bring a simple and concise summary of what the cognitive technologies enabling “Digital Labor” mean in order to raise the awareness level amongst the non technical people that care about the technology impacts on business, economy and society.

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ARTIFICIAL INTELLIGENCE, COGNITIVE
TECHNOLOGIES AND DIGITAL LABOR
impact on business, economy and society
by Emmanuel Gillain, 2017
Disclaimer. This whitepaper was prepared and accomplished by Emmanuel Gillain as a personal
initiative. The views, opinions and conclusions expressed in this whitepaper are those of the author.
They are not given or endorsed by his employer and do not necessarily represent the view of his
employer.

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ARTIFICIAL INTELLIGENCE, COGNITIVE
TECHNOLOGIES AND DIGITAL LABOR
Impact on business, economy and society
by Emmanuel Gillain © 2017
introduction
The use of the “Artificial Intelligence” (AI) technologies will become vital for companies to survive
in the market and the impacts of those technologies will be disruptive for the economy and the
society. “They increasingly challenge human labour, can readily substitute for labour in a wide
range of non-routine cognitive tasks” (Brynjolfsson and McAfee, 2011; MGI, 2013).
I call it a Tsunami: it is coming more strongly and faster than some might believe. Supported by
Moore’s law, combined with Metcalfe’s law of networks, the pace is accelerating, following
exponential laws, one of the main thesis of the book titled “The Second Machine Age: Work,
Progress, and Prosperity in a Time of Brilliant Technologies” (Erik Brynjolfsson and Andrew
McAfee, 2014).
Probably less visible than the automation of “manual work” with robotics, the automation of
knowledge work by cognitive technologies, a branch of AI, will impact skilled and educated
people across all the industries. The movement, which I called “Digital Labor”, is clearly
underway.
“By 2020, it is expected, 60% of the Global 2000 – Forbes’s annual ranking of the top 2000 public
companies - will have doubled productivity by digitally transforming many processes from
human-based to software-based delivery” (IDC Reveals Worldwide Digital Transformation
Predictions, Nov 2015).
Those new technologies present both fantastic opportunities and major socioeconomic risks
which requires strong attention from leaders and governements to ensure the wealth created
doesn’t exacerbate a socioeconomic divide. The starting point of that journey is awareness and
understanding the complexity of the issues. As much has been said already, the whitepaper is a
modest attempt to bring a simple and concise summary of what the cognitive technologies
enabling “Digital Labor” mean in order to raise the awareness level amongst the non technical
people that care about the technology impacts on business, economy and society.
The whitepaper doesn’t pretend to be exhaustive.

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Artificial Intelligence, a pragmatic view
There is actually no common, unique, nor simple definition about “Artificial Intelligence”. The
term itself was first mentioned by Dartmouth College’s John McCarthy in 1955 in a proposal to
university researchers for its summer research project, the famous Dartmouth Summer Research
Project on Artificial Intelligence in 1956.
Brilliant minds, scientists, researches, philosophers, have spent their lifetime to try to answer
that question. Some define it by the tasks : “computer systems able to perform tasks that
normally require human intelligence”, “execute tasks and solve problems in ways normally
attributed to humans”. Professor Yann LeCun (New York University, computer scientist with
contributions in machine learning, computer vision, computational neuroscience, and one of
founding father of the neural networks) defines it for example as “ a set of technologies that
enable machines to accomplish tasks and solve problems, usually handled or solved by human”.
(Les Enjeux de la Recherche en Intelligence Artificielle, “inaugural lecture” in 2016 ). Other define
it by the behavior : “systems that simulate, imitate or exhibit intelligent behavior”. Marvin Minsky
(MIT cognitive scientist concerned largely with research of AI, co-founder of the MIT's AI
laboratory) defines AI as “the science of making machines do things that would require
intelligence if done by men.”
So, most definitions refer to some ”intelligence” or compares it with human intelligence … So,
what is “human intelligence” ? is it the ability to recognize, learn, infer, solve problems, take
rational decision, plan or to have emotions, feel empathy ? Probably all of this together. An
interesting thing is that once a computer can do something (like calculating), even better than
humans, some do not consider it anymore to be a mark of “intelligence” – Those, of course,
always win.
More pragmatically and for sake of simplicity, this whitepaper looks at it from the perspective of
the technology impacts. We can roughly identify 2 major trends, each supported by different
research streams :
- the robotics trends, which mainly have impacts on the manual work. The systems perform
according to learned reflexes and rather leverages a “sensory-motor” coupling approach
(following Rodney Brooks’s drive. Mr Brooks is Professor of Robotics at the MIT and former
director of the MIT AI Laboratory). To quote Professor Bersini, the “AI that learns and
performs unconsciously”
- the cognitive trends, which mainly have impacts on the knowledge work. As they will be
detailed later on, systems that try to explicitely represent the world, for example with explicit
representation states (in the mathematical sense), and are more “deliberate” in their
actions. The “Conscious AI”, quoting again Professor Bersini.
There are obviously “hybrid” systems which combine both aspects , such as “embodied cognitive
science” in cognitive robotics. This whitepaper focuses on the impacts of the cognitive systems.

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Cognitive Systems and their applications
Cognitive systems, as a branch of AI, can broadly be defined as systems that understand, learn,
reason and interact with humans (or devices). They can learn and adapt as information changes,
and as goals and requirements evolve. They may even deal with ambiguity and tolerate
unpredictability. They act according to the context, an important element for behaving
“intelligently”. Those systems are complex integration and interplay of many different AI and
cognitive technologies , each of which comprises multiple branches of research and
development: planning and problem solving, knowledge & reasoning , Natural Language
Processing, machine learning, etc to name only a few. Machine Learning is often an underlying
or complementary technology supporting other ones : deep learning supporting voice and visual
recognition, natural language processing, identifying the features of an analytical model, support
the learning aspects of a knowledge based system to compose a solution to a problem,…
When we look at their use for executing “Digital Labor” tasks , cognitive systems come in 2 main
categories of applications :
(1) the Cognitive Wave, with a focus on mastering Knowledge and Insights, ”Virtual
Employees”, which support decision, or even take decision by themselves. The term
“Virtual Employee”, sometimes called “Virtual Assistant” should not be limited to “bots”
with “human interaction” for simple search tasks and interaction with other background
systems, but should really be understood as much more complete systems which not only
understand and search, but also learn, reason, manipulate knowledge and resolve.
(2) the Autonomics Wave with a focus to make a process “autonomous”, not only
“automate” the process (whether IT or business) but also make the system self-managing,
aware and adaptive, without input from human, using a.o. machine learning algorithms.
Those features make Autonomics different and more advanced than usual Robotic
Process Automation (RPA), designed to execute specifically defined tasks (multi-scripted
for fixed repeatable tasks).
The purpose is then to go from smart decisions in a process, to an action on the environment,
either indirectly (human action) or directly (machine to machine action).
The source to build the machine knowledge needed for those applications can come either
(1) from the data, transformed into information, then knowledge when put in context, or
(2) from “human to machine” interactions (Natural Language Processing),
(3) or even better, from a mix to get the best of both worlds
The picture below depicts a very high level conceptual view :

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Impacts on business
The impacts of the cognitive technologies applications on business are clear : they are cheaper,
less prone to logical errors and faster than humans. And they are consistent.
Those systems reduce cost. Cognitive agents are much cheaper than human labor costs, they are
faster than human employees and can function 24/7 . They present marginal incremental cost
for higher volume. “Robotic Automation tools are up to 65% less expensive than offshore-based
full-time employees” (Everest Group’s Finance and Accounting Outsourcing Annual Report 2014).
A telecom company replaced 45 offshore employees costing $1.35m a year, by 10 software
robots, costing $100,000, bringing an annual savings of $1.25m. Those savings were used to
hire 12 new highly skilled people to do more innovative work locally at their HQ.
They also improve quality and consistency . Less prone to logical errors, they reduce the
possibility of errors. Their decisions are not influenced by emotions or behaviors which affect
human performances. As they diligently perform according to the given rules & policies, they
help ensure compliance : they can make consistent, fully documented decisions that always
comply with the policy set. Their transparency is debatable : it will not only depend on the
learning system applied (remember the differences between learning systems, some of which
can take decisions based on “non explicit or “unconscious learning” approaches) but also on our
ability to follow a raising model complexity.
DATA INFORMATION
MACHINE
KNOWLEDGE
COGNITIVE
HUMAN
KNOWLEDGE
Virtual Assistant
Insight
Decision support
knowledge
tasksunderstand
learn
adapt
reason & infer
in context
ACTION
descriptive
predictive static Process
Automate
Aware
Adapt
AUTONOMICS

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Being much faster, they reduce the process cycles. Faster to adapt and accommodate, they can
scale on demand in high volumes, up or down, by leveraging the elasticity and granularity of the
cloud technologies, all of which brings agility.
If we look at autonomics applications for example, companies organization being structured on
workflows and processes “Anything that is a process can and will be run by AI “ (Hans-Christian
Boos, CEO, Arago, July 2016)
They can also cope with volumes of data that humans will never be able to cope with, bringing
both scaleability (do much more of the same) and improvement in the quality of the decisions
(do better, with more features and elements to base a decision upon. A classical example is the
detection of anomalies, relevant characteristics, patterns, or changes in medical images: AI
based system have far better results than those of top human radiologists. Comparing the
diagnosis of Computerized Tomography lung scan to detect cancerous growth, some AI based
systems report no false negatives compared with a 7% rate for top human radiologists, and false
positive rate of 47% compared with 66% for human radiologists.
It is also worth noting that you can have those benefits (ie, cost reduction, quality and consistency
improvement, speed) all together, at the same time : you aren’t limited anymore by the old
operational dilemna of making investments based on a forced choice between 1 or the other
optimization factors. There isn’t usually any trade-off needed anymore.
NHS Shared Business Service provides services to the U.K. National Health Service (NHS).
Delivered by a team of business professionals, NHS SBS offerings include Finance & Accounting,
Employment Services, Procurement and Primary Care Services. Autonomics has allowed them to
reduce their staff needs from over 150 to having just 5 staff on standby to deal with any issues :
96% improvement in staff productivity, 80% reduction in penalties by improved SLA, 30-40%
savings in reconciliation processes with an accurate and consistent month end close completed
for all trusts every month.
Last but not least, such cognitive systems help companies master Knowledge, with a capital K, as
a strategic asset. As some famous strategists said : “The only sustainable competitive advantage
is your organization’s ability to learn faster than the competition” (Peter Senge, Sr Lecturer and
Director of the Centre for Organisational Learning at the MIT), “The ability to learn faster than
your competitors may be the only sustainable competitive advantage” A. De Geus (ex-Shell Head
of Strategic Planning Group).
In a competitive market, companies won’t be able to survive if they don’t embrace those
technologies. Still mostly applicable to specific types of tasks and a given context (“weak or
narrow AI”, although “narrow” becomes larger) many of those applications works well enough
today to provide strong return on investment – and it will soon become impossible to compete
without.

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For example, autonomics solutions on the market today (leveraging AI and machine learning
technologies rather than simple script based automation) achieves automation rates in IT
processes of 70 to 80% (such as incident, problem, change management). Gartner noted that by
2017, managed services offerings leveraging autonomics and cognitive platforms will
permanently remove head count, driving a 60% reduction in the cost of services (Gartner,
Predicts 2014: Business and IT Services Are Facing the End of Outsourcing as We Know It).
“By 2020, it is expected, 60% of the G2000 will have doubled productivity by digitally
transforming many processes from human-based to software-based delivery” (IDC Reveals
Worldwide Digital Transformation Predictions, Nov 2015)
Looking at another category of applications, companies have developed software that can
accelerate legal work, not only by automating searches using natural language, questions and
concepts rather than keywords, but also by assisting the lawyers to prepare their cases, and by
predicting the outcome of court cases — some with more than 90 % accuracy.
The same applies to all the areas where managing knowledge and making logical inference is a
substantial part of the job – (doctors, accounting, human resources,…).
Economy and society
From an economic perspective, the Automation of Knowledge Work might have the biggest
potential economic impact of all the disruptive technologies. For example, looking at the
disruptive technologies ranked by their economic impact, McKinsey Global Institute states
indeed that automation of knowledge work is going to have one of the largest economic impacts
among the most disruptive technologies over the next 10 years, impacting the $9 trillion dollars
that makes up 27% of global employment costs that go to knowledge workers (Disruptive
technologies: Advances that will transform life, business, and the global economy, May 2013).
Other studies and reports from the World Economic Forum come to similar conclusions.

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Cognitive technologies trigger far more than just incremental efficiencies in business processes
and they are coming at an un-precedented speed. Indeed, being digital, supported by software
and algorithms deployed on hyper-scale cloud infrastructures, shared & leveraged across the
globe, this wave of technologies will scale much faster than the previous automations that have
driven our economy over the last few decades.
Which impacts will it have on economy and society ?
That is a heavily debated topic. Amongst the AI community, there is a broad agreement and
consensus that, the AI revolution will lead to a profound impact on the knowledge workers,
irreversibly transforming employment, economy and the job market.
“The AI revolution is doing to white collar jobs what robotics did to blue collar jobs» will lead to a
restructuring of the economy that is more profound and far-reaching than the transition from the
agricultural to the industrial age” (Erik Brynjolfsson, Andrew McAfee, Race against the machine,
2011) ”
Loss of jobs in the short-run, churning of the job market, changes in the skills demanded on the
job market. A substantial share of jobs is at risk. The growing popularity of artificial intelligence
technology will likely lead to millions of lost jobs, especially among less-educated and less
flexible knowledge workers.
“By 2030, 90% of the jobs as we know them today will be replaced by smart machines” (Gartner,
sep 2013)” or “The rise of robots will lead to a net loss of over 5 million jobs in 15 major developed
and emerging economies by 2020” (World Economic Forum report, jan 2016).

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According to a new report from the McKinsey Global Institute (“Harnessing automation for a
future that works” Jan 2017), with current technologies, nearly half of all the work we do will be
able to be automated by the year 2055, even if different factors including politics could affect the
term. The question to answer is then about mass job re-deployment, as also stressed by one of
the author, Michael Chui.
At the same time, technology and innovation is essential to improving GDP and productivity
growth which our economy is looking for. Economic growth and created wealth will lead to a
demand for new skills, un-imaginable new occupations, complementary and augmented jobs. AI
technologies especially can re-ignite economic growth, with is a transformative effect on growth,
beyond being only a driver of “total factor productivity” : in his report “Why Artificial Intelligence
is the future of growth” (Accenture, Mark Purdy and Paul Daugherty, Sept 2016) Accenture for
example considers AI as a new factor of production, overcoming the physical limitations of the
traditional labor and capital factors. In association with Frontier Economics, Accenture modeled
the potential impact of AI for 12 developed economies that together generate more than 50
percent of the world’s economic output. “Our results reveal unprecedented opportunities for
value creation. We find that AI has the potential to double annual economic growth rates across
these countries—a powerful remedy for slowing rates in recent year”. “AI has the potential to
boost labor productivity by up to 40 percent in 2035 in the countries we studied”
The question then becomes : how positive the impact will be for the whole society ? Will the
distribution of the created wealth build an inclusive society for all, or accelerate a socioeconomic
divide ?
In the U.S., productivity has continued to grow steeply and innovation has been strong, but
median income and employment have stagnated, what Erik Brynjolfsson and Andrew McAfee
calls the “Great Decoupling” : “Labor’s share of GDP held steady for many decades in America,
but since 2000 it has fallen sharply” (McAfee). In the U.S., GDP and productivity growth have
already been decoupled from the workers salary for more than 15 years and similar trends are
appearing in most developed countries, incl Sweden, Finland, and Germany.

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How will the wealth created be distributed between the factors of production, labor and the
capital, or allocated to other social benefits ? The question remains of course open.
government
The question of the role of the governments comes then naturally : do governments have a role
to play to reconcile the benefits of the technology with its expected toll ? Do governments realize
the magnitude and velocity of the impact and understand the role they might have to play ?
The voices of some government officials start speaking up, more loudly : “We must remake
society in the coming Age of AI (…) the President worries that AI could suppress wages, eliminate
jobs, and create new inequalities. We must also develop new economic and social models that
can ensure these technologies don’t leave people behind (…) President Obama says” (Wired
Online, Dec 2016)
A few months ago, on president Obama’s request and explicit concerns, the White House
released a series of reports on future directions and considerations about AI “Artificial
Intelligence, Automation, and the Economy” (White House, Dec 16). Similar reports were
requested by the British Government to the Office for Science (“Artificial intelligence: an
overview for policy-makers, opportunities and implications for the future of decision making” UK,
Government Office for Science, Nov 2016).
It is no wonder that the key recommendations of the reports are the following ones:
- invest and develop, as an engine for economic growth

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- educate for the jobs of the future, expand the access to education
- aid workers in the transition
- develop policies that create broadly shared prosperity, unemployment benefits,…
Some countries, including Germany, start debating the idea of an unconditional basic income
to compensate for social distortion.
We are living in exciting times, with great opportunities for un-precedented levels of productivity
ahead of us.
What we shall do with those gains is up to us to decide, it all depends on how we manage the
transition towards an AI based society: a healthy society will require companies and policy
makers to anticipate future skills requirements, proper income distribution and shape the future
of those that can’t adapt fast enough.
THANK YOU
To Professor Bersini, Mr Hans-Christian Boos, Mr Andreas Ebert and Mr Bruno Schröder for
their insightful and enriching perspectives.
To my wife, Laurence, for her support while I’ve been preparing and writing those lines in my
spare time.