the cognitive advantage: insights from early adopters on driving business value

The cognitive advantageInsights from early adopters on driving business value

© 2016www.ibm.com/cognitive/advantage-reports/

http://www.ibm.com/cognitive/advantage-reports/

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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.

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.

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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 respondentsUsing 2 or more cognitive technologies for more than a year

Beginners | 54% of respondentsUsing cognitive technologies for less than a year or using 1 technology for

more than a year

Planners | 24% of respondentsPlanning to adopt cognitive technologies within 2 years

About the study

54%

24%

22%

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

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%

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

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

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

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



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

47%

49%

74%

66%

73%

73%

76%

24%

28%

38%

34%

42%

48%

51%

44%

42%

47%

47%

45%

40%

35%

11



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

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

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



Retail

56% Personalized customer / user

experience

56% Increased customer engagement

56% Improved decision making &

planning

56% Reduced costs


Health

66% Accelerated innovation of

new products / services


efficiency

64% Improved security & compliance,

reduced risk

62% Reduced costs


Manufacturing

64% Improved decision making

& planning


efficiency




49% Enhanced the learning

experience

Government/Education

54% Personalized customer / user

experience


37% Improved decision making &

planning

36% Improved productivity & efficiency

33% Increased customer engagement

Professional Services

40% Reduced costs

36% Personalized customer/user

experience


36% Expanded ecosystem

34% Accelerated innovation of new

products / services

% achieving outcome with cognitive

Cognitive efforts are being driven both top-down and bottom-up

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% 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

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)

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

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Organizational approach to cognitive

7%41%

40%12%

Comprehensive,

company-wide strategy

More tactical

than strategic

Developing

broader strategy

No strategy yet

Top adoption challenges include the cost of technology and security concerns

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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

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


development



53% Data issues (i.e., quality of data,


volume of data)

Health

62% Cost of technologies /

solution development

59% Difficulty justifying the

investment




tools for implementing cognitive

Manufacturing


tools for implementing cognitive


development



volume of data)

57% Fragmented efforts across our

enterprise

57% Difficulty justifying the

investment

Government/Education




development

58% Difficulty protecting our organization’s

proprietary intellectual capital

57% Difficulty justifying the investment

Professional Services


development



volume of data)

45% Immature technologies and tools

for implementing cognitive


42% Difficulty protecting our

organization’s proprietary

intellectual capital

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

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 Academia Technology

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

Cognitive early adopters take a holistic view of IT, with cloud, analytics and security enabling the cognitive era

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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

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%

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

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

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

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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

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

Chart your cognitive roadmap

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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?

APPENDIX

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Cognitive users already gain major competitive advantage and business results

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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

EX

EC

UT

IVE

SU

MM

AR

Y

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 Engagement49% Improved customer service

Productivity & Efficiency49% Improved productivity & efficiency

Business Growth42% Expanded ecosystem

About the study respondents

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Geography 33% 17% 16% 14% 10% 10%

United States China India Japan Germany United Kingdom

Finance Retail Healthcare Manufacturing Government/

EducationProfessional

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 ManagerC-Level

Corporate

Executive

Director

55%

45%

Organization Size

100-999

employees

46% 54%

1,000+

employees

IT respondents

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

http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=H447240O66679Z38

http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=O958877N40135K95

http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=F713551X77664U56

http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=L723318A82088E14

http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=E224227U13112J55

http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=G754268S52509L83

http://www-03.ibm.com/software/businesscasestudies/rs/sr?synkey=K035506F65847B94

http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=R910947U26232O03

Lead Analysts:

Susanne Hupfer [email protected]

Cynthya Peranandam [email protected]

Contributors:

Lindsey Reichelt, [email protected] Ellen Cornillon, [email protected] Brandon Buckner, [email protected]

ibm.com/cognitive/advantage-reports

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