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© 2015 IBM Corporation
Hamid R. Motahari-Nezhad
IBM Almaden Research Center
San Jose, CA
The Journey to Cognitive Enterprise IT Services:
A Framework for Cognitive Services and Business Processes
Talk at University of New South Wales, Sydney,Australia. Nov. 29, 2016
© 2013 IBM Corporation
Major Technology Trends Impacting Enterprise Business
2
Mobile Social
Cloud
Internet of Things
20162000
© 2013 IBM Corporation
We are here
44 zettabytes
unstructured data
2010 2020
structured data
Data is the world’s new natural resource! (Ginni Rometti, IBM Shareholders Report, 2014)
We are here
Sensors
& Devices
VoIP
Enterprise
Data
Social
Media
5
© 2013 IBM Corporation
Mega Trends: Data, Cloud, and Mobile
4
80%
of the world’s data
today is
unstructured
90%
of the world’s data
was created in the
last two years
1 Trillion
connected devices
generate 2.5
quintillion bytes
data / day
3M+
Apps on leading
App stores
By 2017
The collective computing and storage
capacity of smartphones will surpass all
worldwide servers
48% of enterprises are moving to
the cloud to replace on-premise,
legacy technology today
72% of enterprises have at least
one application running in the
cloud, growing from 57% in 2012
The average enterprise uses 738
cloud services.
© 2013 IBM Corporation
A new computing paradigm is emerging
Tabulating
Systems Era
Programmable Systems Era
Cognitive
Systems Era
© 2013 IBM Corporation
Intelligent Assistance and Machine Learning - Landscape
6
IPSoft’s
Amelia
© 2013 IBM Corporation
Cognitive Era
7
Discovery & Recommendation
Probabilistic
Big Data
Natural Language as the Interface
Intelligent Options
© 2013 IBM Corporation
Towards Computing-At-Scale as the Shared Characteristic of Recent Advances
8
Scalable Computing over
MassiveCommodity Hardware
Building Stronger
Super Computers
Cloud Computing
Crowd Computing
Advanced individual
algorithms
Mass computing applied to AI Complex array of algorithms applied to make
sense of data, and offer cognitive assistance
Big
Data
Individual
MLAlgorithm
Cognitive Computing
© 2013 IBM Corporation
Understands
natural language
and human
communication
Adapts and learns
from user
selections and
responses
Generates and
evaluates
evidence-based
hypothesis
Cognitive System
1
2
3 Cognitive Systems do actively
discover, learn and act
A Cognitive System offers computational capabilities typically based on Natural Language Processing (NLP),
Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that
augment and scale human knowledge and expertise
Watson
© 2013 IBM Corporation
ENTERPRISE SERVICES
10
© 2013 IBM Corporation
Enterprise Services
11
A. Service Provider
• Individual
• Institution
• Public or Private
C. Service Target: The reality to be
transformedor operated on by A,
for the sake of B
• Individuals or people,dimensions of
• Institutions or business and societal organizations,
organizational (role configuration) dimensions of
• Infrastructure/Product/Technology/Environment,
physical dimensions of
• Information or Knowledge,symbolic dimensions
B. Service Customer
• Individual
• Institution
• Public or Private
Forms of
Ownership Relationship
(B on C)
Forms of
Service Relationship
(A & B co-create value)
Forms of
Responsibility Relationship
(A on C)
Forms of
Service Interventions
(A on C, B on C)
Spohrer, J., Maglio, P. P., Bailey, J. & Gruhl, D. (2007). Steps
toward a science of service systems. Computer, 40, 71-77.
From… Gadrey (2002), Pine & Gilmore (1998), Hill (1977)
A B
C
Vargo, S. L. & Lusch, R. F. (2004). Evolving to a new dominant logic for
marketing. Journal of Marketing, 68, 1 – 17.
“Service is the application of competence for
the benefit of another entity.”
Major Types of Service (provider perspective):
• Computational/technology services
• Business/Enterprise services
• People Services
Service Offerings
Definition &
Design
Service Sales
Pursuit
Transition and
Transformation
Service Delivery
& Operation
Lifecycle of
Enterprise (IT)
Services
© 2013 IBM Corporation
Information Technology Service Models
Client Managed
Procure, Own, Install & Manage [CAPEX]
Vendor Managed in the Cloud
On-Demand as a Pay as You Go (PAYG) price [OPEX]
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
Traditional
IT
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
IaaS
Infrastructure
as a Service
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
Managed IaaS
Managed
Infrastructure
as a Service
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
PaaS
Platform
as a Service
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
SaaS
Software
as a Service
Customization, higher costs, slower time to value
Standardization, lower costs, faster time to valueStandardization, lower costs, faster time to value
ClientManaged
VendorManagedintheCloud
Local, Dedicated Public
Workforce Perspective
Staff
Body x Price x
Utilization
Outsource
Body x Price x
Utilization
Digital
Workforce
(Bots + Body) x
Price x Utilization
ClientManaged…….…VendorManaged
© 2013 IBM Corporation
Managed Information Services: From RFP to Transition and Delivery
13
Opportunity Deal																					Deal Deal Checkpoints/															Contract												T&T										Steady-State							Renewal
Identification														Validation										Qualification								Pursuit													QA/Risk	Analysis Delivery
Engagement
Transition	&	
Transformation Renewal
Steady-State	
Delivery
Business	
Development
RFP
Receipt
Week 1
• Team Formation, and
assignment
• Control Matrix Preparation
• Window of opportunity to ask
questions from client
Week 2-x RFP Response
Deadline
Solution &
Approvals in
Place
• Proposal Writing
• Client Presentation Preparation
• RFP Response Items
…
• Detailed SOW Analysis
• Baselines
• SRM
• Solutioning
• Reviews
• Approvals
Control
Matrix
SRM
FRM
Baselines
SOW
Solutioning
• Proposal
• Client Presentation
• Attachments/schedules
Reviews
and
approvals
CSE PM
Transition and
Transformation
Plan
• Contract Writing • Contract Analysis
Service Pursuit Demystified: From RFP to Contract
© 2013 IBM Corporation
Cognitive Enterprise IT Services Framework
14
Prior Deals Service
Offerings
Guidelines,
methodologies
People
Profiles
Lessons
Learned
Service
Delivery Data
Opportunity Deal																					Deal Deal Checkpoints/															Contract												T&T										Steady-State							Renewal
Identification														Validation										Qualification								Pursuit													QA/Risk	Analysis Delivery
Engagement
Transition	&	
Transformation Renewal
Steady-State	
Delivery
Business	
Development
Current Deals
Pipeline
Revenue & Finance
Information
Integrate	and	Make	the	Data	Available	Using	Interfaces	(APIs)	
Deal Information Management
Enable Reusing Deal Artifacts and Sharing Knowledge
Deal Team Analytics Find Expertise and Recommend Them
Deal Competitive Assessment Analyze Competitiveness based on Cost/Price
Deal Win Prediction Analytics to provide deal win prediction, and pipeline ranking
Sales Pipeline Revenue Prediction
Cognitive RFP, Proposal and Contract Analyzing RFPs to extract requirements, and author RFP
Response, and Contract Drafts
Cognitive Solutioning Compose the set of service offerings that meets clients requirements
© 2013 IBM Corporation
COGNITIVE RFP, RESPONSE AND
CONTRACT
15
Hamid R. Motahari Nezhad, Juan M. Cappi, Taiga Nakamura, Mu Qiao: RFPCog: Linguistic-Based Identification and Mapping of Service
Requirements in Request for Proposals (RFPs) to IT Service Solutions. HICSS 2016: 1691-1700
©	2010	IBM	Corporation©	2016	IBM	Corporation
Input	and	problem	statement
§ RFP	Documents	are	textual	documents	sent	by	service	requesters	describing	the	requirements	for	IT	services
– The	requirements	 are	stated	in	natural	language,	with	a	varied	format	in	general
§ RFP	package	contains	10s	or	100s	of	document,	each	with	100s	of	pages	describing	various	aspects	of	existing	IT	
environment	(detail	baseline),	and	future	state	requirements
§ There	are	hundreds	of	requirements	stated	for	each	IT	service	in	each	RFP	that	need	to	be	identified	and	analyzed,	
including	who’s	responsibility	(service	provider	or	customer)	is	to	perform	each
§ Different	clients	organize	the	documents	and	content	differently,	and	use	different	vocabulary	and	terminology	to	
refer	to	IT	services	and	requirements
§ Identification	of	what	constitute	a	requirement	is	very	challenging
– The	structure	(organization)	of	the	document,	the	language	construct	of	sentences	and	also	client	vocabulary	differs
– Natural	language	by	definition	can	be	ambiguous,	documents	have	incomplete	information,	and	expertise	needed	in	interpreting	and
understanding	requirement
©	2010	IBM	Corporation©	2016	IBM	Corporation
Example	IT	Service	Requirements
©	2010	IBM	Corporation©	2016	IBM	Corporation
IT	Service	Requirements	Analysis:	the	need	for	a	meta-model
18
“Service provider shall provide onsite Desktop
Services dispatching resources on 24 hour a day,
7 day a week basis, for Supported Equipment
and Supported Devices at all Client’s Service
Locations, which locations may be modified
from time to time by Client in accordance with
the applicable Change Control Procedure”.
Responsible Party: Service Provider
Verb phrase: shall provide
Topic/Service: OnsiteDesktopServices
SLAneeds: 24 hour a day, 7 day a week
Services for: Supported Equipment and Devices
Locations:All Client’s Service Locations
Duration of service: <Contract term>
©	2010	IBM	Corporation©	2016	IBM	Corporation
Requirements	expressed	in	different	form	and	structures
A	Subsection
Sub-requirements
SP’s	Requirement
Indicators
SP	Requirements
(Extract	these!)
A	Requirement
Title	of	the	table,	potentially	
Service Topic
[Customer]
©	2010	IBM	Corporation©	2016	IBM	Corporation
Research Problems
§ Requirements	identification
– What	statements	constitute	a	requirement	in	RFP	documents?
– Requirements	vs	sub-requirements?
§ Requirements	topic	identification	(IT	services)
– Which	IT	services	they	are	talking	about?
§ Service	Offering	Mapping	- Solutioning
– Which	IT	Service	Offerings	meet	the	client	requirements?
§ Continues	learning	through	Human	feedback
– How	to	manage	human	interactions,	feedback	and	adaptive	learning?
20
©	2010	IBM	Corporation©	2016	IBM	Corporation
From	RFP	(Request	for	Proposal)	to	Proposal:	Methodology	Overview
21
RFP	
Documents	
Processing
Requirements	
Extraction
Provider	
Offering	
Matching
Solution	
Composition
Proposal	
Response	
AutomationPast	RFP	
Response	
Matching
Extracting
requirement
statements
from an RFP
Matching
past RFP
Responses
for Reuse
©	2010	IBM	Corporation©	2016	IBM	Corporation
RFPCog for	Cognitive	RFP	Analysis:	Overview
22
RFP Documents
Contract Documents
Requirements
Identification
Service Catalogs
ITIL
Requirements-
Driven Offerings
Composition
Requirements-driven
Technical Solutions
Composition
Solution
Patterns
Customer
Service Vocabulary
Solutions
Taxonomy
Provider
Offering
Taxonomy
What are client
requirement
statements?
What services
offerings/solutions these
requirements map to?
Requirements	
Topic	
Identification	and	
Grouping
What are in-scope
and out-of-scope
service?
©	2010	IBM	Corporation©	2016	IBM	Corporation
RFP Docs
Structure
Analysis
Pattern-
based
Requirement
Candidate
Identification
NLP-based
Deep
Learning for
Requirement
Identification
Machine
Learning-
Based Topic
Identification
Document
Table
Section
Paragraph
Sentence
Cell
In what sectionof
what document is
the requirement
from?
Boundary
Identification
Requirement
Patterns
How does clients
state requirements?
Patterns:
•( [Subject] + (shall |
must | is required to |
… ) ) + Action Verb +
…
•[Subject] is/are
responsible for …
• Where does a
requirement start
and end?
è What is a
requirement
span?
è Req., and Sub-
req. identification
Recognize
noun (phrases),
verb (phrases), …
Requirement
Features
Apply NLP techniques
for recognition of
Who does what?
Word Dependencies
and Implicit Feature
Identification
Topic/Service
What is the
requirement about?
• Linguistic-based
Requirement Focus
Identification
• Topic-related Feature
Extraction
Use Domain Knowledge
• Provide Service Taxonomy
• Information Technology
Infrastructure Library (ITIL)
• Customer Vocabulary
extracted from Documents
Apply Supervised Learning
using
• Support Vector Machine
• Logistic Regression
RFPCog: Method	Steps	for	Requirements	and	Topic	Identification
©	2010	IBM	Corporation©	2016	IBM	Corporation
Cognitive	Solutioning - Requirements	to	Service	Offerings	Mapping
§ For	a	given	requirement	(or	requirement	group),	the	focus	is	to	identify	service	elements	(at	multiple	level	of	
hierarchies)	that	map	to	the	requirements,	and	their	sub-requirements
– IT	Service	Catalog-aware	Phrase	Matching
– Considering	the	body	text,	concept	hierarchy	through	a	statistically-built	semantic	model	to	identify	matching
§ Novel	Method	for	matching	noun	phrases	in	requirements	and	offerings:	a modified	Longest	Common	Sequence	
(LCS)	term	matcher.	
– One	main	difference	with	other	similarity	metrics	such	Cosine	and	Jaccard is	that	the	LCS	preserves	the	order	of	tokens	in	
matching,	while	other	don’t.	
– Missing	keywords	in	the	two	phrase	are	penalized	based	on	the	importance	of	the	keyword	
24
Based_Similarity_Score=	#LCS	/	Weighted_	
Denominator,	where	Weighted_	Denominator	is	
defined	as	the	weighted	sum	of	the	number	of	
missing	words	in	the	E_Seq.
Final_Similarity_Score = Based_Similarity_Score * (1 – net_distance/C),
in which C is a constant for the maximum length of noun phrases in the population,
and NetDistance is the absolute difference in tokens order difference of the LCS in
NP_Seq and E_Seq (caters for additional terms in between)
“Storage	management	solution”	
and	“management	solution”,	
keywords:	storage,	missing	
words
©	2010	IBM	Corporation©	2016	IBM	Corporation
IT	Requirements	to	Catalog	Mapping	– Interactive	and	Explorative	Visualization
25
©	2010	IBM	Corporation©	2016	IBM	Corporation
Experimental Results	– Requirements	Topic	Identification
26
ML-based Topic Classification Performance (TP Rate)
0.9518 0.8733
0.7587
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SVM Logistic Regression Naïve Bayes
TPRate
Support Vector Machine (SVM) Performance Details
TP Rate FP Rate Precision Recall FMeasure ROC Area Class
0.986 0.232 0.958 0.986 0.972 0.877 F
0.768 0.014 0.908 0.768 0.832 0.877 T
Weighted
Avg.
0.952 0.198 0.951 0.952 0.950 0.877
©	2010	IBM	Corporation©	2016	IBM	Corporation
Related	Work
§ Templated	Information	extraction	from	text
– Steven	Bird,	Ewan	Klein,	and	Edward	Loper,	Natural	Language	Processing	with	Python,	http://www.nltk.org/book/,	visited	July	
2015.
– Ana-Maria	Popescu,	Information	Extraction	from	Unstructured	Web	Text,	PhD	Thesis,	Uni.	Washington,	2007.
§ Extraction	of	requirements	from	textual	software	descriptions	(Concepts,	and	Models	according	to	SVBR	-
Semantic	Business	Vocabulary	and	Rules- ,	and	OPM	- Object-Process	Methodology,	or	LTL	- linear-time	
temporal	logic)
– Ashfa Umber,	Imran	Sarwar Bajwa,	M.	Asif	Naeem,	NL-Based	Automated	Software	Requirements	Elicitation	and	Specification,	
Advances	in	Computing	and	Communications.	Communications	in	Computer	and	Information	Science	Volume	191,	Springer.	
2011,	pp	30-39.
– Dov Dori,	Nahum	Korda,	Avi Soffer,	Shalom	Cohen,	SMART:	System	Model	Acquisition	from	Requirements	Text,	Business	
Process	Management	(BPM).	LNCS.	Vol.	3080,	2004,	pp	179-194.
– Shalini Ghosh,	Daniel	Elenius,	Wenchao Li,	Patrick	Lincoln,	Natarajan	Shankar,	Wilfried Steiner,	ARSENAL:	Automatic	
Requirements	Specification	Extraction	from	Natural	Language,	SRI	INTERNATIONAL,	14	July	2014.
§ This	work	is	the	first	to	investigate	the	problem	of	requirement	extraction	from	natural	text	in	RFP	documents,	
and	specifically	those	from	services	domain
– Evidence-based	topic	identification
– Novel	concept-based,	and	cognitive	similarity	measure	for	requirements-offerings
27
© 2013 IBM Corporation
PREDICTIVE ANALYTICS FOR IT
SERVICES DEALS
28
Hamid R. Motahari Nezhad, Daniel B. Greenia, Taiga Nakamura, Rama Akkiraju:
Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments. IEEE SCC 2014: 155-162
Daniel B. Greenia, Mu Qiao, Rama Akkiraju (and Hamid R. Motahari Nezhad):
A Win Prediction Model for IT Outsourcing Bids. SRII Global Conference 2014: 39-42
Peifeng Yin, Hamid R. Motahari Nezhad, Aly Megahed, Taiga Nakamura:AProgressAdvisor for IT Service Engagements. SCC 2015: 592-599
Aly Megahed, Peifeng Yin, Hamid Reza Motahari Nezhad:An Optimization Approach to Services Sales Forecasting in a Multi-staged Sales
Pipeline. SCC 2016: 713-719
© 2013 IBM Corporation
Outsourcing Service Opportunities - Pipeline Management
§Service providers maintain and manage a pipeline of service opportunities to
pursue.
§Service pursuit management is a very elaborative, time-consuming and resource-
demanding process (for large deals, $10M+)
§ Effective pipeline management (pipeline prioritization) and maintaining a pipeline
of healthy opportunities are key for service providers
–Opportunity win prediction
–Opportunity health analysis
29
Objective:	Build	a	predictive	model	for	estimating	the	probability	of	winning	strategic	IT	service	deals,	
and	ranking	deals	in	the	pipeline
© 2013 IBM Corporation
Sales Opportunity Data
§Quantitative information about the deal (categorical, and numerical)
–Hundreds of numerical and categorical information about deals including
client name, deal size (contract value), sales stage , sector, deal complexity,
market analysis, quality and risk assessment, etc.
§Deal comments made by the sales team and also by technical solutioning team
–Comments are made at time intervals (often weekly)
–Comments are short, sometimes cryptic, with specific jargons
–Often do not include full English sentences, sentences are connected (no
punctuation), etc.
13
© 2013 IBM Corporation
Business and Technical Problems
§Predicting the outcome of an engagement by devising a predictive model that
uses both quantitative and textual comments, and analyzing them to find
predictive features.
–Predicting the outcome of the engagement based on quantitative and
comments
–How early we can predict and with what accuracy
–Pipeline ranking
§Identifying the health of an engagement by looking at the textual comments
that made by the sales team
–Engagement health: understanding the current status of the engagement by
looking at the comments
14
© 2013 IBM Corporation
Win Prediction Model: Combined Quantitative and Qualitative Model
Historical
Quantitative	
Data
Score	each	deal	
and	produce	a	
prioritized	list	of	
deals
Sales	executives	
receive	prioritized	
list
1)	Deal	1
2)	Deal	2
3)	Deal	3
…
n)	Deal	N
Current	pipeline
data
Logistic	
Regression	
&	Bayesian	
Model
Historical	Deal	
comments
Comment-based	
Prediction	Model
Cmment
-based	
scores
Quanti
tative-
based	
scores
Combine	
Predictions
Extensive	feature	
engineering	with	defining	
derived	features
15
© 2013 IBM Corporation
Prioritization Performance Evaluation
33
The	Win	
Prediction	
ranked	list	is	
frontloaded	
with	deals	
that	are	
likely	to	win:	
70%	of	wins	
are	in	top	
40%	of	the	
list.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.00
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0.70
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0.77
0.79
0.81
0.83
0.85
0.87
0.89
0.91
0.93
0.95
0.97
0.99
Cum.FractionofWins
Cum. Fraction of Data
Randomly Prioritized Win Probability Prioritized
TCV prioritized Expected Revenue Prioritized
© 2013 IBM Corporation
Deal Win Prediction using Comments
34
Textual comment
Pre-processing, and
Key n-gram Selection
Sentiment-based
Tag extractions
Correlation Analysis of
Extracted tags
With outcomes
Sentiment-based
Tags
tags-based
Outcome Prediction
Model
Textual
Features
(key n-grams)
Weighted Combined Outcome Prediction
Text-based Prediction
Model Builder
Tag-based
Prediction Model Builder
Textual Feature (n-gram)
Selection
TermExtractor
Sentiment-based
Tag Extractor
Feature Preparation and Selection Module
Text-based
Outcome Prediction
Domain
Vocabulary
and Types
project
Comments
New (open) project
comments
project
Comments
(Training)
project
Comments
(Training)
Combined
Predicted
Outcome
Sentiment-based
Outcome Prediction
Hamid R. Motahari Nezhad, Daniel B. Greenia, Taiga Nakamura, Rama Akkiraju:
Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments. IEEE SCC 2014: 155-162
© 2013 IBM Corporation
Illustration of the approach
Sentiment-based
Tag Extraction
Comment	
Text
Vocabulary
SP
Internal BU
Partner
Competition
Customer
New tag computation, and tag-
based Outcome Prediction
The set of terms identified
as frequently appearing
terms in from Loss Reason
fields:
Proposal, Price, Solution,
Cost, … .
Phrase-Entity Relationship <subject, phrase: sentiment,
object>: new sentiment
C1 C2 … … Cn
Text pre-processing, comment subset selection,
text feature selection
C1 C2 … … Cn
…
Prediction
(Weighted)
Tag-based
Predictor
Sentiment-
based features
Project
Entities
Text-based
Predictor
Text features
(n-gram)
Final
Predicted
Outcome
Comments score = ∑ s(i)* w_c(i), i is phrase with a sentiment in the update
s(i): sentiment score of I, w_c(i): class memebership to
indicative terms
18
© 2013 IBM Corporation
Experiments
§ 4,105 historical engagement data over 3 years as the training set
§ Close to 500 in-flight engagement deals as the testing set
36
Experiment Overall
Accuracy
Win
Prediction
Accuracy
Win
Prediction
Recall
Loss
Prediction
Accuracy
Loss
Prediction
Recall
Free-form text 61.5% 72% 60% 51% 76%
Text with Concept-
based Features
70% 85% 61% 55% 81%
Text with Concept-
based and Sentiment-
based Features
72.5% 87% 62.5% 58% 84%
© 2013 IBM Corporation37
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Predictive Value of the Number of comments - Win Outcome
Total Comments Predictive Comment
Evaluating	how	early	(#	of	
comments,	here)	the	prediction	
matches	the	final	outcome:	
between	1/3	and	half	of	the	
comments
A	follow	up	analysis	shows	that	
only	in	11%	of	cases	the	prediction	
may	change	as	new	comments	
become	available
© 2013 IBM Corporation
Combining Quantitative and Qualitative Analyses
38
Quantitative	Model
Relies	on	historical		attributes	for	
historical	deals
Comment-based	Model
Leverages	deal	team	“local”	insights	
to	gauge	the	trajectory	of	the	current	
deal	(micro	view).
Prob.	Of	Winning	=	Weight1 x	Quant	Score		+	Weight2 x	Qual Score
Quant
Model
Qual
Model
Historica
l Sales
Data
Current
Deal
logs
Model	output	is	
combined	using	
weights	(logistic	
regression).
© 2013 IBM Corporation
Sentiment-based Deal Health Analysis
Historical	
comments
Break	the	comment	
text	into	sentences
Week 1 Week 2 Week 3 … Week n
S1 S2 … Sm
Sentence-level
Annotation
Comment-level
Annotation
Comment-level
Annotation
Comment-level
Annotation …
Deal-level	Health	Status	
Win, Promising, Progress,
Neutral
Warning, Troubled, Loss
Weighted aggregation of scores
Mapping each labels to a score between -1 .. 0 .. +1
23
© 2013 IBM Corporation
Opportunity Health Analysis based on comments
§ Mapping each opportunity comment to a health status
– “Promising”, “Progressing”, “Neutral”, “Warning”, “In-Jeopardy”
§ Examples
– Price needs to be approved by WW
– Customer has asked for some changes to the proposal
– Client requirements are to be confirmed [early stages]
– Agreement to proceed w/ Provider1 & Provider2
– ABB accepted the proposal from Competitor
– The issues with Partner has been resolved
40
Deal	Health	Analytics	Tool	Offers	functions	for	Monitoring	the	status	of	Deals	as	Sellers	Comments	arrive	during	
the	quarter.
© 2013 IBM Corporation
DEAL PROGRESS MONITORING
41
P. Yin, H.R. Motahari-Nezhad, A. Megahed, T. Nakamurra, A Progress Advisor for IT Service Engagements. IEEE SCC 2015 (to appear).
© 2013 IBM Corporation
Problem Definition and Objective
§ Limitations of the Win Prediction Model:
Prediction is for eventualwin or loss, not for the event of the deal being rolled over to the next quarter
– Prediction is for eventualwin or loss, not for the event of the deal being rolled over to the next quarter
– There is no prediction capability for the outcome and timeline of milestones (key to deal success)
– There is no idea on when key events (such as win or loss) would happen
§ Objective:
– Building a model that gives analytical insights about the key events and milestones as well as the timeframe within which
they happen
42
© 2013 IBM Corporation
Analysis
§ Analysis shows that distribution of time intervals for the occurrence of key events and milestones decays exponentially
§ Longer time interval of no activity (event progression) leads to a higher chance of losing
43
Time Unit
EmpiricalProbability
(c) Probability of Loss w.r.t.
time unit
Geometric distribution
© 2013 IBM Corporation
Methodology
§ Devise a Bernoulli based deal-specific process for the prediction of event time intervals
– It identifies the probability of the occurrence of events and thus helps in understanding how fast or slow a deal is moving
forward
– This model is used to learn the weights of deal attributes to compute the parameter of a geometric distribution for the next
event occurrence time interval
§ Bernoulli-Dirichlet Generative Process: models the type of occurred events: win, loss, Milestone update
– It is trained to learn the weights of deal attributes to compute the parameters of a stochastic process that models the type
of next occurring event
§ Prediction
– The model estimates the probability of different event types given the deal attributes X, and time interval T, i.e.,
probability that the given event may happen within the time interval T
44
© 2013 IBM Corporation
DEAL COMPETITIVENESS ASSESSMENT
45
© 2013 IBM Corporation
The basic premise to be used throughout the Deal Competitive Assessment is to be able to compare a given ‘Compare
From’ source to available “Compare To” data sources through a standard method of peer selection, and to present the output
in a standard way globally
Tower/Service Scope
Peer
Criteria
Peer Selection Criteria Compare To Sources
Bid Data
Market Data
Delivery Data
DiminishingNumberofDataSamples
X1
X2
X3
X4
X5
Local Sources
Compare
From
Sources
Deal
Metric
Standard Global Representation
Standard
Model
Offering
Standard Models
Offerings
Contract Prices Pricing
Deal Competitiveness Assessment
30
© 2013 IBM Corporation
Approach to Assessing Competitiveness
§Mine ‘similar’ prior deals and market benchmark data
§Determine the upper and lower bounds on unit costs and unit prices for each of the
service involved in an IT service solution.
§Add things up to get upper and lower bounds, and assess the percentile of the
given case.
§Create a case management solution, where:
–Users can edit/add/remove services involved.
–Users can see/change/add peer deals
§The key challenge is in determining ‘similarity’ among complex IT service solutions.
We present an approach to derive close comparables in this effort
47
© 2013 IBM Corporation
Peer-Selection Filtering
§ Boolean: Has global resources or not
§ Geographical: Where it was
§ Categorical: Won, lost, or either
§ Numerical: Quantity of services
§ Unstructured text: Attributes with long text descriptions, images, etc.
§ Timing: recent enough.
48
Tuple: {service, # of units requested, $unit cost,
$ unit price, geo deliver-from, geo deliver-to}
D1
s1, 200, $44
s2, 300, $2.88
s3, 2000, $555
s4, 1000, $674
cs1, N/A, 10%
cs2, N/A, 20%
D2
s2, 200, $3.50
s4, 3000, $500
cs1, N/A, 12%
cs2, N/A, 18%
D3
s1, 500, $40
s3, 1,500, $450
cs1, N/A, 15%
cs2, N/A, 22%
D4
s2, 200, $3.50
s4, 1500, $620
cs1, N/A, 12%
cs2, N/A, 18%
© 2013 IBM Corporation
The System View of IT Service Solution Price Competitiveness Analysis
33
© 2013 IBM Corporation
Sales Pipeline Revenue Prediction Methodology Overview
50
Historical	
Win	Conversion	&
Growth	Data
What future opportunities would
come into the pipeline that will be
won by the end of the period
(Growth)?
Wouldwewinthese
opportunities(Conversion)?
Non-Linear	
Optimization	
Model
Linear	
Optimization	
Model
Optimal	
Weights	
Optimal	
No.	of	
Historical	
Periods	to	
Use	(N)
Current	
Pipeline
Revenue	
Prediction	
(Conversion	
&	Growth
Apply	Weights	on	
N	Historical	
Conversion	and	
Growth	Rates
Apply	
Rates	to	
Current	
Pipeline
Objective: Predicting the revenue of sales pipeline for different sales stages
Aly Megahed, Peifeng Yin, Hamid Reza Motahari Nezhad:An
Optimization Approach to Services Sales Forecasting in a
Multi-staged Sales Pipeline. SCC 2016: 713-719
© 2013 IBM Corporation
FROM SERVICES TO COGS, AND TO COGNITIVE
BPM
What advances in AI and Machine Learning
mean for Service Computing and BPM?
51
© 2013 IBM Corporation
Service Computing: From API to CCL
§ The End of using API for Programming Business Logic
– APIs will be used to initiate Cogs (Intelligent Bots)
– The Business Transaction to be performed in Conversations with Cogs
§ Cogs representing Providers/Consumers,spanning over a spectrum:
– From Cogs taking over the interface of existing Apps
– To Cogs codifying and understanding the business logic and engaging in
conversations to transact
§ Cog Conversation Language (CCL)
– CCL should provide support for defining a rich natural language conversations for a
Cog to deliver business functionalities to the users (other Cogs, and Humans)
• The Language to Program Cogs
• An initial example is Watson Dialog Services Template Language
52
Source: blog.cloudsecurityalliance.org
© 2013 IBM Corporation
The notion of Service/People Composition to be Re-Defined
§ In current Hybrid composition/mashup (People,
Services) methods:
– Services are represented with API calls
– People are integrated with Human Tasks (GUI
is the interaction paradigm)
– Composition methods are finding deterministic
models of interactions, defined apriori
§ We are moving towards dynamic composition of
cogs and human in which
– Cogs are participating in NL conversations
– Human are approached through messaging
and natural language
– Composition are performed dynamically during
the conversation,require non-deterministic
models, defined in online and on-demand
model
53
Weather
Cog
Health
Agent
Personality
Insight Cog.
Provider
Cogs
Travel Cog 1
Travel Cog 2
Planning a Vacation
Trip
Considering preferences,
experience, conditions, cost,
Availability, etc.
Mediated and facilitated by Cogs
Human-Cog interaction
Cog-Cog interaction
Natural Language
Natural Language, CCL,
(ACL, KQML, etc.)?
ACL: Agent Communication Language, KQML, etc.
© 2013 IBM Corporation
The App Composition (Mashup) is already moving away from explicit API calls
§ Implicit Data Sharing with the notion of Central Shared Context on
Mobile Platforms
– Events
– Notifications
– Metadata descriptions
§ Google Now on Tap (implicit integration)
– Central Shared Context
§ Apple Proactive
54
© 2013 IBM Corporation
Process Automation Stages in Enterprise & in IT Services
Humans
(Manual)
Program/
Workflow
Robotics
(RPA)
Cognitive
55
Issues Current Enterprises facing
• High volume of manual processes
• With high variability
• Involving unstructured data
“85% of a typical firm’s 900+ processes
can be automated.”
High Cost of Automation
using Traditional
Approaches (to go from
50% to 85%)
© 2013 IBM Corporation
Historical and Future Perspectives on BPM
56
Databases
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
Presentation Presentation
XML
API
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
TQM
General Workflow
BPR
BPM
time
ERP
WFM
EAI
‘85 ‘90 ‘95 ‘05‘00‘98
IT Innovations
Management Concepts
DatabasesDatabases
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
Presentation Presentation
XML
API
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
Presentation Presentation
XML
API
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
TQMTQM
General Workflow
BPRGeneral Workflow
BPR
BPMBPMBPM
time
ERPERP
WFMWFM
EAIEAI
‘85 ‘90 ‘95 ‘05‘00‘98
IT Innovations
Management Concepts
Ref: Ravesteyn, 2007
‘16
Social BPM
iBPMS: Business
Process Analytics
‘2021
The Future of BPM is also Cognitive
Dark Data
Cognitive BPM
Cognitive
Analytics
Cognitive
Processes
Interact
LearnEnact
Cognitive
Capabilities
© 2013 IBM Corporation
Dark Data: digital footprint of people, systems, apps and IoT devices
§ Handling and managing work (processes) involves interaction among employees, systems and devices
§ Interactions are happing over email, chat, messaging apps, and
§ There are descriptions of processes, procedures, policies, laws, rules, regulations, plans, external entities such as
customers, partners and government agenies, surrounding world, news, social networks, etc.
§ The need for activities over interactions of people, systems, and IoT devices to be coordinate
57
Citizens
Assistant
Business
Employees/
agents
Plans
Rules
Policies
Regulations
TemplatesInstructions/
Procedures
ApplicationsSchedules
Communications such as
email, chat, social media,
etc.
Organization
Dark Data: Unstructured Linked Information
IoT Devices and Sensors
© 2013 IBM Corporation
Spectrum of Work: Processes and Cognitive
58
Structured Processes
Unstructured Processes
Knowledge-based
Routine
Existing Technology
Dark Data: Mobile, Social, Communication (email, voice, video), Documents, Notes, Sensors
BPM
Engines
Workflow
Engines
Case
Management
Groupware
Knowledge-Intensive
Processes
Email, Chat, Messaging
Ad-hoc, unstructured
Processes
Cognitive Process Management
Conversational
Interface for
Processes
Cognitive Process
Learning
Cognitive
Process
Analytics
Cognitive
Enactment
© 2013 IBM Corporation
Cognitive BPM Systems
§ A Cognitive BPM system is a cognitive system that provides cognitive support in all phases of a
process lifecycle over structured and unstructured information sources, and is able to
continuously discover, learn and proactively act to support achieving a desired outcome
– It offers cognitive interaction and analytics support over structured processes
– For unstructured processes, it offers intelligent and integrated process (model) definition,
reasoning and adaptation
• Process is not assumed apriori defined; but is discovered, learned and customized based
on accumulated knowledge and experience
–It continually learns to improve the process
59
© 2013 IBM Corporation
Cognitive BPM Lifecycle
60
Cognitive
BPMS
Define
Enact
Monitor
Analyze
Next Steps, Adapt
Interact
Sense
Learn,
Discover
To
Traditional BPM
Cognitive BPM
© 2013 IBM Corporation
Cognitively-Enabled Processes: Shifting process lifecycle
from Define-Execute-Analyze-Improve to Plan-Act-Learn
§ For each enactment of the overall process, many iterations around
this loop
§ At a given time, multiple goals & sub-goals may be active
– Numerous threads of activity
– Each thread modeled essentially as a “case” as in Case Mgmt
– Cf. [Vaculin et al, 2013]
§ As new information arrives the cycle might re-start for some or all
threads
– Planning based on new info
• New goal formulation
• Planning to achieve those goals
§ “Cognitive Agent” helps by
– Perform the planning
– Learn from large volumes of structured/unstructured data
– Over time, learn best practices and incorporate into planning
Plan /
Decide
Act
<<World Effect>>
Learn
Richard Hull, Hamid R. Motahari Nezhad: Rethinking BPM
in a Cognitive World: Transforming How We Learn and
Perform Business Processes. BPM 2016: 3-19
© 2013 IBM Corporation
Towards Cognitive BPM: Example Scenarios
62
Example (1): Integrate IBM BPM with IBM
Watson
http://www.ibm.com/developerworks/bpm/library/techarticles/1501_mehra-bluemix/1501_mehra.html#N1009D
Email, Chat, and Calendaring apps are
the most used channels for doing work
in the enterprise
Addressing the work organization and
management for Knowledge workers:
monitoring communication channels (email,
chat), and:
- Capturing, prioritizing and organizing work
of a worker
- Identifying actionable statements
(requests, commitments, questions) and
track them over the course of
conversations
Example (2): eAssistant for
Knowledge Workers
© 2013 IBM Corporation63
Inbox - Verse Highlighting actionable statements Recommending fulfilment actions
IBM Insight 2015 – The session on “Given your collaboration tools a brain”
© 2013 IBM Corporation64
IBM Insight 2015 – The session on “Given your collaboration tools a brain”
Send File Action Archetype Send File Action Archetype Send File Action Archetype
© 2013 IBM Corporation65
IBM Insight 2015 – The session on “Given your collaboration tools a brain”
Invite/Calendar Action Archetype Automated Invite Parameters Extraction Calendar Entry Creation
© 2013 IBM Corporation
eAssistant App and APIs
66
Watson (& BigInsight NLP) Apps and Services on BlueMix
CollaborationTools
Enterprise Repositories, Applications and Data Sources
Feeds
Repositories
Document
collections
…
eAssistant Apps
Personal
Knowledge
Graph Builder
Conversation Analytics,
Auto-Response,
Prioritization
Calendar and
Scheduling
Assistant
Cognitive
Process
Learning
To-do, Task
and Process
Assistant
Cognitive Work Assistant APIs
Semantic Role
Labeling
POS tagging
Dependency
Analysis
Co-reference
resolution
Named Entity
Recognition
Knowledge
Graph
Builder
H. R. M. Nezhad. Cognitive assistance at work. In AAAI Fall Symposium Series. AAAI Publications, November, 2015.
© 2013 IBM Corporation
Cognitive BPM: Selected research challenges
§ Cognitive process learning:
4Knowledge acquisition methods from unstructured information (text, image, etc.)
4Combine with traditional process mining on logs
4Building actionable knowledge graphs & executable code
§ Cognitively enabled processes: Plan-Act-Learn
4Blending of “model” and “instance”
4Recognizing goals from digital exhaust and process history
4Advances in planning research – incremental, multi-threaded activity, richer goal
languages, prioritized and soft goals, …
4Enough uniformity to support reporting, identification of best practices
§ Cognitive Assistants for business processes
4Assist workers across numerous tasks, including process management & optimization
4Interactive learning where cognitive agents ask process questions
4Gradual learning through experience, and process improvement
© 2013 IBM Corporation
Summary
§ The Future of Computing is ….
§ The Future of Work is ….
§ The Future of Services is ….
§ The Future of BPM is ….
§ A huge, unprecedented opportunity for the research community to advance our understanding,methods and technology
underpinning these transformations and disruptions!
68
Cognitive
Cognitive Computing
Cognitive Assistance
Cognitive Services
Cognitive BPM
© 2013 IBM Corporation
QUESTIONS?
Thank You!
69
© 2013 IBM Corporation
Model of Human AdministrativeAssistants: conceptual framework
70 T. Erickson,etc.: Assistance:The Work Practice of Human Administrative Assistants and their Implications for IT and Organizations,CSCW’08.
Blocking, Doing, Redirecting
Key to the performance of Assistants
© 2013 IBM Corporation
Cognitive BPM in Cognitive Assistants/Agents
§ Goals
– Increasing worker’s productivity, efficiency, and creativity (serendipity)
§ Current cognitive assistants are focused on personal space or virtual conversationalagents
§ Cognitive Work Agent
– Is process and work aware
– Monitors worker’s input channels and interactions (emails, chats, social connections,external and
internal environment, knows rules, policies and processes)
– Proactively acts on worker’s behalf and reacts to requests: becomes a copy of you in work environment
• Commands/requests - Responds to simple requests intelligently
• Situational awareness – monitors the environments to overcome information overloading (selective).
• Deep QA: process questions, how-tos, previous successfulprocess experience
– Organizes and assists your work
• Extract tasks/commitments, promises, commitments
• Managed to-dos: status updates, over-dues, plans
• Manages calendar, schedules,social contacts
• Finds and present prior related interactions to a particular conversation
– Learns how work gets done, and can take care of them for their human subject
71
© 2013 IBM Corporation
Cognitive Assistant
§ A software agent (cog) that
– “augments human intelligence” (Engelbart’s definition1 in 1962)
– Performs tasks and offer services (assists human in decision making and taking actions)
– Complements human by offering capabilities that is beyond the ordinary power and reach of human (intelligence
amplification)
§ A more technical definition
– Cognitive Assistant offers computational capabilities typically based on Natural Language Processing (NLP),
Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that
augment and scale human intelligence
§ Getting us closer to the vision painted for human-machine partnership in 1960:
– “The hope is that, in not too many years, human brains and computing machines will be coupled together very
tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way
not approached by the information handling machines we know today”
“Man-Computer Symbiosis , J. C. R. Licklider IRE Transactions on Human Factors in Electronics, volume HFE-1,
pages 4-11, March 1960
72 1
Augmenting Human Intellect: A Conceptual Framework, by Douglas C. Engelbart, October 1962

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Cognitive Enterprise Services

  • 1. © 2015 IBM Corporation Hamid R. Motahari-Nezhad IBM Almaden Research Center San Jose, CA The Journey to Cognitive Enterprise IT Services: A Framework for Cognitive Services and Business Processes Talk at University of New South Wales, Sydney,Australia. Nov. 29, 2016
  • 2. © 2013 IBM Corporation Major Technology Trends Impacting Enterprise Business 2 Mobile Social Cloud Internet of Things 20162000
  • 3. © 2013 IBM Corporation We are here 44 zettabytes unstructured data 2010 2020 structured data Data is the world’s new natural resource! (Ginni Rometti, IBM Shareholders Report, 2014) We are here Sensors & Devices VoIP Enterprise Data Social Media 5
  • 4. © 2013 IBM Corporation Mega Trends: Data, Cloud, and Mobile 4 80% of the world’s data today is unstructured 90% of the world’s data was created in the last two years 1 Trillion connected devices generate 2.5 quintillion bytes data / day 3M+ Apps on leading App stores By 2017 The collective computing and storage capacity of smartphones will surpass all worldwide servers 48% of enterprises are moving to the cloud to replace on-premise, legacy technology today 72% of enterprises have at least one application running in the cloud, growing from 57% in 2012 The average enterprise uses 738 cloud services.
  • 5. © 2013 IBM Corporation A new computing paradigm is emerging Tabulating Systems Era Programmable Systems Era Cognitive Systems Era
  • 6. © 2013 IBM Corporation Intelligent Assistance and Machine Learning - Landscape 6 IPSoft’s Amelia
  • 7. © 2013 IBM Corporation Cognitive Era 7 Discovery & Recommendation Probabilistic Big Data Natural Language as the Interface Intelligent Options
  • 8. © 2013 IBM Corporation Towards Computing-At-Scale as the Shared Characteristic of Recent Advances 8 Scalable Computing over MassiveCommodity Hardware Building Stronger Super Computers Cloud Computing Crowd Computing Advanced individual algorithms Mass computing applied to AI Complex array of algorithms applied to make sense of data, and offer cognitive assistance Big Data Individual MLAlgorithm Cognitive Computing
  • 9. © 2013 IBM Corporation Understands natural language and human communication Adapts and learns from user selections and responses Generates and evaluates evidence-based hypothesis Cognitive System 1 2 3 Cognitive Systems do actively discover, learn and act A Cognitive System offers computational capabilities typically based on Natural Language Processing (NLP), Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that augment and scale human knowledge and expertise Watson
  • 10. © 2013 IBM Corporation ENTERPRISE SERVICES 10
  • 11. © 2013 IBM Corporation Enterprise Services 11 A. Service Provider • Individual • Institution • Public or Private C. Service Target: The reality to be transformedor operated on by A, for the sake of B • Individuals or people,dimensions of • Institutions or business and societal organizations, organizational (role configuration) dimensions of • Infrastructure/Product/Technology/Environment, physical dimensions of • Information or Knowledge,symbolic dimensions B. Service Customer • Individual • Institution • Public or Private Forms of Ownership Relationship (B on C) Forms of Service Relationship (A & B co-create value) Forms of Responsibility Relationship (A on C) Forms of Service Interventions (A on C, B on C) Spohrer, J., Maglio, P. P., Bailey, J. & Gruhl, D. (2007). Steps toward a science of service systems. Computer, 40, 71-77. From… Gadrey (2002), Pine & Gilmore (1998), Hill (1977) A B C Vargo, S. L. & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68, 1 – 17. “Service is the application of competence for the benefit of another entity.” Major Types of Service (provider perspective): • Computational/technology services • Business/Enterprise services • People Services Service Offerings Definition & Design Service Sales Pursuit Transition and Transformation Service Delivery & Operation Lifecycle of Enterprise (IT) Services
  • 12. © 2013 IBM Corporation Information Technology Service Models Client Managed Procure, Own, Install & Manage [CAPEX] Vendor Managed in the Cloud On-Demand as a Pay as You Go (PAYG) price [OPEX] Applications Data Runtime Middleware O/S Virtualization Servers Storage Networking Traditional IT Applications Data Runtime Middleware O/S Virtualization Servers Storage Networking IaaS Infrastructure as a Service Applications Data Runtime Middleware O/S Virtualization Servers Storage Networking Applications Data Runtime Middleware O/S Virtualization Servers Storage Networking Managed IaaS Managed Infrastructure as a Service Applications Data Runtime Middleware O/S Virtualization Servers Storage Networking PaaS Platform as a Service Applications Data Runtime Middleware O/S Virtualization Servers Storage Networking SaaS Software as a Service Customization, higher costs, slower time to value Standardization, lower costs, faster time to valueStandardization, lower costs, faster time to value ClientManaged VendorManagedintheCloud Local, Dedicated Public Workforce Perspective Staff Body x Price x Utilization Outsource Body x Price x Utilization Digital Workforce (Bots + Body) x Price x Utilization ClientManaged…….…VendorManaged
  • 13. © 2013 IBM Corporation Managed Information Services: From RFP to Transition and Delivery 13 Opportunity Deal Deal Deal Checkpoints/ Contract T&T Steady-State Renewal Identification Validation Qualification Pursuit QA/Risk Analysis Delivery Engagement Transition & Transformation Renewal Steady-State Delivery Business Development RFP Receipt Week 1 • Team Formation, and assignment • Control Matrix Preparation • Window of opportunity to ask questions from client Week 2-x RFP Response Deadline Solution & Approvals in Place • Proposal Writing • Client Presentation Preparation • RFP Response Items … • Detailed SOW Analysis • Baselines • SRM • Solutioning • Reviews • Approvals Control Matrix SRM FRM Baselines SOW Solutioning • Proposal • Client Presentation • Attachments/schedules Reviews and approvals CSE PM Transition and Transformation Plan • Contract Writing • Contract Analysis Service Pursuit Demystified: From RFP to Contract
  • 14. © 2013 IBM Corporation Cognitive Enterprise IT Services Framework 14 Prior Deals Service Offerings Guidelines, methodologies People Profiles Lessons Learned Service Delivery Data Opportunity Deal Deal Deal Checkpoints/ Contract T&T Steady-State Renewal Identification Validation Qualification Pursuit QA/Risk Analysis Delivery Engagement Transition & Transformation Renewal Steady-State Delivery Business Development Current Deals Pipeline Revenue & Finance Information Integrate and Make the Data Available Using Interfaces (APIs) Deal Information Management Enable Reusing Deal Artifacts and Sharing Knowledge Deal Team Analytics Find Expertise and Recommend Them Deal Competitive Assessment Analyze Competitiveness based on Cost/Price Deal Win Prediction Analytics to provide deal win prediction, and pipeline ranking Sales Pipeline Revenue Prediction Cognitive RFP, Proposal and Contract Analyzing RFPs to extract requirements, and author RFP Response, and Contract Drafts Cognitive Solutioning Compose the set of service offerings that meets clients requirements
  • 15. © 2013 IBM Corporation COGNITIVE RFP, RESPONSE AND CONTRACT 15 Hamid R. Motahari Nezhad, Juan M. Cappi, Taiga Nakamura, Mu Qiao: RFPCog: Linguistic-Based Identification and Mapping of Service Requirements in Request for Proposals (RFPs) to IT Service Solutions. HICSS 2016: 1691-1700
  • 16. © 2010 IBM Corporation© 2016 IBM Corporation Input and problem statement § RFP Documents are textual documents sent by service requesters describing the requirements for IT services – The requirements are stated in natural language, with a varied format in general § RFP package contains 10s or 100s of document, each with 100s of pages describing various aspects of existing IT environment (detail baseline), and future state requirements § There are hundreds of requirements stated for each IT service in each RFP that need to be identified and analyzed, including who’s responsibility (service provider or customer) is to perform each § Different clients organize the documents and content differently, and use different vocabulary and terminology to refer to IT services and requirements § Identification of what constitute a requirement is very challenging – The structure (organization) of the document, the language construct of sentences and also client vocabulary differs – Natural language by definition can be ambiguous, documents have incomplete information, and expertise needed in interpreting and understanding requirement
  • 18. © 2010 IBM Corporation© 2016 IBM Corporation IT Service Requirements Analysis: the need for a meta-model 18 “Service provider shall provide onsite Desktop Services dispatching resources on 24 hour a day, 7 day a week basis, for Supported Equipment and Supported Devices at all Client’s Service Locations, which locations may be modified from time to time by Client in accordance with the applicable Change Control Procedure”. Responsible Party: Service Provider Verb phrase: shall provide Topic/Service: OnsiteDesktopServices SLAneeds: 24 hour a day, 7 day a week Services for: Supported Equipment and Devices Locations:All Client’s Service Locations Duration of service: <Contract term>
  • 20. © 2010 IBM Corporation© 2016 IBM Corporation Research Problems § Requirements identification – What statements constitute a requirement in RFP documents? – Requirements vs sub-requirements? § Requirements topic identification (IT services) – Which IT services they are talking about? § Service Offering Mapping - Solutioning – Which IT Service Offerings meet the client requirements? § Continues learning through Human feedback – How to manage human interactions, feedback and adaptive learning? 20
  • 22. © 2010 IBM Corporation© 2016 IBM Corporation RFPCog for Cognitive RFP Analysis: Overview 22 RFP Documents Contract Documents Requirements Identification Service Catalogs ITIL Requirements- Driven Offerings Composition Requirements-driven Technical Solutions Composition Solution Patterns Customer Service Vocabulary Solutions Taxonomy Provider Offering Taxonomy What are client requirement statements? What services offerings/solutions these requirements map to? Requirements Topic Identification and Grouping What are in-scope and out-of-scope service?
  • 23. © 2010 IBM Corporation© 2016 IBM Corporation RFP Docs Structure Analysis Pattern- based Requirement Candidate Identification NLP-based Deep Learning for Requirement Identification Machine Learning- Based Topic Identification Document Table Section Paragraph Sentence Cell In what sectionof what document is the requirement from? Boundary Identification Requirement Patterns How does clients state requirements? Patterns: •( [Subject] + (shall | must | is required to | … ) ) + Action Verb + … •[Subject] is/are responsible for … • Where does a requirement start and end? è What is a requirement span? è Req., and Sub- req. identification Recognize noun (phrases), verb (phrases), … Requirement Features Apply NLP techniques for recognition of Who does what? Word Dependencies and Implicit Feature Identification Topic/Service What is the requirement about? • Linguistic-based Requirement Focus Identification • Topic-related Feature Extraction Use Domain Knowledge • Provide Service Taxonomy • Information Technology Infrastructure Library (ITIL) • Customer Vocabulary extracted from Documents Apply Supervised Learning using • Support Vector Machine • Logistic Regression RFPCog: Method Steps for Requirements and Topic Identification
  • 24. © 2010 IBM Corporation© 2016 IBM Corporation Cognitive Solutioning - Requirements to Service Offerings Mapping § For a given requirement (or requirement group), the focus is to identify service elements (at multiple level of hierarchies) that map to the requirements, and their sub-requirements – IT Service Catalog-aware Phrase Matching – Considering the body text, concept hierarchy through a statistically-built semantic model to identify matching § Novel Method for matching noun phrases in requirements and offerings: a modified Longest Common Sequence (LCS) term matcher. – One main difference with other similarity metrics such Cosine and Jaccard is that the LCS preserves the order of tokens in matching, while other don’t. – Missing keywords in the two phrase are penalized based on the importance of the keyword 24 Based_Similarity_Score= #LCS / Weighted_ Denominator, where Weighted_ Denominator is defined as the weighted sum of the number of missing words in the E_Seq. Final_Similarity_Score = Based_Similarity_Score * (1 – net_distance/C), in which C is a constant for the maximum length of noun phrases in the population, and NetDistance is the absolute difference in tokens order difference of the LCS in NP_Seq and E_Seq (caters for additional terms in between) “Storage management solution” and “management solution”, keywords: storage, missing words
  • 26. © 2010 IBM Corporation© 2016 IBM Corporation Experimental Results – Requirements Topic Identification 26 ML-based Topic Classification Performance (TP Rate) 0.9518 0.8733 0.7587 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 SVM Logistic Regression Naïve Bayes TPRate Support Vector Machine (SVM) Performance Details TP Rate FP Rate Precision Recall FMeasure ROC Area Class 0.986 0.232 0.958 0.986 0.972 0.877 F 0.768 0.014 0.908 0.768 0.832 0.877 T Weighted Avg. 0.952 0.198 0.951 0.952 0.950 0.877
  • 27. © 2010 IBM Corporation© 2016 IBM Corporation Related Work § Templated Information extraction from text – Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with Python, http://www.nltk.org/book/, visited July 2015. – Ana-Maria Popescu, Information Extraction from Unstructured Web Text, PhD Thesis, Uni. Washington, 2007. § Extraction of requirements from textual software descriptions (Concepts, and Models according to SVBR - Semantic Business Vocabulary and Rules- , and OPM - Object-Process Methodology, or LTL - linear-time temporal logic) – Ashfa Umber, Imran Sarwar Bajwa, M. Asif Naeem, NL-Based Automated Software Requirements Elicitation and Specification, Advances in Computing and Communications. Communications in Computer and Information Science Volume 191, Springer. 2011, pp 30-39. – Dov Dori, Nahum Korda, Avi Soffer, Shalom Cohen, SMART: System Model Acquisition from Requirements Text, Business Process Management (BPM). LNCS. Vol. 3080, 2004, pp 179-194. – Shalini Ghosh, Daniel Elenius, Wenchao Li, Patrick Lincoln, Natarajan Shankar, Wilfried Steiner, ARSENAL: Automatic Requirements Specification Extraction from Natural Language, SRI INTERNATIONAL, 14 July 2014. § This work is the first to investigate the problem of requirement extraction from natural text in RFP documents, and specifically those from services domain – Evidence-based topic identification – Novel concept-based, and cognitive similarity measure for requirements-offerings 27
  • 28. © 2013 IBM Corporation PREDICTIVE ANALYTICS FOR IT SERVICES DEALS 28 Hamid R. Motahari Nezhad, Daniel B. Greenia, Taiga Nakamura, Rama Akkiraju: Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments. IEEE SCC 2014: 155-162 Daniel B. Greenia, Mu Qiao, Rama Akkiraju (and Hamid R. Motahari Nezhad): A Win Prediction Model for IT Outsourcing Bids. SRII Global Conference 2014: 39-42 Peifeng Yin, Hamid R. Motahari Nezhad, Aly Megahed, Taiga Nakamura:AProgressAdvisor for IT Service Engagements. SCC 2015: 592-599 Aly Megahed, Peifeng Yin, Hamid Reza Motahari Nezhad:An Optimization Approach to Services Sales Forecasting in a Multi-staged Sales Pipeline. SCC 2016: 713-719
  • 29. © 2013 IBM Corporation Outsourcing Service Opportunities - Pipeline Management §Service providers maintain and manage a pipeline of service opportunities to pursue. §Service pursuit management is a very elaborative, time-consuming and resource- demanding process (for large deals, $10M+) § Effective pipeline management (pipeline prioritization) and maintaining a pipeline of healthy opportunities are key for service providers –Opportunity win prediction –Opportunity health analysis 29 Objective: Build a predictive model for estimating the probability of winning strategic IT service deals, and ranking deals in the pipeline
  • 30. © 2013 IBM Corporation Sales Opportunity Data §Quantitative information about the deal (categorical, and numerical) –Hundreds of numerical and categorical information about deals including client name, deal size (contract value), sales stage , sector, deal complexity, market analysis, quality and risk assessment, etc. §Deal comments made by the sales team and also by technical solutioning team –Comments are made at time intervals (often weekly) –Comments are short, sometimes cryptic, with specific jargons –Often do not include full English sentences, sentences are connected (no punctuation), etc. 13
  • 31. © 2013 IBM Corporation Business and Technical Problems §Predicting the outcome of an engagement by devising a predictive model that uses both quantitative and textual comments, and analyzing them to find predictive features. –Predicting the outcome of the engagement based on quantitative and comments –How early we can predict and with what accuracy –Pipeline ranking §Identifying the health of an engagement by looking at the textual comments that made by the sales team –Engagement health: understanding the current status of the engagement by looking at the comments 14
  • 32. © 2013 IBM Corporation Win Prediction Model: Combined Quantitative and Qualitative Model Historical Quantitative Data Score each deal and produce a prioritized list of deals Sales executives receive prioritized list 1) Deal 1 2) Deal 2 3) Deal 3 … n) Deal N Current pipeline data Logistic Regression & Bayesian Model Historical Deal comments Comment-based Prediction Model Cmment -based scores Quanti tative- based scores Combine Predictions Extensive feature engineering with defining derived features 15
  • 33. © 2013 IBM Corporation Prioritization Performance Evaluation 33 The Win Prediction ranked list is frontloaded with deals that are likely to win: 70% of wins are in top 40% of the list. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.75 0.77 0.79 0.81 0.83 0.85 0.87 0.89 0.91 0.93 0.95 0.97 0.99 Cum.FractionofWins Cum. Fraction of Data Randomly Prioritized Win Probability Prioritized TCV prioritized Expected Revenue Prioritized
  • 34. © 2013 IBM Corporation Deal Win Prediction using Comments 34 Textual comment Pre-processing, and Key n-gram Selection Sentiment-based Tag extractions Correlation Analysis of Extracted tags With outcomes Sentiment-based Tags tags-based Outcome Prediction Model Textual Features (key n-grams) Weighted Combined Outcome Prediction Text-based Prediction Model Builder Tag-based Prediction Model Builder Textual Feature (n-gram) Selection TermExtractor Sentiment-based Tag Extractor Feature Preparation and Selection Module Text-based Outcome Prediction Domain Vocabulary and Types project Comments New (open) project comments project Comments (Training) project Comments (Training) Combined Predicted Outcome Sentiment-based Outcome Prediction Hamid R. Motahari Nezhad, Daniel B. Greenia, Taiga Nakamura, Rama Akkiraju: Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments. IEEE SCC 2014: 155-162
  • 35. © 2013 IBM Corporation Illustration of the approach Sentiment-based Tag Extraction Comment Text Vocabulary SP Internal BU Partner Competition Customer New tag computation, and tag- based Outcome Prediction The set of terms identified as frequently appearing terms in from Loss Reason fields: Proposal, Price, Solution, Cost, … . Phrase-Entity Relationship <subject, phrase: sentiment, object>: new sentiment C1 C2 … … Cn Text pre-processing, comment subset selection, text feature selection C1 C2 … … Cn … Prediction (Weighted) Tag-based Predictor Sentiment- based features Project Entities Text-based Predictor Text features (n-gram) Final Predicted Outcome Comments score = ∑ s(i)* w_c(i), i is phrase with a sentiment in the update s(i): sentiment score of I, w_c(i): class memebership to indicative terms 18
  • 36. © 2013 IBM Corporation Experiments § 4,105 historical engagement data over 3 years as the training set § Close to 500 in-flight engagement deals as the testing set 36 Experiment Overall Accuracy Win Prediction Accuracy Win Prediction Recall Loss Prediction Accuracy Loss Prediction Recall Free-form text 61.5% 72% 60% 51% 76% Text with Concept- based Features 70% 85% 61% 55% 81% Text with Concept- based and Sentiment- based Features 72.5% 87% 62.5% 58% 84%
  • 37. © 2013 IBM Corporation37 0 5 10 15 20 25 30 35 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165 169 173 177 181 Predictive Value of the Number of comments - Win Outcome Total Comments Predictive Comment Evaluating how early (# of comments, here) the prediction matches the final outcome: between 1/3 and half of the comments A follow up analysis shows that only in 11% of cases the prediction may change as new comments become available
  • 38. © 2013 IBM Corporation Combining Quantitative and Qualitative Analyses 38 Quantitative Model Relies on historical attributes for historical deals Comment-based Model Leverages deal team “local” insights to gauge the trajectory of the current deal (micro view). Prob. Of Winning = Weight1 x Quant Score + Weight2 x Qual Score Quant Model Qual Model Historica l Sales Data Current Deal logs Model output is combined using weights (logistic regression).
  • 39. © 2013 IBM Corporation Sentiment-based Deal Health Analysis Historical comments Break the comment text into sentences Week 1 Week 2 Week 3 … Week n S1 S2 … Sm Sentence-level Annotation Comment-level Annotation Comment-level Annotation Comment-level Annotation … Deal-level Health Status Win, Promising, Progress, Neutral Warning, Troubled, Loss Weighted aggregation of scores Mapping each labels to a score between -1 .. 0 .. +1 23
  • 40. © 2013 IBM Corporation Opportunity Health Analysis based on comments § Mapping each opportunity comment to a health status – “Promising”, “Progressing”, “Neutral”, “Warning”, “In-Jeopardy” § Examples – Price needs to be approved by WW – Customer has asked for some changes to the proposal – Client requirements are to be confirmed [early stages] – Agreement to proceed w/ Provider1 & Provider2 – ABB accepted the proposal from Competitor – The issues with Partner has been resolved 40 Deal Health Analytics Tool Offers functions for Monitoring the status of Deals as Sellers Comments arrive during the quarter.
  • 41. © 2013 IBM Corporation DEAL PROGRESS MONITORING 41 P. Yin, H.R. Motahari-Nezhad, A. Megahed, T. Nakamurra, A Progress Advisor for IT Service Engagements. IEEE SCC 2015 (to appear).
  • 42. © 2013 IBM Corporation Problem Definition and Objective § Limitations of the Win Prediction Model: Prediction is for eventualwin or loss, not for the event of the deal being rolled over to the next quarter – Prediction is for eventualwin or loss, not for the event of the deal being rolled over to the next quarter – There is no prediction capability for the outcome and timeline of milestones (key to deal success) – There is no idea on when key events (such as win or loss) would happen § Objective: – Building a model that gives analytical insights about the key events and milestones as well as the timeframe within which they happen 42
  • 43. © 2013 IBM Corporation Analysis § Analysis shows that distribution of time intervals for the occurrence of key events and milestones decays exponentially § Longer time interval of no activity (event progression) leads to a higher chance of losing 43 Time Unit EmpiricalProbability (c) Probability of Loss w.r.t. time unit Geometric distribution
  • 44. © 2013 IBM Corporation Methodology § Devise a Bernoulli based deal-specific process for the prediction of event time intervals – It identifies the probability of the occurrence of events and thus helps in understanding how fast or slow a deal is moving forward – This model is used to learn the weights of deal attributes to compute the parameter of a geometric distribution for the next event occurrence time interval § Bernoulli-Dirichlet Generative Process: models the type of occurred events: win, loss, Milestone update – It is trained to learn the weights of deal attributes to compute the parameters of a stochastic process that models the type of next occurring event § Prediction – The model estimates the probability of different event types given the deal attributes X, and time interval T, i.e., probability that the given event may happen within the time interval T 44
  • 45. © 2013 IBM Corporation DEAL COMPETITIVENESS ASSESSMENT 45
  • 46. © 2013 IBM Corporation The basic premise to be used throughout the Deal Competitive Assessment is to be able to compare a given ‘Compare From’ source to available “Compare To” data sources through a standard method of peer selection, and to present the output in a standard way globally Tower/Service Scope Peer Criteria Peer Selection Criteria Compare To Sources Bid Data Market Data Delivery Data DiminishingNumberofDataSamples X1 X2 X3 X4 X5 Local Sources Compare From Sources Deal Metric Standard Global Representation Standard Model Offering Standard Models Offerings Contract Prices Pricing Deal Competitiveness Assessment 30
  • 47. © 2013 IBM Corporation Approach to Assessing Competitiveness §Mine ‘similar’ prior deals and market benchmark data §Determine the upper and lower bounds on unit costs and unit prices for each of the service involved in an IT service solution. §Add things up to get upper and lower bounds, and assess the percentile of the given case. §Create a case management solution, where: –Users can edit/add/remove services involved. –Users can see/change/add peer deals §The key challenge is in determining ‘similarity’ among complex IT service solutions. We present an approach to derive close comparables in this effort 47
  • 48. © 2013 IBM Corporation Peer-Selection Filtering § Boolean: Has global resources or not § Geographical: Where it was § Categorical: Won, lost, or either § Numerical: Quantity of services § Unstructured text: Attributes with long text descriptions, images, etc. § Timing: recent enough. 48 Tuple: {service, # of units requested, $unit cost, $ unit price, geo deliver-from, geo deliver-to} D1 s1, 200, $44 s2, 300, $2.88 s3, 2000, $555 s4, 1000, $674 cs1, N/A, 10% cs2, N/A, 20% D2 s2, 200, $3.50 s4, 3000, $500 cs1, N/A, 12% cs2, N/A, 18% D3 s1, 500, $40 s3, 1,500, $450 cs1, N/A, 15% cs2, N/A, 22% D4 s2, 200, $3.50 s4, 1500, $620 cs1, N/A, 12% cs2, N/A, 18%
  • 49. © 2013 IBM Corporation The System View of IT Service Solution Price Competitiveness Analysis 33
  • 50. © 2013 IBM Corporation Sales Pipeline Revenue Prediction Methodology Overview 50 Historical Win Conversion & Growth Data What future opportunities would come into the pipeline that will be won by the end of the period (Growth)? Wouldwewinthese opportunities(Conversion)? Non-Linear Optimization Model Linear Optimization Model Optimal Weights Optimal No. of Historical Periods to Use (N) Current Pipeline Revenue Prediction (Conversion & Growth Apply Weights on N Historical Conversion and Growth Rates Apply Rates to Current Pipeline Objective: Predicting the revenue of sales pipeline for different sales stages Aly Megahed, Peifeng Yin, Hamid Reza Motahari Nezhad:An Optimization Approach to Services Sales Forecasting in a Multi-staged Sales Pipeline. SCC 2016: 713-719
  • 51. © 2013 IBM Corporation FROM SERVICES TO COGS, AND TO COGNITIVE BPM What advances in AI and Machine Learning mean for Service Computing and BPM? 51
  • 52. © 2013 IBM Corporation Service Computing: From API to CCL § The End of using API for Programming Business Logic – APIs will be used to initiate Cogs (Intelligent Bots) – The Business Transaction to be performed in Conversations with Cogs § Cogs representing Providers/Consumers,spanning over a spectrum: – From Cogs taking over the interface of existing Apps – To Cogs codifying and understanding the business logic and engaging in conversations to transact § Cog Conversation Language (CCL) – CCL should provide support for defining a rich natural language conversations for a Cog to deliver business functionalities to the users (other Cogs, and Humans) • The Language to Program Cogs • An initial example is Watson Dialog Services Template Language 52 Source: blog.cloudsecurityalliance.org
  • 53. © 2013 IBM Corporation The notion of Service/People Composition to be Re-Defined § In current Hybrid composition/mashup (People, Services) methods: – Services are represented with API calls – People are integrated with Human Tasks (GUI is the interaction paradigm) – Composition methods are finding deterministic models of interactions, defined apriori § We are moving towards dynamic composition of cogs and human in which – Cogs are participating in NL conversations – Human are approached through messaging and natural language – Composition are performed dynamically during the conversation,require non-deterministic models, defined in online and on-demand model 53 Weather Cog Health Agent Personality Insight Cog. Provider Cogs Travel Cog 1 Travel Cog 2 Planning a Vacation Trip Considering preferences, experience, conditions, cost, Availability, etc. Mediated and facilitated by Cogs Human-Cog interaction Cog-Cog interaction Natural Language Natural Language, CCL, (ACL, KQML, etc.)? ACL: Agent Communication Language, KQML, etc.
  • 54. © 2013 IBM Corporation The App Composition (Mashup) is already moving away from explicit API calls § Implicit Data Sharing with the notion of Central Shared Context on Mobile Platforms – Events – Notifications – Metadata descriptions § Google Now on Tap (implicit integration) – Central Shared Context § Apple Proactive 54
  • 55. © 2013 IBM Corporation Process Automation Stages in Enterprise & in IT Services Humans (Manual) Program/ Workflow Robotics (RPA) Cognitive 55 Issues Current Enterprises facing • High volume of manual processes • With high variability • Involving unstructured data “85% of a typical firm’s 900+ processes can be automated.” High Cost of Automation using Traditional Approaches (to go from 50% to 85%)
  • 56. © 2013 IBM Corporation Historical and Future Perspectives on BPM 56 Databases BackendSystems Layer Self-Generating Integration SAP using java API Web Service API Excel using com API MSMQ using com or java API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service Presentation Presentation XML API BackendSystems Layer Self-Generating Integration SAP using java API SAP using java API Web Service API Web Service API Excel using com API Excel using com API MSMQ using com or java API MSMQ using com or java API Databases using jdbc API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service PresentationPresentation PresentationPresentation XML API XML API BPMS TQM General Workflow BPR BPM time ERP WFM EAI ‘85 ‘90 ‘95 ‘05‘00‘98 IT Innovations Management Concepts DatabasesDatabases BackendSystems Layer Self-Generating Integration SAP using java API Web Service API Excel using com API MSMQ using com or java API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service Presentation Presentation XML API BackendSystems Layer Self-Generating Integration SAP using java API SAP using java API Web Service API Web Service API Excel using com API Excel using com API MSMQ using com or java API MSMQ using com or java API Databases using jdbc API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service PresentationPresentation PresentationPresentation XML API XML API BPMS BackendSystems Layer Self-Generating Integration SAP using java API Web Service API Excel using com API MSMQ using com or java API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service Presentation Presentation XML API BackendSystems Layer Self-Generating Integration SAP using java API SAP using java API Web Service API Web Service API Excel using com API Excel using com API MSMQ using com or java API MSMQ using com or java API Databases using jdbc API Databases using jdbc API Business Rules Layer Production Business Level Objects Business Level Objects Inv oices Business Lev el Obj ects AFE’s Business Level Objects Anything Business Level Objects Process Layer Any Process General Workflow System and UserInteractionsCalculation Interface Layer Web Service PresentationPresentation PresentationPresentation XML API XML API BPMS TQMTQM General Workflow BPRGeneral Workflow BPR BPMBPMBPM time ERPERP WFMWFM EAIEAI ‘85 ‘90 ‘95 ‘05‘00‘98 IT Innovations Management Concepts Ref: Ravesteyn, 2007 ‘16 Social BPM iBPMS: Business Process Analytics ‘2021 The Future of BPM is also Cognitive Dark Data Cognitive BPM Cognitive Analytics Cognitive Processes Interact LearnEnact Cognitive Capabilities
  • 57. © 2013 IBM Corporation Dark Data: digital footprint of people, systems, apps and IoT devices § Handling and managing work (processes) involves interaction among employees, systems and devices § Interactions are happing over email, chat, messaging apps, and § There are descriptions of processes, procedures, policies, laws, rules, regulations, plans, external entities such as customers, partners and government agenies, surrounding world, news, social networks, etc. § The need for activities over interactions of people, systems, and IoT devices to be coordinate 57 Citizens Assistant Business Employees/ agents Plans Rules Policies Regulations TemplatesInstructions/ Procedures ApplicationsSchedules Communications such as email, chat, social media, etc. Organization Dark Data: Unstructured Linked Information IoT Devices and Sensors
  • 58. © 2013 IBM Corporation Spectrum of Work: Processes and Cognitive 58 Structured Processes Unstructured Processes Knowledge-based Routine Existing Technology Dark Data: Mobile, Social, Communication (email, voice, video), Documents, Notes, Sensors BPM Engines Workflow Engines Case Management Groupware Knowledge-Intensive Processes Email, Chat, Messaging Ad-hoc, unstructured Processes Cognitive Process Management Conversational Interface for Processes Cognitive Process Learning Cognitive Process Analytics Cognitive Enactment
  • 59. © 2013 IBM Corporation Cognitive BPM Systems § A Cognitive BPM system is a cognitive system that provides cognitive support in all phases of a process lifecycle over structured and unstructured information sources, and is able to continuously discover, learn and proactively act to support achieving a desired outcome – It offers cognitive interaction and analytics support over structured processes – For unstructured processes, it offers intelligent and integrated process (model) definition, reasoning and adaptation • Process is not assumed apriori defined; but is discovered, learned and customized based on accumulated knowledge and experience –It continually learns to improve the process 59
  • 60. © 2013 IBM Corporation Cognitive BPM Lifecycle 60 Cognitive BPMS Define Enact Monitor Analyze Next Steps, Adapt Interact Sense Learn, Discover To Traditional BPM Cognitive BPM
  • 61. © 2013 IBM Corporation Cognitively-Enabled Processes: Shifting process lifecycle from Define-Execute-Analyze-Improve to Plan-Act-Learn § For each enactment of the overall process, many iterations around this loop § At a given time, multiple goals & sub-goals may be active – Numerous threads of activity – Each thread modeled essentially as a “case” as in Case Mgmt – Cf. [Vaculin et al, 2013] § As new information arrives the cycle might re-start for some or all threads – Planning based on new info • New goal formulation • Planning to achieve those goals § “Cognitive Agent” helps by – Perform the planning – Learn from large volumes of structured/unstructured data – Over time, learn best practices and incorporate into planning Plan / Decide Act <<World Effect>> Learn Richard Hull, Hamid R. Motahari Nezhad: Rethinking BPM in a Cognitive World: Transforming How We Learn and Perform Business Processes. BPM 2016: 3-19
  • 62. © 2013 IBM Corporation Towards Cognitive BPM: Example Scenarios 62 Example (1): Integrate IBM BPM with IBM Watson http://www.ibm.com/developerworks/bpm/library/techarticles/1501_mehra-bluemix/1501_mehra.html#N1009D Email, Chat, and Calendaring apps are the most used channels for doing work in the enterprise Addressing the work organization and management for Knowledge workers: monitoring communication channels (email, chat), and: - Capturing, prioritizing and organizing work of a worker - Identifying actionable statements (requests, commitments, questions) and track them over the course of conversations Example (2): eAssistant for Knowledge Workers
  • 63. © 2013 IBM Corporation63 Inbox - Verse Highlighting actionable statements Recommending fulfilment actions IBM Insight 2015 – The session on “Given your collaboration tools a brain”
  • 64. © 2013 IBM Corporation64 IBM Insight 2015 – The session on “Given your collaboration tools a brain” Send File Action Archetype Send File Action Archetype Send File Action Archetype
  • 65. © 2013 IBM Corporation65 IBM Insight 2015 – The session on “Given your collaboration tools a brain” Invite/Calendar Action Archetype Automated Invite Parameters Extraction Calendar Entry Creation
  • 66. © 2013 IBM Corporation eAssistant App and APIs 66 Watson (& BigInsight NLP) Apps and Services on BlueMix CollaborationTools Enterprise Repositories, Applications and Data Sources Feeds Repositories Document collections … eAssistant Apps Personal Knowledge Graph Builder Conversation Analytics, Auto-Response, Prioritization Calendar and Scheduling Assistant Cognitive Process Learning To-do, Task and Process Assistant Cognitive Work Assistant APIs Semantic Role Labeling POS tagging Dependency Analysis Co-reference resolution Named Entity Recognition Knowledge Graph Builder H. R. M. Nezhad. Cognitive assistance at work. In AAAI Fall Symposium Series. AAAI Publications, November, 2015.
  • 67. © 2013 IBM Corporation Cognitive BPM: Selected research challenges § Cognitive process learning: 4Knowledge acquisition methods from unstructured information (text, image, etc.) 4Combine with traditional process mining on logs 4Building actionable knowledge graphs & executable code § Cognitively enabled processes: Plan-Act-Learn 4Blending of “model” and “instance” 4Recognizing goals from digital exhaust and process history 4Advances in planning research – incremental, multi-threaded activity, richer goal languages, prioritized and soft goals, … 4Enough uniformity to support reporting, identification of best practices § Cognitive Assistants for business processes 4Assist workers across numerous tasks, including process management & optimization 4Interactive learning where cognitive agents ask process questions 4Gradual learning through experience, and process improvement
  • 68. © 2013 IBM Corporation Summary § The Future of Computing is …. § The Future of Work is …. § The Future of Services is …. § The Future of BPM is …. § A huge, unprecedented opportunity for the research community to advance our understanding,methods and technology underpinning these transformations and disruptions! 68 Cognitive Cognitive Computing Cognitive Assistance Cognitive Services Cognitive BPM
  • 69. © 2013 IBM Corporation QUESTIONS? Thank You! 69
  • 70. © 2013 IBM Corporation Model of Human AdministrativeAssistants: conceptual framework 70 T. Erickson,etc.: Assistance:The Work Practice of Human Administrative Assistants and their Implications for IT and Organizations,CSCW’08. Blocking, Doing, Redirecting Key to the performance of Assistants
  • 71. © 2013 IBM Corporation Cognitive BPM in Cognitive Assistants/Agents § Goals – Increasing worker’s productivity, efficiency, and creativity (serendipity) § Current cognitive assistants are focused on personal space or virtual conversationalagents § Cognitive Work Agent – Is process and work aware – Monitors worker’s input channels and interactions (emails, chats, social connections,external and internal environment, knows rules, policies and processes) – Proactively acts on worker’s behalf and reacts to requests: becomes a copy of you in work environment • Commands/requests - Responds to simple requests intelligently • Situational awareness – monitors the environments to overcome information overloading (selective). • Deep QA: process questions, how-tos, previous successfulprocess experience – Organizes and assists your work • Extract tasks/commitments, promises, commitments • Managed to-dos: status updates, over-dues, plans • Manages calendar, schedules,social contacts • Finds and present prior related interactions to a particular conversation – Learns how work gets done, and can take care of them for their human subject 71
  • 72. © 2013 IBM Corporation Cognitive Assistant § A software agent (cog) that – “augments human intelligence” (Engelbart’s definition1 in 1962) – Performs tasks and offer services (assists human in decision making and taking actions) – Complements human by offering capabilities that is beyond the ordinary power and reach of human (intelligence amplification) § A more technical definition – Cognitive Assistant offers computational capabilities typically based on Natural Language Processing (NLP), Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that augment and scale human intelligence § Getting us closer to the vision painted for human-machine partnership in 1960: – “The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information handling machines we know today” “Man-Computer Symbiosis , J. C. R. Licklider IRE Transactions on Human Factors in Electronics, volume HFE-1, pages 4-11, March 1960 72 1 Augmenting Human Intellect: A Conceptual Framework, by Douglas C. Engelbart, October 1962