Wondering how and why forward-thinking businesses are already adopting cognitive computing and artificial intelligence technologies? Curious about the top business challenges organizations are tackling with cognitive computing? The "IBM Cognitive Study," which surveyed 600 leaders and decision-makers from around the world, provides answers to these questions and more. About 70% of decision-makers say that cognitive computing is extremely important to their business strategy and success. Learn how smart companies are becoming cognitive businesses, and how they're already driving tangible results and ROI.

1. The cognitive advantage
Insights from early adopters on driving business value
© 2016www.ibm.com/cognitive/advantage-reports/

2. 2
The cognitive era is now
Organizations are using cognitive technology to outthink the market–
unlocking new digital intelligence from large volumes of data.
The cognitive computing market is now on an exponential growth curve,
expected to grow from $2.5 billion in 2014 to more than $12.5 billion by 2019.
Within the next two years, it is expected that half of all consumers will interact
with cognitive technology on a regular basis.

3. To understand how organizations are capitalizing on the potential of
cognitive computing and to uncover emerging patterns of adoption,
we surveyed more than 600 cognitive decision makers worldwide who
already have or are planning cognitive initiatives.
3

4. 4
In this study, cognitive computing/artificial intelligence (AI) refers to computer-based, intelligent technologies that analyze data and interpret
information to generate hypotheses, formulate possible answers to questions, or provide recommendations and predictions. These technologies
learn and reason as a result of their interactions.
We garnered insights from more than 600 cognitive decision makers
worldwide, cross-industry, from IT to line of business, at various stages of cognitive adoption.
Cognitive early adopters
Advanced users | 22% of respondents
Using 2 or more cognitive technologies for more than a year
Beginners | 54% of respondents
Using cognitive technologies for less than a year or using 1 technology for
more than a year
Planners | 24% of respondents
Planning to adopt cognitive technologies within 2 years
About the study
54%
24%
22%

5. 5
Organizations already gain major competitive
advantage from their use of cognitive computing. They
achieve a range of business outcomes–from customer
engagement to productivity & efficiency and business
growth.

6. 6
say cognitive
computing is
essential to digital
transformation
say adopting cognitive
is very important to
their organization’s
strategy and success
Early adopters see cognitive computing
as a key differentiator
of users say outcomes
from cognitive
initiatives exceed
their expectations
65% 58% 62%

7. They consider cognitive to be a key ingredient of their strategy
to increase competitive advantage
7
50%
of users say they already gain major
competitive advantage from their
cognitive initiatives
58%
of early adopters regard cognitive
computing as a “must have” for
organizations to remain competitive
within the next few years

8. Patterns of adoption are emerging as organizations
kickstart cognitive initiatives
Functional patterns
Functional areas of the
business where cognitive
initiatives are being used
or planned
Goal-based patterns
Business-need or goal-based
use-cases where cognitive
initiatives are being used or
planned
Technology patterns
Technologies currently being
used or planned in cognitive
initiatives
1 2 3

9. IT, Data Analytics and Customer Service are common entry points
9
Advanced Users Beginners Planners
Already using Planning on using Already using Planning on using Planning on using
40%
41%
42%
44%
47%
48%
48%
50%
51%
59%
66%
48%
45%
41%
44%
35%
42%
43%
41%
38%
36%
23%
Marketing
Sales
Communications/PR
Product Development
Human Resources
Finance
Corporate strategy & management
Operations
Customer Service
Data Analytics
IT
23%
23%
19%
16%
18%
25%
20%
24%
24%
37%
39%
47%
47%
46%
56%
47%
42%
52%
53%
48%
46%
47%
58%
48%
38%
47%
31%
34%
53%
60%
53%
69%
70%
Functional patterns:1

10. 47%
46%
40%
42%
47%
47%
35%
38%
49%
40%
50%
34%
42%
41%
47%
51%
43%
44%
40%
43%
44%
38%
38%
39%
43%
40%
38%
38%
47%
40%
42%
38%
37%
40%
58%
57%
54%
60%
41%
49%
40%
35%
63%
41%
65%
44%
44%
43%
51%
57%
60%
10
Advanced Users Beginners Planners
Already using Planning on using Already using Planning on using Planning on using
2
Product & service innovation and IT automation are common use cases
Goal-based patterns:
60%
61%
62%
64%
65%
65%
65%
65%
66%
69%
70%
70%
70%
72%
73%
73%
77%
30%
34%
29%
24%
27%
25%
20%
24%
26%
20%
26%
21%
22%
19%
23%
20%
19%
Security & compliance
Customer service
Customer behavior & sentiment analysis
Sales & marketing optimization
Asset management
Personalized advice & recommendations
Research & discovery
Intelligent virtual assistants
Cloud management
Smart machines
Performance & quality management
Self-paced personalized learning
Procurement & supply chain operations
Decision support systems
Business process automation
IT automation
Product & service innovation

11. 47%
49%
74%
66%
73%
73%
76%
24%
28%
38%
34%
42%
48%
51%
44%
42%
47%
47%
45%
40%
35%
11
Advanced Users Beginners Planners
Already using Planning on using Already using Planning on using Planning on using
3
A variety of capabilities are being used in cognitive initiatives
Technology patterns:
58%
68%
75%
77%
79%
80%
84%
19%
21%
20%
15%
20%
15%
13%
Intelligent robotics
Social and emotional (affective) computing
Natural language processing (NLP)
Machine learning
Knowledge representation and reasoning
Pattern recognition
Automated scheduling and planning

12. Users achieve a range of outcomes via their
cognitive initiatives–customer engagement, productivity & efficiency,
and business growth
12
Customer Engagement
49% Improved customer
service
49% Personalized customer /
user experience
43% Increased customer
engagement
42% Enabled faster response
to customer / market
needs
Productivity & Efficiency
49% Improved productivity
& efficiency
46% Improved decision making
& planning
46% Improved security &
compliance, reduced risk
45% Reduced costs
42% Enhanced the
learning experience
Business Growth
42% Expanded ecosystem
41% Expanded business
into new markets
39% Accelerated
innovation of new
products / services
% achieving with cognitive

13. Top outcomes from cognitive initiatives vary by industry
Finance
49% Increased market agility
46% Improved customer service
43% Increased customer
engagement
43% Improved productivity &
efficiency
42% Improved security &
compliance, reduced risk
Retail
56% Personalized customer / user
experience
56% Increased customer engagement
56% Improved decision making &
planning
56% Reduced costs
55% Improved customer service
Health
66% Accelerated innovation of
new products / services
66% Improved productivity &
efficiency
64% Improved security & compliance,
reduced risk
62% Reduced costs
59% Improved customer service
Manufacturing
64% Improved decision making
& planning
58% Improved productivity &
efficiency
54% Improved security &
compliance, reduced risk
52% Improved customer service
49% Enhanced the learning
experience
Government/Education
54% Personalized customer / user
experience
50% Improved customer service
37% Improved decision making &
planning
36% Improved productivity & efficiency
33% Increased customer engagement
Professional Services
40% Reduced costs
36% Personalized customer/user
experience
36% Improved customer service
36% Expanded ecosystem
34% Accelerated innovation of new
products / services
% achieving outcome with cognitive

14. Cognitive efforts are being driven both
top-down and bottom-up
14
% citing as major driver
51%
48%
47%
47% 46%
35%
Executive mandates
Competitor actions
Developer experimentation
with cognitive
Business user
experimentation
with cognitive External
customer demand
Personal use
of cognitive

15. IT and Line of Business collaborate on cognitive decision making,
with technology leaders serving as the primary advocates
15
Collaboration underpins cognitive initiatives Strongest advocates for cognitive initiatives
% citing as strong advocate
45%
26%
29%
45% IT and LoB
in collaboration
29% More LoB driven
26% More IT driven
43% Chief Technology Officer (CTO)
43% Chief Information Officer (CIO)
43% IT Management below C-level
27% Chief Data Officer (CDO)
25% LoB Management below C-level
23% CEO/President
16% Chief Marketing Officer (CMO)

16. 46% say that while their organization sees the value in cognitive computing,
they struggle with a roadmap for adoption
While these organizations view cognitive as essential,
many still struggle with strategy and an adoption roadmap
16
Organizational approach to cognitive
7%
41%
40%
12%
Comprehensive,
company-wide strategy
More tactical
than strategic
Developing
broader strategy
No strategy yet

17. Top adoption challenges include
the cost of technology and security concerns
17
62% 57% 55%
54%54%Cost of technologies /
solution development
Security concerns Immature technologies
and tools for implementing
cognitive solutions
Data issues (i.e., quality of data,
integrating and converting data,
volume of data)
Insufficient skills
Top five challenges in adopting cognitive computing

18. Top adoption challenges vary by industry
% citing this adoption challenge
Finance
60% Cost of technologies / solution
development
54% Security concerns
53% Immature technologies and
tools for implementing
cognitive
53% Fragmented efforts across our
enterprise
53% Insufficient skills
Retail
58% Immature technologies and tools
for implementing cognitive
58% Cost of technologies / solution
development
56% Security concerns
53% Insufficient skills
53% Data issues (i.e., quality of data,
integrating and converting data,
volume of data)
Health
62% Cost of technologies /
solution development
59% Difficulty justifying the
investment
59% Security concerns
54% Insufficient skills
53% Immature technologies and
tools for implementing cognitive
Manufacturing
67% Immature technologies and
tools for implementing cognitive
63% Cost of technologies / solution
development
59% Data issues (i.e., quality of data,
integrating and converting data,
volume of data)
57% Fragmented efforts across our
enterprise
57% Difficulty justifying the
investment
Government/Education
62% Insufficient skills
61% Security concerns
59% Cost of technologies / solution
development
58% Difficulty protecting our organization’s
proprietary intellectual capital
57% Difficulty justifying the investment
Professional Services
60% Cost of technologies / solution
development
47% Data issues (i.e., quality of data,
integrating and converting data,
volume of data)
45% Immature technologies and tools
for implementing cognitive
43% Security concerns
42% Difficulty protecting our
organization’s proprietary
intellectual capital

19. Extensive skills gaps exist for software developers and cognitive experts,
posing a challenge for cognitive projects
19
63% Computer Scientists (e.g. experts in cognitive computing/AI including machine
learning, knowledge representation and other cognitive/AI techniques)
58% Software developers who code/implement cognitive applications and systems
57% Data experts (e.g., Data scientists, Data analysts)
58% Domain experts (Subject matter experts with skills and expertise to train
cognitive systems)
53% IT professionals focused on infrastructure, cloud, networking, etc.
% citing moderate-to-major skills gap

20. An ecosystem of experts, including technology and consulting companies,
helps organizations with cognitive initiatives
20
To provide / build
product components
To influence IT
directions/decisions
To train staff
43%
Consulting
Companies
External
Developers
Developer
Communities
Industry
Analysts
Clients AcademiaTechnology
Companies
29%
34%
37%
30%
32%
36%
25%
30%
32%
20%
33%
35%
31%
27%
31%
25%
25%
26%
22%
29%
% using partner for this activity

21. Cognitive early adopters take a holistic view of IT, with cloud,
analytics and security enabling the cognitive era
21
9 in 10 say each of
these will play an important
role in their cognitive
initiatives within 2 years:
• Cloud
• Big data & analytics
• Mobile
• Security
85% say Internet
of Things will play an
important role in their
cognitive initiatives
within 2 years

22. 22
Cloud-based services are preferred to access
and use cognitive capabilities
55% Favor cloud-based services (cognition-as-a-service)
over non-cloud
32% Have an equal mix of cloud and non-cloud
10% Favor non-cloud over cloud-based services
Cloud is the primary platform of choice for these
organizations to drive cognitive projects
55%
10%
32%

23. Both SaaS and PaaS are leveraged
for developing and deploying cognitive initiatives
23
53%
of users access
cognitive technology via
Software-as-a-Service
51%
of users access
cognitive technology via
Platform-as-a-Service

24. Cognitive early adopters expect to make significant use of
open source technology to support their cognitive initiatives
54%
of cognitive early
adopters already use or expect
to make heavy use of open
source technology to support
cognitive initiatives
24
74%
of developers expect to make
heavy use of open source
technology

25. Cognitive users rely on diverse types and sources
of data for their initiatives
Sources of data:
51% use internal company data
48% use external data
43% use shared industry data
In future, over 90% plan to use
all of these
Kinds of data:
62% structured data
vs.
38% unstructured data

26. 26
Cognitive early adopters
are analytically mature organizations
Analytical capabilities used within the organization
Descriptive analytics
(i.e., historic data, event data)
Advanced analytics
(predictive/prescriptive analytics i.e., sophisticated
intelligence and modeling to recommend next
steps or actions)
75%61%
28%

27. 27
Early adopters unlock insights by
applying cognitive technology to untapped data
60%
say cognitive computing is
essential to tackling data
challenges that
conventional analytics
cannot
53%
say cognitive computing will
unlock the hidden value of
their organization’s dark
data

28. Chart your cognitive roadmap
28
Team for success
Encourage your IT and business leaders to
collaborate on the organization’s cognitive
initiatives.
Enlist a team of cognitive, software
development and data specialists to
implement and manage cognitive pilots and
supplement your in-house expertise through
your ecosystem of partners.
Advance your data strategy
The success of your cognitive initiative will
depend on the volume and quality of data at
your disposal.
Consider leveraging a diverse range of
untapped data sources based on your
business need—from structured to
unstructured, and from internal to external
sources.
Choose your on-ramp
Determine your starting point for cognitive by
considering your organization’s needs and
capabilities.
Target a use case with a strategic goal and
data to support it.
Will you pursue enterprise-wide
transformation, or improve a specific
business process?

29. APPENDIX
29

30. Cognitive users already gain
major competitive advantage and business results
30
65% of cognitive early adopters say adopting cognitive is
very important to their organization’s strategy and
success
58% of cognitive early adopters say cognitive computing is
essential to digital transformation
50% of cognitive users say they already gain major
competitive advantage from their cognitive initiatives
Patterns of adoption are emerging among advanced cognitive users
Cognitive early adopters take a holistic view of IT: 9 of 10 say cloud, analytics, mobile and security will each play an important role in cognitive
initiatives within 2 years
53% say cognitive computing will unlock the hidden value of their organization’s dark data
While IT and LoB collaborate on cognitive decision making, technology leaders are the key advocates
EXECUTIVESUMMARY
1. Functional patterns
• IT
• Data Analytics
2. Goal-based patterns
• Product and service innovation
• IT Automation
3. Technology patterns
• Automated scheduling and planning
• Pattern recognition
Cognitive users achieve a range of outcomes via their
cognitive initiatives:
Customer Engagement
49% Improved customer service
Productivity & Efficiency
49% Improved productivity & efficiency
Business Growth
42% Expanded ecosystem

31. About the study respondents
31
Geography 33% 17% 16% 14% 10% 10%
United States China India Japan Germany United Kingdom
Finance Retail Healthcare Manufacturing Government/
Education
Professional
Services
Other
24% 10% 11% 22% 9% 10% 14%Industry
To smooth possible geographic distortions, responses were weighted based on an IBM assessment of each country’s total IT spend.
40%
10%
10%
16%
24%
Respondents by role
Line of business
Manager
Non Manager
C-Level
Corporate
Executive
Director
55%
45%
Organization Size
100-999
employees
46% 54%
1,000+
employees
IT respondents

32. 32
MD Anderson Cancer Center (Healthcare)
Solution that aggregates large volumes of unstructured patient data from a variety of sources, enabling clinicians and researchers to run analytics in
near-real time to identify patterns and gain important insights.
The Chinese University of Hong Kong (Healthcare)
Cloud-based solution that enables research teams to accelerate their exploration of large, complex cancer data sets and identification of cancer
trends.
Baylor College of Medicine (Education)
Cognitive technology is used to accelerate medical research by analyzing over 300,000 articles automatically, looking for key words and phrases that
indicate correlations among studies.
Guiding Eyes (Education)
Solution that advances the art and science of raising guide dogs by analyzing structured and unstructured data to discover genetic, health,
temperament and environmental factors that correlate with success.
Alpha Modus (Financial Markets)
Cloud-based solution that can analyze large volumes of unstructured and natural language data from financial services industry sources, including
stock exchanges, social media, and other market indicators, making it easy to explore new ideas and turn them into practical investment applications.
WayBlazer (Travel & Transportation)
Travel application and APIs that use analyze unstructured social media content from thousands of sites and deliver personalized recommendations
to travelers who issue natural-language questions.
Honest Cafe (Retail)
Analytics are used to explore vending machine transactions, payments and weather data, find correlations, and uncover patterns in customer
behavior.
Pon Holdings (Automotive, Industrial Products)
Solution that extracts and analyzes unstructured data from millions of web pages, using the insights to generate leads for sales and marketing
departments and also to improve pricing.
Cognitive client stories

33. Lead Analysts:
Susanne Hupfer Susanne_Hupfer@us.ibm.com
Cynthya Peranandam cynthya@us.ibm.com
Contributors:
Lindsey Reichelt, lreichelt@us.ibm.com Ellen Cornillon, ellencor@us.ibm.com Brandon Buckner, bsbuckne@us.ibm.com
ibm.com/cognitive/advantage-reports
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