straight talk about ai and big data: what is real? what is … · 2018-05-22 · hype: ai is very...

MAKING AI WORK FOR YOUR INDUSTRY

Straight Talk about Ai and Big Data:What is Real? What is Marketing Hype? April, 2018

© CognitiveScale 2017. All Right Reserved. | Confidential | Do Not Use Without Permission

Manoj Saxena

Executive Chairman, CognitiveScaleFormer GM, IBM Watson

Agenda

1. What is happening with AI and Blockchain? Why is it important?

2. How much of this is real? How much is marketing hype?

3. Where are some of the real life, high value deployments?

4. How should one go about exploiting these opportunities?

© CognitiveScale. All Right Reserved. | Confidential 2

New ParadigmCusp of a dramatically different world

brought on by intelligent machines

New Leaders, Many Walking DeadWinners will focus on practical and responsible

outcomes with AI, not mere tech or algos

Exponential OpportunityA “Digital Renaissance” is underway and will create significant change in order of things

Three key takeaways


Definition: What is Artificial Intelligence?


“AI is the science and

engineering of making intelligent

computer programs/machines

that learn from patterns”

AI & Machine Intelligence

Technologies

In 2017 AI became the new frontier for Digital Transformation

AI is already disrupting everything from coffee to cancer

© CognitiveScale 2017. All Rights Reserved | Confidential | Do Not Use Without Permission

What most people think of AI


What we think of AI

AI = Augmented Intelligence(Man + Machine and NOT Man vs Machine)


Highest value is gained when systems of engagement and systems of record together deliver insight

5

Augmentation is a much larger opportunity than automation

• Create entirely new experiences and business models for new revenues

• Augment capabilities of knowledge workers and create new revenues

• Replace knowledge labor and reduce overall costs

• Replace transactional labor and reduce overall costsEnterprise

Buyers ServiceProviders

Consultants/Advisors


Agenda






Where is AI implementation today in the Enterprise?

AI = Artificially Inflated

AI = Amazing Innovations


Hype: AI is very poorly understood and implemented

1. AI is going to take jobs away Opportunity for augmenting jobs is way larger than replacement of humans with AI. 8m vs 1.2 billion.

2. Big Data and Analytics are AI These are used in AI. Similar to senses. Sensing more does not automatically make you more intelligent.

3. NLP, Machine Learning and Deep Learning are AIThese are just tools for complex pattern recognition. Like equating a fuel pump to a car.

4. Robotic Process Automation (RPA) is AI RPA handles rule-based work and structured data inputs and not judgement-based work with unstructured data.

5. Data Science platforms alone can achieve great results “Dots vs Bubbles”. Large gap in bridging data science workflow with software devops workflows.

6. Enterprise AI can be an opaque Black box 99+% of AI startups operate AI as a Black box. Their AI is not explainable and not compliance ready


Imagine: AI powered Healthcare Care Management

13

Busin

ess V

alue

Prescriptive

Predictive

Deductive

What could happen next?

What is the best course of action?

Workflows that learn and act continuously?

DiagnosticWhat does that mean?

Descriptive

What happened? 90%

of B

usin

esse

sar

e fo

cuse

d he

reCO

GNI

TIVE

CLO

UD

OPP

ORT

UNIT

Y SP

ACE

Pollen.com Tweet Tue, 10:00 CT

Ragweed, 81832

5 Ragweed variants17 children at risk

3 admitted <90 days

4 will end up in ER1 w/o reimbursement

a) Parents reminderb) School Nursec) Mail Inhalerd) Parents Uber

Wed AM:How many affectedWhich option best?Update knowledge

* No dependence on IBM Watson. Watson APIs can be orchestrated using Cortex OS based on Ai requirements.

1. KNOW MEMy history, my

environment, goals.

2. ENGAGE MEContextual insights with

evidence.

3. LEARN FROM MEActions and outcomes, deeper profile of one.

AI Powered Care Delivery Process


Imagine: AI powered P&C Insurance Claims Process

14

Busin

ess V

alue

Prescriptive

Predictive

Deductive

What could happen next?

What is the best course of action?

How can we learn and act continuously?

Diagnostic

What does that mean?

Descriptive

What happened? 90%

of B

usin

esse

sar

e fo

cuse

d he

reCO

GNI

TIVE

CLO

UD

OPP

ORT

UNIT

Y SP

ACE

© CognitiveScale 2017. All Right Reserved. | Confidential | Do Not Use Without Permission

Weather.com update Fri, 4:30 CT

Category 4, 81832

2,239 homes at risk1,934 are customers Loss Ratio increase

853 level 4 damageContractor shortage12% Customer churn

a) Fly Drone, wire $b) On site adjustersc) Identify contractor

Sat AM:Drone/adjuster dataHow many affectedWhich option best?Update knowledge

* No dependence on IBM Watson. Watson APIs can be orchestrated using Cortex OS based on Ai requirements.

1. KNOW MEMy history, my

environment, goals.

2. ENGAGE MEContextual insights with

evidence.

3. LEARN FROM MEActions and outcomes, deeper profile of one.

InsuranceAI Powered Process

AI and digital are powering powerful business model disruption

• World’s largest taxi company has no taxis (Uber)

• Largest accommodation provider owns no real estate (Airbnb)

• Largest phone companies own no telco infra (Skype, WeChat)

• World’s most valuable retailer has no inventory (Alibaba)

• One of the largest banks holds no cash (Bitcoin)

• World’s largest movie house owns no cinemas (NetFlix)

These are ALL technology companies that engage customers brilliantly!

© CognitiveScale. All Right Reserved. | Confidential15

Disruption at work: Unbundling of Banks

29

HERE’S$WELLS$FARGO$UNDER$ATTACK

Source:%CB%Insights%P Disrupting%Banking:%The%FinTechStartups%That%Are%Unbundling%Wells%Fargo,%Citi%and%Bank%of%America

Cloud

Social

Mobile

Big Data

Analytics A.I.

MILLENNIALS

Source: CB Insights


Disruption at work: Unbundling of Consumer Packaged Goods

28

AND$LOTS$OF$INCUMBENTS$LEFT$TO$UNBUNDLE.$THIS$IS$AN$EXAMPLE$OF$P&G.

Source:%CB%Insights%P Disrupting%Procter%&%Gamble:%The%Startups%Unbundling%P&G%and%the%Consumer%Packaged%Goods%Industry

Cloud

Social

Mobile

Big Data

Analytics A.I.

MILLENNIALS


And it is occurring at an accelerated pace

Pokemon Go à 14 days

Agenda






Our Case Studies: We have delivered compelling business outcomes

20

Data

stru

ctur

e

Five AI Entry Points

Natural Language Processing

Document Processing

Image Processing

Audio Processing

Video Processing

- 31 % Trouble Tickets

+23 % Conversion lift

Call Center Reg Compliance

+ 14x Super Bowl Video Ad Features

Text

Documents

Images

Audio

Video

$36M in new efficiencies

Data KnowledgeProcess Customer Product

Cortex 5

Cortex 2

Cortex 4

Source: CognitiveScale

Agenda






Step 1: Improving understanding of AI as a Strategic Capability

• Business led discussions about potential outcomes• Demonstration days with internal and external booths• Discovery sessions for target use case generation

• Build target use case portfolio• Set Budgets ($300k-$500k/90 day sprints)• Engage, learn, and transfer skills

• COE to run parallel projects• Decide Build vs Buy vs Partner mix• On Prem vs On demand managed• Spin offs for new business lines?

EDUCATE SCALEACTIVATE

23

Step 2: Build an AI Heat Map for your business


Engagement/RetentionLeverage interactions across channels to deliver targeted,

contextually relevant messages to advisors, agents, & clients

Cross-Sell/Up-SellProfile-of-One Personalization

based on declared, observed, & inferred preferences

Product InnovationDetermine optimal adjustments to product

based on propensity to spend, demographics, regulations, & macro trends

Customer AcquisitionAI powered customer engagement

based on profile of one to drive personalized offers and onboarding experience, increasing conversion

Customer ExperienceTransform customer facing

experiences with AI-powered insights and guidance

1st Line SupportGenerate insights for

Advisors/Agents from customer interactions, with evidence to

speed issue resolution

CollectionsProactively identify potential bad debt,

generate early reasons-to-save the situation, increasing retention

ComplianceGain automated visibility into IT

assets and automated assurance of compliance across the firm

Sample AI Heat map

Source: CognitiveScale

Summary: How do I go about capitalizing on AI?

1. Start top down with business outcomes Focus on business outcomes around a targeted “heat map”, deliver incremental capabilities

2. Build a culture of “learn fast and pivot” Bad things absolutely will happen. Plan and build for that.

3. Pay for success vs Pray for successMake small bets that deliver in weeks, no big-bang deployments, doubt vendor marketing claims

4. Lay Your Company’s AI Business foundation Run three parallel tracks of Production, Innovation, Center of Excellence for Skills/Processes

5. Partner Smartly Build a AI ecosystem not just an IT platform. Partner with both nimbler and large companies


Summary: AI is your next competitive frontier

Source: IBV 2017 Study of 1,400 CEO, CFO, CMOs

19McKinsey Global Institute Artificial intelligence: The next digital frontier?

These forces will help determine the industries that AI is likely to transform the most. However, if current trends hold, variation of adoption within industries will be even larger than between industries. We expect that large companies with the most digital experience will be the first movers because they can leverage their technical skills, digital expertise, and data resources to develop and smoothly integrate the most appropriate AI solutions.

•••

After decades of false starts, artificial intelligence is on the verge of a breakthrough, with the latest progress propelled by machine learning. Tech giants and digital natives are investing in and deploying the technology at scale, but widespread adoption among less digitally mature sectors and companies is lagging. However, the current mismatch between AI investment and adoption has not stopped people from imagining a future where AI transforms businesses and entire industries. In the next chapter, we explore the four major ways in which AI can create value across the value chain in different sectors.

Exhibit 4

Sectors leading in AI adoption today also intend to grow their investment the most

SOURCE: McKinsey Global Institute AI adoption and use survey; McKinsey Global Institute analysis

10

13

5

2 84

10

146 12 16

11

6

8

18 24 30

12

200

2

4

22 28

9

26 32

3

7

1

0

Current AI adoption% of firms adopting one or more AI technology at scale

or in a core part of their business, weighted by firm size2

Automotiveand assembly

Energy and resources

Health care

Media and entertainment

Education

Retail

Travel and tourism

Future AI demand trajectory1

Average estimated % change in AI spending, next 3 years, weighted by firm size2

High tech andtelecommunications

Construction

Professional services

Transportation and logistics

Consumer packaged goods

Financial services

1 Based on the midpoint of the range selected by the survey respondent.2 Results are weighted by firm size. See Appendix B for an explanation of the weighting methodology.

Falling behind

Leading sectors
