and reduction); and c) percentage ofline managers trained in performance appraisal, communication, delegation, motivation, team building and foreign language. High integration is the result of personnel representation at board level, the personnel function being consulted about corporate strategy from the outset, the presence of a written personnel strategy, and the translation of such a strategy into a clear set of work programmes. As mentioned earlier, the personnel function is represented at board level in the majority (55 per cent of organizations). For our sample companies, 87.4 per cent have corporate strategies. Of these, 34.6 per cent consult the personnel function at the outset, 42 per cent involve
  • 30. personnel in early consultation, and only 13.6 per cent involve personnel during the implementation stage. Over a quarter (26.4 per cent) of sample organizations did not have a personnel strategy, 29.9 per cent had an unwritten strategy and 43.7 per cent had a written personnel strategy. A clear majority (57.4 per cent) of organizations felt that their personnel strategy was translated into clear work programmes. High devolvement is the result of: primary responsibility for pay, recruitment, training, industrial relations, health and safety and expansion/ reduction decisions lying with the line (see Table 6); line responsibility for these six areas on an increasing trend (see Table 7); and, evidence of devolved competency with at least 33 per cent of the workforce being trained in appraisals, communications,delegation, motivation, team building and foreign languages. Budhwar's [47] analysis shows that when the four measures of integration are summated and divided into a single scale of high and low type, 50.5 per cent of the sample organizations would be categorised as having high integration and 49.5 per cent fall into the low integration category. The average score of the summated integration scale for a1193 organizations is .50. These results show a moderate level of
  • 31. integration being practised in the sample industries. On the other hand, the summated scales demonstrate a low level of devolvement. Sixty-one per cent of the sample practise low levels of devolvement of HRM to line managers. Journal of General Management Vol. 26 No.2 Winter 2000 Table 6: Primary Responsibility for Major Decisions on Personnel Issues Personnel Issues Line Line Mgt in IIR Dilpt. inHRDept. Consultation COllSuJtationRelated to: Mgt. wi!il1lB.l)llUt. withLineMat. Pay andBenefits 48.3 14.3 11.0 26.4 Recruitment and Selection 17.2 12.9 34.4 35.5 Training andDevelopment 15.1 18.3 22.5 44.1 Performance Aonraisal 17.5 6.9 30.4 45.2 Industrial Relations 36.3 13.2 25.3 25.2 Health and Safety 18.5 32.6 19.6 29.3 Workforce 19.4 19.4 44.1 17.1Expansion/Reduction WorkSystem/Job Design 7.6 33.7 40.2 18.5 Figures in the above cells represent valid percentage, calculated after excluding the missing values. Table 7: Change in Responsibility of Line Management for Different Personnel Issues
  • 32. PellSonnelIssues Increased (%) Same(%) Decreased (%) Pay andBenefits 27.2 65.2 7.6 Recruitment and Selection 43.5 48.9 7.6 Training and Development 69.6 23.9 6.5 Performance Appraisal 60.0 37.8 2.2 Industrial Relations 28.9 63.3 7.8 Healthand Safety 61.5 35.2 3.3 Workforce 38.9 54.4 6.7Expansion/Reduction WorkSystem/Job Design 43.3 53.3 3.3 The results confirm the relevance of the 5-P model of HRM in British organizations. They also help to examine the main emphasis of Brewster's [48] European model of HRM, i.e, the linkages between corporate strategy and HRM strategy. Conclusion Overall, the results show a mixed picture, i.e. from strong to moderate applicability of the mentioned HRM models in Britain. The study aimed to examine HRM in context, and the findings should be useful for relevant policy makers. In particular, it seems that the sample firms are practising a relatively low level of devolvement in comparison to the integration function. Ifthe HRM function is to become more strategic, then the level of practice of both these concepts has to increase. Such demands are likely to increase in future as more and more firms restructure
  • 33. and become lean in order to respond to competitive and other pressures [49]. The study has two main limitations. First, it is restricted to six industries ofthe UK manufacturing sector. Second, the views of only top .. Journal of General Management Vol. 26 No. 2 Winter 2000 personnel specialists were examined. In order, therefore, to obtain a more comprehensive picture, research needs to be extended to other business sectors and to the views of other key actors (such as line managers). Future research could also build upon this study by investigating other models ofHRM and their applicability in different national contexts. Appendix Table 1: Factors Determining HRM Practices in British Organizations Independent. lJependentVariables If BiJta . t·valueVarin/J/es Training and development 0.2102 0.2984* 2.3790 Introductory through planned iob rotation
  • 34. lifecycle stage Communication through 0.1629 -0.2663* -2.0720 immediate superior Turnaround Recruiting managerial staff by 0.3695 -0.3038* -2.6170 lifecycle stage advertising externally Recruiting managerial staff by 0.3695 0.3658** 3.0590 Less than 499 advertising externally employees Recruiting clerical staff from 0.1014 -0.3184* - 2.4220 recruitment agencies Between 500- Recruiting clerical staff as 0.3337 0.2891* 2.4600 599 employees apprentices Between 1000- Training and development 4999 through assessment centres 0.2607 0.3547** 2.8530 employees Recruiting managerial staff by 0.1563 -0.2835* -2.1800 advertising internally Recruiting professionals/technical staff by 0.1039 0.3223* 2.4550 use of search/selection More than
  • 35. consultants 5000 Recruiting manual staffby 0.3698 -0.4529** -3.9340 employees word of mouth Training and development through formal career plans 0.1406 0.375** 2.9170 Training and development 0.1685 0.4105** 3.2460 through succession plans Training and development 0.2102 0.3873** 3.0880 though planned job rotation Public Limited Recruiting managerial staff by 0.3695 0.4436** 3.8050Company advertising externally Recruiting managerial staff 0.0830 -0.2881* -2.1700 from current employees State-owned Recruiting clerical staff from 0.2842 -0.2583* -2.0650 organization current emnlovees Recruiting manual staff by
  • 36. 0.3698 -0.3342** -2.9100 word of mouth Organizations incorporated Commnnication through trade 0.7445 -0.216** -3.0370 between 1869- unions or work councils 1899 Organizations incorporated Recruiting manual staff from 0.1557 0.2609* 2.0240 between 1900- current employees 1947 Continued ... Journal of General Management Vol. 26 No.2 Winter 2000 Table 1 Continued: Independent lJepen4ent Variables .Jf Beta tvalueVariable Recruitingclericalstaffby 0.2465 -0.3931** -3.2110advertising externally Recruitingmanualstaffby 0.1974 -0.2767* -2.1550advertising externally Organizations Trainingand development 0.2607 0.4364** 3.3780incorporated throughassessment centres between 1948- Communication through 0.1629 -0.3255* - 2.53201980 immediatesuperior
  • 37. No formal communication 0.3517 0.3265** 2.7370methods Communication through 0.0858 0.2929* 2.2090suggestion box(es) Recruitingclericalstaff from 0.2842 -0.3019* -2.4240current employees Cost reduction Recruitingclericalstaff as 0.3337 0.4182** 2.9450HRstrategy apprentices Recruiting manualstaff as 0.1330 0.3646** 2.8240apprentices Talent Recruitingmanualstaff by 0.3698 -0.3655** - 3.2440improvement word of mouthHRstrategy Recruiting managerial staffby 0.0777 0.2787* 2.0930use of search/selection Talent consultants acquisition HR Recruitingmanualstaff from 0.0914 0.3024* 2.2880strategy recruitmentagencies Trainingand development 0.2607 0.2857* 2.2090throughassessment centres Effective Recruitingclericalstaff as 0.3337 0.2882* 2.0300resourceHR apprenticesstrategy Recruitingmanagerial staff by 0.3695 0.3593** 2.9750advertising externally Recruitingmanualstaff by 0.1226 0.3502** 2.6960Unionised advertising internally firms Communication through 0.3517 -0.255* -2.1820attitude survey Communication throughtrade 0.7445 0.5656** 6.4000unions or work councils * Significance at .05 level; **Significance at .01 level
  • 38. - .. Journal of General Management Vol. 26 No.2 Winter 2000 Table 2: Influence of Different Aspects of National Factors on HRM Aspectsoff"lational (;ultttre No. of Cases Mean 1 Way in which managers are socialised 84 18.07 2 Common values, norms of behaviour and customs 81 20.28 3 The influence of pressure groups 58 10.47 4 Assumptions that shape the way managers perceive and 84 25.98 think: about the organization 5 The match to the organization's culture and 'the way we 86 35.58 do things around here' N(ltif.}1Inl T- o '011.6 1 National Labour Laws 82 40.91 2 Trade Unions 61 21.72 3 Professional Bodies 56 15.11 4 Educational and Vocational training set-up 84 27.62 5 International Institutions 54 20.07
  • 39. A~l1ects QflIusinessEnvironment 1 Increased national/international competition - 72 27.56 Globalisation of corporate business structure Growth of new business arrangements, e.g. business 2 alliances, joint ventures and foreign direct investment 66 19.01 through mergers and acquisitions 3 More sophisticated information/communication 70 19.62 technology or increased reliance on automation 4 Changing composition of the workforce with respect to 48 12.39 gender, age, ethnicity and changing employee values 5 Downsizing of the workforce and business re- 69 23.13 engineering 6 Heightened focus on total management or customer 78 26.92 satisfaction
  • 40. Aspects qfBusinessSector 1 Common strategies, business logic and goals being 71 22.95 pursued by firms across the sector 2 Regulations and standards (e.g. payments, training, 79 20.35 health and safety) specific to your industrial sector Specific requirement/needs of customers or suppliers 3 that characterise your sector (i.e. supply chain 82 28.96 management) 4 The need for sector-specific knowledge in order to 56 15.35 provide similar goods/services in the sector 5 Informal or formal benchmarking across competitors in 61 16.39the sector (e.g, best practices of market leaders) Cross-sector co-operative arrangements, e.g, common 6 technological innovations followed by all firms in the 37 10.54 sector 7 Common developments in business operations and work 49 14.40
  • 41. practices dictated by the nature of the business 8 A labour market or skill requirement that tends to be 39 13.10used by your business sector only Respondentswere asked to allocate a totalof100points to the different aspects ofthe above nationalfactors. Journal of General Management Vol. 26 No.2 Winter 2000 References [1] Jackson, S. E. and Schuler, R. S., 'Understanding Human Resource Management in the Context ofOrganizations and their Environment' , Annual Review of Psychology, Vol. 46, 1995, pp. 237-264. [2] Legge, K., Human Resource Management: Rhetorics and Realities, Chippenham: MacMillan Business, 1995. [3] Sisson, K. and Storey, J., The Realities of Human Resource Management, Buckingham: Open University Press, 2000. [4] Guest, D. E., 'Human Resource Management and Performance: A Review and Research Agenda', International Journal ofHuman Resource Management, Vol. 8, No.3, 1997, pp. 263-276. [5] Schuler. R. S. and Jackson, S. E., Strategic Human Resource
  • 42. Management, London: Blackwell, 1999. [6] Brewster, C., 'Towards a European Model of Human Resource Management', Journal of International Business Studies, Vol. 26, No.1, 1995, pp. 1-22. [7] Legge, K., 1995, op. cit. [8] Benkhoff, B., 'A Test ofthe HRM Model: Good For Employers and Employees', Human Resource Management Journal, Vol. 7, No.4, 1997,pp.44-60. [9] Monks, K., 'Global or Local? HRM in the Multinational Company: The Irish Experience', The International Journal of Human Resource Management, Vol. 7, No.3, 1996, pp. 721-735. [10] Truss, C., Gratton, L., Hailey, H., McGovern, P. and Stiles, P., 'Soft and Hard Models ofHuman Resource management: A Reappraisal' , Journal ofManagement Studies, Vol. 34, No.1, 1997, pp. 53-73. [11] Legge, K., 1995. op. cit. [12] Brewster, C., 1995, op. cit. [13] Legge, K., 1995. op. cit. [14] Poole, M., 'Editorial: Human Resource Management in An International Perspective', International Journal of Human Resource Management, Vol. 1, No.1, 1990, pp. 1-15. [15] Fombrun, C. J., Tichy, N. M. and Devanna, M. A., Strategic Human Resource Management, New York: Wiley, 1984.
  • 43. [16] Chandler, A., Strategy and Structure, Cambridge, MA: MIT Press, 1962. [17] Schuler, R. S. and Jackson, S. E., 'Organizational Strategy and Organizational Level as Determinants of Human Resource Management Practices', Human Resource Planning, Vol. 10, No.3, 1987, pp. 125-141. [18] Beer, M., Spector, B., Lawrence, P. R., Quinn Mills, D. and Walton, R. E., Human Resource Management, New York: Free Press, 1984. [19] Legge, K., 1995. op. cit. [20] Poole, M., 1990. op. cit. .. [21] [22].. [23] [24] [25] [26] [27] [28] Journal of General Management Vol. 26 No. 2 Winter 2000 Hendry, C and Pettigrew, A.M., 'Patterns of Strategic Change in
  • 44. the Development of Human Resource Management', British Journal ofManagement, Vol. 3, 1992, pp. 137-156. Hendry, C., Pettigrew, A. M. and Sparrow, P. R., 'Changing Patterns of Human Resource Management,' Personnel Management, Vol. 20, No. 11, 1988, pp. 37-47. Schuler, R. S., 'Linking the People with the Strategic Needs of the Business', Organizational Dynamics, Summer, 1992, pp. 18-32. Ibid. Brewster, C., 1995, op. cit. Ibid. Budhwar, P., 'Taking Human Resource Management Research To The Next Millennium: Need For An Integrated Framework', Annual Academy of Management Conference, Chicago, 1999. Budhwar, P. and Debrah, Y., 'Rethinking Comparative and Cross National Human Resource Management Research,' The International Journal of Human Resource Management, 2001 (forthcoming). [29] Budhwar, P. and Sparrow, P., 'An Integrative Framework For Determining Cross National Human Resource Management Practices', Human Resource Management Review, 2001 (forthcoming) . [30] Budhwar, P. and Sparrow, P., 'National Factors Determining Indian and British HRM Practices: An Empirical Study', Management International Review, Vol. 38, Special Issue 2, 1998, pp. 105-121. [31] Truss, C., Gratton, L., Hailey, H., McGovern, P. and Stiles, P., 1997, op. cit.
  • 45. [32] Brewster, C. and Hegewisch, A., (eds.) Policy and Practice in European Human Resource Management, London and New York: Routledge, 1994. [33] Baird, L. and Meshoulam, r., 'Managing Two Fits of Strategic Human Resource Management', Academy of Management Review, Vol. 13, No.1, 1988, pp. 116-128. [34] Jackson, S. E., Schuler, R. S. and Rivero, J. C., 'Organizational Characteristics as Predictors of Personnel Practice', Personnel Psychology, Vol. 42, No.4, 1989, pp. 727-786. [35] Legge, K., 1995, op. cit. [36] Hendry, C., Human Resource Management: A Strategic Approach to Employment, Bath: Butterworth-Heinemann, 1998. [37] Townley, B. 'Communicating with Employees' , in Sisson, K. (ed.), Personnel Management: A Comprehensive Guide to Theory and Practice in Britain, Blackball Business: London, 1996, pp.595- 633. [38] Beer, M., Spector, B., Lawrence, P. R., Quinn Mills, D. and Walton, R. E., 1984, op. cit. [39] Jackson, S. E. and Schuler, R. E., 1995, op. cit. [40] Tayeb, M., Organizations and National Culture, London: Sage,
  • 46. 1988. Journal of General Management Vol. 26 No.2 Winter 2000 [41] Sisson, K. and Storey, J., 2000, op. cit. [42] Brewster, C. and Hegewisch, A., 1994, op. cit. [43] Heery, E., 'Annual Review Article 1996'. British Journal of Industrial Relations, Vol. 35, 1997, pp. 87-109. [44] Collin, A. and Holden, L., 'The National Framework for Vocational Education and Training', in. Beardwell, 1. and Holden, L., (eds.), Human Resource Management. London: Pitman Publishing, 1997, pp.345-377. [45] Truss, C., Gratton, L., Hailey, H., McGovern, P. and Stiles, P., 1997,op. cit. [46] Budhwar, P., 'Strategic Integration and Devolvement of Human Resource Management in the British Manufacturing Sector' , British Journal of Management, Vol. 11, No.4, 2000, (in Press). [47] Ibid. [48] Brewster, C., 1995, op. cit. [49] Terry, M. and Purcell, J., 'Return to Slender' , People Management,
  • 47. 23 October, 1997,46-51. - 110 https://doi.org/10.1177/1536504219865226 California Management Review 2019, Vol. 61(4) 110 –134 © The Regents of the University of California 2019 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/1536504219865226 journals.sagepub.com/home/cmr Special Issue on AI Demystifying AI: What Digital transformation leaDers Can teaCh You about realistiC artifiCial intelligenCe Jürgen Kai-Uwe Brock1 and Florian von Wangenheim2 SUMMARY Recent years have seen a reemergence of interest in artificial intelligence (AI) among both managers and academics. Driven by technological advances and public interest, AI is considered by some as an unprecedented revolutionary technology with the
  • 48. potential to transform humanity. But, at this stage, managers are left with little empirical advice on how to prepare and use AI in their firm’s operations. Based on case studies and the results of two global surveys among senior managers across industries, this article shows that AI is typically implemented and used with other advanced digital technologies in firms’ digital transformation projects. The digital transformation projects in which AI is deployed are mostly in support of firms’ existing businesses, thereby demystifying some of the transformative claims made about AI. This article then presents a framework for successfully implementing AI in the context of digital transformation, offering specific guidance in the areas of data, intelligence, being grounded, integrated, teaming, agility, and leadership. KeYwoRDS: artificial intelligence, polls and surveys, managers, management, management skills I n 2014, Dr. Julio Mayol wondered, “We have access to a vast quantity of data but it’s hard to extract meaningful information that helps us improve the quality of the care we provide.” Dr. Mayol, Medical Director and Director of Innovation at the Carlos Clinical Hospital in Madrid, Spain, founded in 1787, found the answer after consulting with an external group
  • 49. of technology advi- sors: artificial intelligence (AI). About six months later, the innovation unit of the hospital, under his leadership, embarked on a project to apply AI. Rather than opt- ing for an off-the-shelf generic solution, the team worked closely with a technology 1Fujitsu, Tokyo, Japan 2ETH Zurich, Zurich, Switzerland 865226CMRXXX10.1177/1536504219865226California Management ReviewDemystifying AI: What Digital Transformation Leaders Can Teach You research-article2019 https://us.sagepub.com/en-us/journals-permissions https://journals.sagepub.com/home/cmr Demystifying AI: What Digital Transformation Leaders Can Teach You 111 provider of AI solutions and co-created an innovative new AI application tailored to their specific needs. After one year, the system was ready for field testing. Six months later, first results showed that the diagnostic and patient’s risks assessment solution can cut the time in half for preliminary assessment of patient records, with a 95% accuracy compared with eight domain experts who were psychiatrists with more than 20 years of experience. An unanticipated positive side effect of this immense increase in efficiency was that the medical staff had
  • 50. much more time for consultations and patient care, thereby increasing customer satisfaction.1 This exemplary case of a successful AI2 application stands in stark contrast to mounting evidence of AI failures,3 gaps between firms’ AI ambition and execu- tion,4 and a general “post-AI-hype sobering.”5 Given the mixed evidence and the paucity of empirical insights related to the successes and failures of AI implementa- tion projects, we embarked on a global research project with the aim of understand- ing managers’ perceptions and evaluations of AI. This research was informed by insights derived from the opening case as well as publicly available case study mate- rial.6 All these cases were excluded from the survey investigation. Between 2016 and 2018, more than 3,000 executives and managers were surveyed globally from across industries with a total of nearly 7,000 projects, including the application of AI and other advanced digital technologies. We do not necessarily assume that manag- ers know best, but they are important information sources about current and future AI potential for various reasons. First, given their involvement and business inter- est, they are the decision makers about future projects. Second, their perceptions and experiences will influence future implementation success. Based on the above assessment (with high expectations and a few success
  • 51. stories on the one side, but frustrating experiences and stopped projects on the other side), we first establish the current prevalence of AI in business (study 1) and then explore key dimensions of successful AI implementations (study 2). Specifically, study 1 focused on the following research question (RQ1; see Figure 1): Research Question 1 (RQ1): To what extent has the application of AI diffused in business? In study 2, we explored AI in business in more detail. Specifically, we were interested in the following three research questions (RQ2-RQ4; see Figure 1): Research Question 2 (RQ2): What are the anticipated perceived business impacts of AI? More specifically, to what extent are managers expecting impacts in the area of operations, offerings, and customer interactions?7 Research Question 3 (RQ3): Assuming differences concerning the per- ceived business impact of AI, what explains those differences, for example, on a country, industry, firm level, executive (respondent), and skill level? Research Question 4 (RQ4): Given the opening case of AI success, can we identify leaders, firms that are experienced and successful in
  • 52. CALIFORNIA MANAGEMENT REVIEW 61(4) 112 implementing AI-related projects?8 What makes them different from lag- gards, firms that have not yet moved beyond the planning phase, in terms of country-, industry-, firm-level factors, executive (respondent) factors, skills, and organizational traits? Furthermore, could these leaders real- ize more positive business impacts in areas such as operational efficiency, organizational agility, revenue growth, competitiveness, and customer experience? And how long does it usually take from the start of an AI project to achieving business impacts? In addition, we investigate whether leaders differ in their perception of AI implementation challenges and key success factors. Research Structure and Guiding Frameworks Figure 1 illustrates our research structure and the guiding frameworks we used. Phase 1 of our research, the exploration phase, was informed by ini- tial case study research (e.g., the opening case) and a first digital transformation Figure 1. Structure and guiding frameworks of the research.
  • 53. Sources: Framework references (in alphabetical order): John Hagel III and Marc Singer, “Unbundling the Corpo- ration,” Harvard Business Review, 77/2 (March/April 1999): 133-141; E. T. Penrose, The Theory of the Growth of the Firm (London: Wiley, 1959); Lynn W. Phillips, “Assessing Measurement Error in Key Informant Reports: A Methodological Note on Organizational Analysis in Marketing,” Journal of Marketing Research, 18/4 (November 1981): 395-415; Everett M. Rogers, Diffusion of Innovations, 4th ed. (New York, NY: The Free Press, 1995); B. Wernerfeld, “A Resource-based View of the Firm,” Strategic Management Journal, 5/2 (April-June 1984): 171- 180; Robert K. Yin, Case Study Research: Design and Methods, 4th ed. (Los Angeles, CA: Sage, 2009). Demystifying AI: What Digital Transformation Leaders Can Teach You 113 and AI survey that aimed at understanding the extent of AI diffusion across firms worldwide. Given that AI is a recently adopted technology for most firms, this stage of our research was informed by diffusion of innovation theory in general and organizational innovation adoption implementation research in particular. From this body of past research, plus managerial interviews, we developed our stages of AI implementation model (see Figure 2). Phase 2 of our research, the descriptive phase, aimed at a broad yet deeper understanding of AI in firms’ digi- tal transformation worldwide. As the differential analysis emerged in phase 2 of
  • 54. our research, this phase was guided by three related organizational frameworks: the theory of the growth of the firm, the resource-based view of the firm, and the pragmatic firm conceptualization proposed by Hagel and Singer.9 All three have Figure 2. Status of AI implementation in firms (study 1). Source: We derived these five stages from interviews with managers and the organizational stages-of- innovation-adoption-implementation literature (e.g., G. W. Downs Jr., and L. B. Mohr, “Conceptual Issues in the Study of Innovations,” Administrative Science Quarterly, 21/4 (December 1976): 700-714; M. A. Scheirer, “Approaches to the Study of Implementation,” IEEE Transactions on Engineering Management, 30/2 (1983): 76- 82; L. G. Tornatzky and B. H. Klein, “Innovation Characteristics and Innovation Adoption-implementation: A Meta-analysis of Findings,” IEEE Transactions on Engineering Management, 29/1 (1982): 28-45; Everett M. Rogers, Diffusion of Innovations, 4th ed. (New York, NY: The Free Press, 1995). Note: n = 1,614 firms, worldwide, 2016-2017: “Which best describes the progress of your firm’s AI (Artificial Intelligence) implementation?” AI = artificial intelligence; DX = digital transformation. CALIFORNIA MANAGEMENT REVIEW 61(4) 114 an internal resources focus in common and have guided our assessments in terms of firms’ experience, capabilities, challenges, and success factors. As a multistage,
  • 55. mixed-methods research design, research phase 1 informed research phase 2. Data Collection Data for this study were obtained in two waves (see Figure 1). Following a set of digital transformation AI case studies, the first exploratory survey was con- ducted in 2016-2017, addressing the first research question. The survey was con- ducted globally, online, utilizing a database of executives and senior managers provided by an international market research firm. The second survey was con- ducted in 2017-2018, addressing RQs 2 to 4. The second survey used a similar methodology. It was conducted globally, online, and the same market research firm provided samples of executive respondents. Sampling was guided by the following: • Global coverage: firms from key countries (in terms of economy/GDP) in the Triad.10 • A minimum country quota of n = 50 where possible (with the exception of New Zealand with n = 29 in study 2, this was achieved). • North American Industry Classification System (NAICS) industry sampling: focus on manufacturing (NAICS code 23, 31, 32, 33), information (NAICS code 51), transportation (NAICS code 48, 49), retail (NAICS
  • 56. code 41, 42, 44, 45), financial services (NAICS code 52), healthcare (NAICS code 62). • Minimum industry quota: n = 50. • Focus on medium to large firms in terms of revenue and employees. • Senior respondents: focus on key informants of firms at C- or VP-level or above. In order to assess response bias, we followed the logic of Armstrong and Overton11 and found no statistically significant differences at the .05 level in regard to any of the variables that we are reporting below. In terms of common method bias, we applied the marker variable approach and found no significant effects.12 Overall, we are confident that our findings can be generalized to medium to large Triad enterprises in the industries sampled and that our results are not the result of the instrument/sample used in our analysis. Sample details are provided in the endnote.13 AI in Business To what extent are firms already using AI in their business? In 2016-2017, the time of the first survey, AI applications had already diffused quite broadly, with only 15% of firms not yet having any AI plans and 20%
  • 57. already having delivered results (see Figure 2). Demystifying AI: What Digital Transformation Leaders Can Teach You 115 Interestingly, the application of AI was typically an integral part of a firm’s digital transformation project. With the exception of isolated experimentation with specific AI techniques such as deep learning, AI was not used in isolation, but as one technological element of several technologies aimed at enhancing a firm’s present and future business.14 It emerged that digital transformation is often the context for AI projects, such as call center transformation using advanced analyt- ics and AI or transforming operations using Internet of things (IoT), advanced analytics, and AI. The Business Impact of AI In the second survey, which was executed in 2017-2018, we built on the insights from the first survey and explored the role of AI in firms more deeply. What are the anticipated perceived business impacts of AI? AI can impact the internal operations of a firm, its offerings (in the form of smart products and services15), and how it interacts with its customers. The survey data largely con-
  • 58. firm this (Figure 3). The surveyed executives foresee AI to impact their firms’ offerings. More specifically, they foresee AI to impact the creation of smart ser- vices, to automate operations and manufacturing, to support decision-making and knowledge management, and to automate customer interfaces. Interestingly, the strength of the anticipated impacts does not vary too much (average range: 3.7-3.8 on a 5-point scale). This we interpret as the typical fairly undifferentiated perception by businesses of a new technology prior to wider and deeper diffusion and the emergence of standard business cases and applications. Despite the largely undifferentiated perception of AI’s business impact— the perception of AI business impacts was also largely similar across countries and industries—some differences emerged. The impact on smart services is more Figure 3. Anticipated DX/AI impacts on business (study 2).Note: n = 1,218 firms, worldwide, 2017-2018: “In terms of business impact of AI, to what extent will AI make an impact in each of following areas?” Note: AI = artificial intelligence. *Knowledge management refers to decision-making and knowledge management support. CALIFORNIA MANAGEMENT REVIEW 61(4) 116
  • 59. pronounced in financial services and less in healthcare, and the impact on manu- facturing is more pronounced in the manufacturing sector. However, these signifi- cant differences only exhibited effects that are rather small.16 While, on average, AI’s anticipated business impact is seen as moderate to high across all the business impact categories investigated, 3% of the ratings anticipate no impact and 21% anticipate a high impact.17 What explains those differences? Of the 10+ factors at the country, industry, firm, and executive (respon- dent) level we examined,18 only digital skills have a strong impact. Firms with stronger digital skills anticipate stronger AI-induced business impacts compared with firms with weaker digital skills. This observation is stable across industries and regions (see Figure 4). These digital skills comprised four, interrelated organizational capabilities: • Strategic capabilities: digital strategy and digital business development skills. • Technology capabilities: skills in new digital technologies such as AI or IoT. • Data capabilities: data science skills. • Security capabilities: cybersecurity skills.
  • 60. These results suggest that, just like with other technological innovations in the past, to realize the potential of the new digital technology, AI requires specific organizational capabilities as the firm and the new technology align for best appli- cation and impact.19 AI requires new information technology (IT) skills that are both AI-specific, such as machine learning skills, and generic, such as understand- ing of modern programming languages (e.g., Python), application development techniques (e.g., agile software development), and modern IT architecture skills (e.g., edge computing).20 In addition, data management and analytical skills are required. AI thrives on massive amounts of data requiring the existence of digital data, its management, and its analysis and synthesis. Given that most data are network generated (e.g., websites, sensor data from IoT devices), security skills— generic as well as AI-empowered—become vital to ensure access rights, intrusion detection, and data integrity.21 Last, these skills have to be embedded in a coher- ent and suitable strategic framework to ensure a guided implementation and wider organizational alignment and support.22 We find that firms that have already implemented and delivered business outcomes through three or more digital transformation projects (stage 4; see Figure 2) exhibit particularly strong digital capabilities.23
  • 61. These firms we label digital transformation leaders (DX leaders for short); this group consists of about 8% of all firms surveyed. Digital Transformation Leader Analysis What makes these digital transformation (DX) leaders different? In order to find out, we compared them with those firms in our sample that either had Demystifying AI: What Digital Transformation Leaders Can Teach You 117 no plans yet or were still in the planning phase (implementation stage 0 and 1; see Figure 2). We term these firms “laggards.” Following this classification, we compared 114 DX leaders with 424 laggards. We examined organizational traits Figure 4. Anticipated business impact of AI and firms’ digital skills (a) across industry (study 2)a and (b) across regions (study 2)a. Note: AI = artificial intelligence. aAnticipated business impact and digital skills based on separate summated scales, combining the individual impact and skills items. CALIFORNIA MANAGEMENT REVIEW 61(4) 118
  • 62. in the area of strategy, leadership, data management, agility, organizational pro- cesses, and innovation, as well as country-, industry-, and firm- level factors. The DX leaders we identify came from across all countries surveyed and are as prevalent among traditional firms as among digital natives.24 They also did not differ significantly with regard to the reported duration from project start to busi- ness impact.25 The weak industry differences we observe are a reflection of the different extent of digital transformation initiatives in the industries surveyed, with financial services leading and healthcare lagging.26 With regard to traditional organizational measures (such as size in revenue or the number of employees), DX leaders tend to be larger. They report higher revenues and more employees, but these differences are not very pronounced.27 Besides their noted stronger digi- tal capabilities, we identify organizatio nal characteristics where these leaders excelled. Organizational Characteristics When compared with laggards, DX leaders differ significantly and strongly28 in seven organizational traits. In order of size of the difference (effect size) between the two groups, these were integrated data management, CEO pri-
  • 63. ority, security strategy, digital processes, digital strategy, agility, and open innova- tion ecosystem. Integrated data management, the most pronounced difference between lead- ers and laggards, refers to the organizational capability of managing customer and organizational data in a holistic and integrated fashion, avoiding data silos and incompatible data formats. This aspect goes hand in hand with AI’s dependence on data. CEO priority refers to a firm’s leader prioritizing and leading the firm’s digital transformation efforts, which include the application of advanced digital technologies such as AI. An organization-wide security strategy refers to the defini- tion and execution of a cybersecurity strategy across the whole organization. Given the importance of data, a strategic approach to data security—including the management of access rights, intrusion detection, and disaster recovery mecha- nisms—is critical. Digital processes refer to the digitalization of a firm’s core pro- cesses such as sourcing, production, performance reviews, or travel management and expense claims. Digital processes are often the outcome of digital transforma- tion projects. Organization-wide digital strategy refers to the development and exe- cution of a strategic approach to digital transformation, an approach that is contrasted to unplanned or tactical approaches. Organizational agility refers to a
  • 64. firm’s ability to rapidly and flexibly respond to customers’ needs, adapt produc- tion/service delivery to demand fluctuations, and implement decisions in the face of market changes. Agile organizations continuously search for ways to reinvent or redesign their organization and they can do so in a fast and flexible manner as they learn and adapt in the process. Innovation ecosystem refers to the establishment of an open ecosystem for innovation, beyond the boundaries of the firm. Such ecosystems tie into the resources, capabilities, and strength of a firm’s network of business relationships, such as ties with suppliers, alliance partners, and custom- ers (Figure 5). Demystifying AI: What Digital Transformation Leaders Can Teach You 119 Business Impact The seven organizational aspects enable the leaders to achieve much greater business impacts compared with laggards. Leaders report significantly stronger actual business impacts in their projects compared with laggards. We find significantly higher levels of impact on transformations of existing business models, improvements in operational efficiency, increase in revenue, strengthen- ing of offerings’ competitiveness, and customer experience
  • 65. enhancements. All of the observed differences are strong (see Figure 6). This moves beyond the mere anticipation of business impact (Figure 3) to managers’ perception of real busi- ness impacts actually achieved. Challenges Reflecting the importance of digital skills, the main challenge for all firms is lack of skilled staff and knowledge in digital technologies, which was men- tioned as an implementation challenge by more than half of the firms combined. Lack of organizational agility, internal resistance to change, security risks, lack of leadership and sufficient funding, as well as the challenge of integrating new digital technology with existing technology were stated as challenges by about a quarter of the firms each. Unavailability of suitable technology partners and Figure 5. Organizational characteristics of DX leaders (study 2). Note: n = 538 firms (114 leaders, 424 laggards), worldwide, 2017-2018: “To what extent do you agree with the following statements?” (from 1 = strongly agree to 5 = strongly disagree; statements randomly rotated): (1) Digital transformation is the top priority of our CEO; (2) We are executing an organization-wide digital transformation strategy; (3) We have achieved organizational agility; (4) We have established an open ecosys- tem for innovation; (5) We have digital business processes; (6) We are managing customer and organizational
  • 66. data in an integrated manner; (7) We are executing an organization-wide cybersecurity strategy. DX = digital transformation. CALIFORNIA MANAGEMENT REVIEW 61(4) 120 unstable technology are mentioned as challenges by 19% and 13% of firms, respectively (Figure 7). Contrary to our expectations, we uncover few differences in terms of chal- lenges perceived by leaders versus laggards. Only organizational agility, security risks, and lack of leadership were challenges that the leaders did perceive as less of a challenge in their AI projects. However, the effect of these differences was fairly small.29 We interpret this rather surprising finding as follows. Although the barri- ers or challenges that firms perceive are similar, the DX leaders have more experi- ence and a stronger resource base to overcome them. This becomes most obvious when looking at the three challenges the DX leaders perceived as significantly less of a challenge and comparing those with challenges that are perceived similar. For example, lack of leadership: DX leaders stated lack of leadership significantly less often as an implementation barrier compared with laggards. Given that DX lead- ers had significantly more CEOs prioritizing digital transformation, perceptions of
  • 67. lack of leadership support should be lower. On the contrary, both DX leaders and laggards perceive lack of skilled staff as a key barrier. This is despite the finding that DX leaders have a stronger digital skills resource base. Taken together, this points to the view that perceived challenges are similar, but that in some cases (e.g., lack of leadership), the DX leaders have already developed to a degree that the challenges are less of an actual implementation success barrier compared with the laggards. Figure 6. DX/AI business impact (study 2). Note: n = 538 firms (114 leaders, 424 laggards), worldwide, 2017-2018: “To what extent have you delivered outcomes specified in each of following statements?” (from 1 = not at all to 5 = to a great extent; statements randomly rotated): (1) Increase in revenue; (2) Improvement in customer experience; (3) Strengthening of competitiveness of products or services; (4) Efficiency improvements; (5) Improvement of business agility; (6) Transformation of business models. AI = artificial intelligence. Demystifying AI: What Digital Transformation Leaders Can Teach You 121 Success Factors In contrast to the implementation challenges, which were similar across the firms surveyed, leaders differed compared with laggards in
  • 68. terms of the importance they attributed to factors contributing to digital transformation out- come success. The following eight success factors turn out to be significantly dif- ferent among the AI leaders: organizational agility, engagement of skilled staff, leadership, support from technology partners, investment, culture, alignment of new digital technologies with existing IT, and learning from failed projects. The biggest difference between the leaders and the laggards is organiza- tional agility. Leaders attribute much more importance to organizational agility as a factor of project success. Second, leaders have more engaged staff with the required digital skills and leadership support. Support from technology partners, sufficient funding, a supportive culture, alignment of new digital technologies with a firm’s existing technology, and learning from failure were also rated much higher as contributing factors of project success by the leaders as compared with the laggards (Figure 8). Figure 7. DX/AI implementation challenges (study 2). Note: n = 3,557 answers by 1,218 firms, worldwide, 2017-2018: “Which of the following statements describes your key challenges? Please select up to three” (categories randomly rotated): (1) Lack of skilled staff**; (2) Lack of knowledge of digital technology**; (3) Lack of organizational agility; (4) Lack of leadership; (5) Fear of
  • 69. change or internal resistance; (6) Unavailability of a right technology partner; (7) Lack of funds; (8) Integrat- ing digital technologies with existing IT; (9) Cybersecurity risks; (10) Adoption of digital technology too early, before it is robust and stable; (11) Others. AI = artificial intelligence; IT = information technology. **Combined in the figure. CALIFORNIA MANAGEMENT REVIEW 61(4) 122 DIGITAL: Guidelines for Successful AI Applications The present research departed from the tension between AI success sto- ries on one hand and failures and frustrations on the other hand. Based on our research, we now identify seven areas for managerial action and implementa- tion. We use the acronym “D-I-G-I-T-A-L” to describe our implications, where “D” stands for data, “I” for intelligence, “G” for grounded, “I” for integral, “T” for teaming, “A” for agile, and “L” for leadership. We explore these areas below. The more DIGITAL a company is, the higher the likelihood that their digital transformation–embedded AI projects will succeed. For easier comprehension, Figure 9 illustrates the links between the empirical evidence presented and the elements of the DIGITAL implementation framework. Some of the conclusions that we draw from our analyses, obviously, refer to other change projects within
  • 70. the digital transformation of companies as well. With DIGITAL, we also give credit to this notion and remind that implementation of AI, in its present con- dition, is typically linked to digital transformation of corporations in general. For each of the elements of DIGITAL, we provide managers with a few action- inducing discovery questions in order to guide their AI applications. Answering any of those questions with a clear “No” should alert managers to act accord- ingly (see Figure 9). Figure 8. DX/AI success factors (study 2). Note: n = 538 firms (114 leaders, 424 laggards), worldwide, 2017-2018: “To what extent have the following factors contributed to the overall outcomes you reported?” (from 1 = not at all to 5 = to a great extent; state- ments randomly rotated): (1) Engagement of skilled staff; (2) Having the right organization and processes; (3) Leadership by management; (4) Development of an enabling culture; (5) Support from technology partners; (6) Investment by the business; (7) Alignment of new digital technologies with existing IT; (8) Learning from failed projects. AI = artificial intelligence; IT = information technology. Demystifying AI: What Digital Transformation Leaders Can Teach You 123 Data
  • 71. Just like Dr. Mayol alluded to in his opening quote, the fundamental basis for AI success is data. AI requires data, digital data, and in high quality. The AI machine learning technique of deep learning is particularly data hungry. For Figure 9. Proposed AI implementation success framework, empirical evidence, and action-inducing discovery questions. Note: AI = artificial intelligence. CALIFORNIA MANAGEMENT REVIEW 61(4) 124 example, Google needed some 10 million images to teach its Google brain sys- tem to identify human faces, human bodies, and cats.30 In a more recent appli- cation, the number of images used increased to 300 million images.31 Without data available for training, AI cannot create value for firms, and without skills to acquire, manage, and analyze the data, valuable and actionable insights cannot be generated. Our research confirms this. Firms with strong data capabilities are expecting to derive more value from AI, and our DX leader analysis showed that integrated data management practices and digital processes, which generate digi- tal data, separate the leaders from the laggards significantly and strongly.
  • 72. Managers are, therefore, advised to start with a data inventory check before embarking on AI projects. The data inventory check should address questions such as “Do we own or have access to data that are relevant to analytically solve the business problem we are addressing?” “Are the data available in the right digi- tal format?” “Are the data sets sufficiently large to be efficient and effective?” “Are the data sufficiently complete, consistent, accurate, and timely?” Be Intelligent Data are the necessary foundation for AI success, but data alone are not sufficient. We identified lack of skilled staff and knowledge in digital technolo- gies as the top AI implementation challenge and engaged skilled staff as one of the key AI implementation success factors. Therefore, managers need to develop digital intelligence in the form of suitable human skills within their organization. This intelligence extends beyond the necessary data-related data science skills to include the strategic-, technological-, and security-related capabilities that we discussed earlier. In fact, AI requires organizations to develop human intel- ligence. How shall managers develop this intelligence, especially considering that AI-specific technical talent is scarce?32 First, it is important to realize that AI success is not just a
  • 73. function of tech- nical skills such as data science capabilities and skills in new digital technologies and cybersecurity. Managerial skills in the form of strategic capabilities are vital. Our results showed that firms with stronger capabilities in the area of digital strat- egies and digital business development skills are expecting to derive more value from AI compared with firms with a weaker skills base. At the heart of these managerial skills is awareness and understanding. This implies awareness of the possibilities and requirements of AI and related new digital technologies and an understanding of how to best leverage this technology in the idiosyncratic context of the firm. Questions such as “How can AI help defend, grow, or transform our business?” or “How can AI improve operational efficiencies?” are indicative. Answering such questions does not require an in-depth how-to technical under- standing of AI, but does require managerial curiosity and interest paired with firm, customer, and industry knowledge. Second, the required technical AI skills need to be attained. In principle, managers have two options. Develop technical AI skills internally or acquire these skills from outside the firm. Interestingly, we found no difference in how leaders
  • 74. Demystifying AI: What Digital Transformation Leaders Can Teach You 125 approached skills scarcity compared with laggards.33 We recommend a dual- sourcing strategy. Managers should develop existing internal skills and source external talent at the same time in order to build the necessary technical skill base to ensure efficient and effective application of AI technologies. Finally, digital intelligence has to do with patience. AI is not an instant panacea. Our research unearthed rather lengthy, multiyear processes from project start to impactful execution. Just like the successful case that opened this article, AI projects are usually not deterministic from start to finish, but emerge as the project participants learn, and the system provides feedback. This also distinguishes AI projects from many other IT projects, where the end goal is the successful implementation and use of a system. Especially when embark- ing on an AI project for the first time, managers should allow the AI project team to experiment and provide them with a generous timeline to deliver results and sufficient funding, one of the main barriers we identified. This includes allowing for a “failure culture” as the team learns. DX leaders excelled at learning from failure and it helped them to reap more benefits from their AI projects.
  • 75. In summary, managers are advised to start with an internal resources check before embarking on AI projects. This check should address questions such as “Do we have a digital strategy in place?” “Do we have the managerial and technical skills required to support successful digital transformation with AI? If not, how do we develop or acquire these skills?” “Are we willing and able to tolerate investing in an emerging rather than deterministic AI digital transformation journey, includ- ing accepting failure?” Be Grounded Following the insights derived from more than 7,000 projects world- wide, we conclude that firms are mainly applying the new digital technology to improve their existing business(es) (see Figure 3). The reported business impacts of the DX leaders also suggest a grounded approach with impacts such as improving the existing offering, increasing revenue, or enhancing operational efficiency (see Figure 6). Managers embarking on AI projects should take this insight as suggestive of a rather grounded approach to AI, at least initially. Rather than pursuing high-flying “pie-in-the-sky” projects, firms should “start small” with AI and base the project in their existing core business(es). Our opening case illustrated this: a focused application area and a relatively small
  • 76. project size. The setup allowed for early results, and the project setup was not made overly com- plicated. Again, this also points to the need for using AI for solving concrete busi- ness problems, rather than viewing it as radical innovation and business model disruption from the start—this may happen eventually, but later. AI, just as other technologies, is ultimately not about technology but business opportunities and capabilities. Only when enough experience has been accumulated should firms proceed to more difficult and complex projects involving innovation and new business models. This grounded approach also signifies that adopting AI is like adopting other new technology successfully. Start small, test, learn, and then CALIFORNIA MANAGEMENT REVIEW 61(4) 126 apply more widely. As we noted earlier in this article, this suggests that realizing the potential of AI requires the firm and the new technology to co-align for best application and impact, and it rejects the notion of technological or organiza- tional imperatives.34 Before embarking on AI projects, managers should conduct a reality check in terms of scope and intent of the project(s). This check should address questions
  • 77. such as “Are we experienced enough and resourced properly for the scope of the project?” “Are we following an incremental, current business focused approach with our DX/AI project(s)?” “Do we have a DX/AI projects roadmap?” Be Integral Successful firm-wide AI implementations require an integral, holistic approach. Being integral comes in six flavors: strategy, processes, data manage- ment, technology alignment, employee engagement, and culture. As soon as AI leaves the experimental, feasibility-testing lab environment and is applied to a real business case, managers should first make sure it is embed- ded in and supportive of the firms’ digital strategy. The existence of a digital strat- egy separated the DX leaders from the laggards and signals the importance of viewing AI in a broader context. A firm’s digital strategy, which, in essence, out- lines and documents how a firm wants to achieve its strategic objectives with the help of digital technologies—including but not exclusive to AI—channels its activ- ities and provides for a guiding purpose. Executing a digital strategy implies the “digitalization” of a firm’s core processes: from procurement processes to internal operations to customer engagement.35 AI cannot augment analog processes. Managers
  • 78. should ask them- selves how digital their firms’ core processes are. Our DX leaders’ analysis showed that digital processes significantly and strongly distinguished them from laggards. AI requires data. As firms digitize their operations, thereby creating more digital data, the need for an integrated data management approach becomes vital. It is, therefore, not surprising that integrated data management was the number one organizational characteristic differentiating DX leaders from lag- gards, because the mere existence of a lot of data is good but not good enough. Data, even if sufficiently large, complete, consistent, accurate, and timely, are limited if they “live” in isolation and are not connected with other relevant data. Subscribing to the view that firms are essentially consumers, producers, manag- ers, and distributors of information,36 all their data should be connected and integrated to allow for maximum value capture and knowledge generation. To address this challenging task, some innovative firms have recently started to set up so-called data lakes, a centralized repository that allows them to store all their structured and unstructured data and access in a unified way. As firms employ AI for more complex, broader tasks and processes (say, enhancing customer experi- ences), integrated data management becomes more important. Enhancing cus-
  • 79. tomer experiences, for example, requires tapping into data from the firm’s ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), Demystifying AI: What Digital Transformation Leaders Can Teach You 127 CMS (Content Management System), SMM (Social Media Monitoring), and other systems in order to ensure an integral approach to the customer journey. Integrated data management requires technology alignment. Lack of it was one of the key barriers to AI success and successful alignment one of the key suc- cess factors we identified. Technology alignment specifically refers to the integra- tion of new digital technologies, including AI, with a firm’s existing technologies, and it is all about the question of whether the new and the old can “speak” together and “understand” each other in terms of data. For example, can the firm’s legacy system provide the data required for an AI application in a format that it can compute? Managers should ensure that technology alignment is looked after and instruct experts to ensure seamless integration of the old with the new. Last, integral implies managers should ensure employee engagement and a
  • 80. supportive culture. Successful AI thrives with engaged skilled staff and an enabling culture, both of which help to overcome internal resistance and lack of skills and knowledge, two of the key barriers we identified. Engagement is particularly important for the employees that will be impacted by AI. As firms seem to be par- ticularly interested in applying AI in the creation of smart services (see Figure 3), for example, engaging and working with the service frontline employees will be instrumental to the success in smart services. Even though our research supports the augmentation view of AI, managers should be aware that frontline employees might fear displacement and should address this fear proactively. Research on how frontline employees’ roles, responsibilities, and actions are likely to change due to AI-automated customer interfaces is in its infancy, but it is safe to assume that managers who address such inherent concerns proactively are more likely to achieve employee engagement. In conclusion, managers are advised to think about AI as a broad means to support its company-wide digital transformation efforts. To ensure an integral approach, managers should address questions such as “Have our firm’s core busi- ness processes been digitalized?” “Has our firm analyzed what existing/new offer- ings can benefit from DX/AI?” “Has our firm integrated all data into one single
  • 81. data repository?” “Is our firm’s existing IT compatible with the DX/AI technology we plan to adopt?” Be Teaming Our opening case illustrated that going for AI alone is unlikely to lead to success. Just as Dr. Mayol and his colleagues collaborated with a technol- ogy adviser and provider, DX leaders stated support by technology partners as a key success factor, and the unavailability of support by a technology partner was stated as an implementation challenge by nearly 20% of firms. Teaming with one or several technology partners—which include technology generalists such as IBM or Fujitsu, software firms such as Microsoft or SAP, or consultancies such as Accenture or Deloitte37—provides firms with two main advantages. First, it provides them with early access to new technology in a field that is still ill- defined and emergent. Second, it allows them to tap into the economies of scope CALIFORNIA MANAGEMENT REVIEW 61(4) 128 and project experiences that both technology firms and technology consultancies have accumulated over the past years as AI projects have mushroomed. For most firms, AI will, for the foreseeable future, not be an off-the-shelf
  • 82. product. Hence, teaming with technology partners to co-create a tailored AI solution is going to be the main approach for now. The importance of teaming extends beyond the confines of working with technology partners and includes a firm’s business ecosystem,38 which includes suppliers, competitors, customers, and alliance partners from different industries. Having established an open innovation ecosystem was one of the organizational characteristics that set the DX leaders apart from the laggards. Open innovation ecosystems are a means to develop innovative offerings (products and services) beyond the internal capacity and capabilities of the firms. This approach recog- nizes that great talent often resides outside of a firm. Given the discussed AI tech- nical skills scarcity, an open innovation ecosystems approach is particularly fruitful in the case of AI, and the DX leaders demonstrated this. Having established an Open Innovation Ecosystem was one of their defining characteristics. Generally, managers have two options: establishing their own ecosystem39 or joining an existing one.40 Often, as was the case with Dr. Mayol and his colleagues, the route to take is shaped by the nature of the collaboration with the technology partner(s). Managers are advised to think about AI as an opportunity to partner and
  • 83. develop powerful ecosystems. To understand the teaming options, managers should address questions such as “Does our firm know with whom to partner in support of our DX/AI success?” “Does our firm know with whom competitors partner in their DX/AI projects?” “Did our firm develop or join an ecosystem to enhance its offerings?” Be Agile Organizational agility is both a key barrier and a key success factor accord- ing to our empirical analyses. Lack of it was the second most important AI imple- mentation challenge, and great levels of organizational agility was the number one AI success factor. We found agility to also be one of the main business impacts of AI. Agility is, therefore, both a central AI success antecedent as well as an outcome of successful AI implementations, thereby reinforcing its importance as an antecedent. How can managers foster organizational agility? Organizational agility research suggests that a firm’s ability to sense change and to respond read- ily to it by reconfiguring its resources, processes, and strategies is at the core of organizational agility.41 In the context of AI projects, this relates to flexibility in the way the project is approached and managed throughout its life cycle, as AI projects tend to be emergent rather than determinis tic, as we noted earlier (cf.
  • 84. Be Intelligent). Managers are advised to assess their company’s agility in a realistic manner and implement corrective actions if needed. The following questions, guided by the logic of Singh et al.,42 are instructive: “Compared to our competition, how quickly and frequently are we adapting our processes and offerings to stay Demystifying AI: What Digital Transformation Leaders Can Teach You 129 competitive?” “Compared to our competition, how flexible are we to accommo- dating small, medium, and large changes to our processes and offerings?” Lead Managers should lead and actively endorse the firm’s AI project(s), just like Dr. Mayol at the Carlos Clinical Hospital in Madrid, and not relegate leader- ship to the project management level. We identified executive leadership as a key success factor and lack of leadership support as a key barrier. The DX leaders we analyzed demonstrated this fact forcefully. One of their defining organiza- tional characteristics was a CEO who prioritized the firm’s digitalization efforts, including AI and other advanced digital technologies.
  • 85. If data are the foundation for impactful AI, leadership provides the trans- formational energy for firms to be DIGITAL and, as a consequence, successful with AI. Notably, AI has several similarities with other technologies to be implemented in firms, and some of the aspects of our DIGITAL framework would relate to other change management projects as well. However, the broad nature of AI (requiring data, analyses, interdisciplinary teams) makes for some specific requirements, such as data, agility, and the teaming aspect. Managers are advised to reflect honestly, “Is our executive team and mid- dle management comfortable and supportive of the changes that DX/AI will bring to our firm?” “Is our executive team and middle management actively endorsing and continuously communicating the status and progress of our DX/AI activities to all stakeholders?” Conclusion and Outlook AI certainly holds a lot of promise but it is not a panacea. In order to reap its benefits, we developed DIGITAL as a guideline for AI success grounded in the empirical insights of close to 7,000 DX projects that involved new digital technol- ogies such as AI. The basic approach of this article was to learn from today’s DX leader how to become the AI leader of tomorrow. At the same
  • 86. time, in line with the title of this article, we believe we can demystify a few aspects of AI. The results of this study imply that, in many ways, AI is similar to other technologies companies adopt and implement. It certainly is typically deployed in digital transformation projects, and, as such, shares many similarities with other digital projects. At the same time, the focus on self-learning projects and long-run scaling comes with several interesting findings, such as the focus being integral, teaming, and agile. In contrast to press reports and also some academic papers, our approach to AI is a contemporary and realistic one. Before visions of “AI tak- ing over everything” will become true, “realistic AI” will take place for a long time. It is and will be a competitive advantage to be quick and effective in AI, and our DIGITAL framework and the associated questions to managers should help overcome the barriers, but also some of the illusions, so that “realistic AI” will become real. CALIFORNIA MANAGEMENT REVIEW 61(4) 130 A question that we also addressed in the survey was the future role of humans in AI projects, which we do not report here in detail due to the contro-
  • 87. versial and nonconclusive nature of the responses, and the vastness of the topic. But overall, it is interesting that firms are positive about AI as a technology and the role of humans. First, they expect that AI and humans will work together in the future, rather than working against each other or replacing humans, for at least quite some time. Furthermore, managers assume that we will even see a human premium emerging in the future, in the sense that people will be paying more to get personal, human-to-human services rather than AI technology– mediated services. Interestingly, leaders and laggards are united in this view and showed no significant differences. Acknowledgments The authors wish to thank Fujitsu, Corporate Executive Officer and former Chief Marketing Officer Yoshiteru Yamada, and Manager Noriaki Tanaka for support- ing the research project. Author Biographies Jürgen Kai-Uwe Brock is the former CMO of Fujitsu Americas, currently work- ing as a senior assignee at Fujitsu’s HQ in Tokyo, Japan (email: [email protected] fujitsu.com). Florian von Wangenheim is Professor of Technology Marketing, Department of