Using AI and Data to create business advantage · AI is already disrupting everything from coffee to cancer ... KNOW ME My history, my environment, ... How can we learn and act continuously?

MAKING AI WORK FOR YOUR INDUSTRY @manojsaxena

Using AI and Data to create business advantage

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

Manoj Saxena

Chairman, CognitiveScale

Founding MD, The Entrepreneurs Fund

Former General Manager, IBM Watson

April, 2018

@manojsaxena

Outline

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

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

3. Why is AI being seen as a strategic business capability?

4. What are some good CxO practices? Approach to AI, Data, Ethics?

5. Discussion

©CognitiveScale.AllRightReserved.|Confidential 2

@manojsaxena

New ParadigmCusp of a dramatically different world

brought on by intelligent machines

New Game. New Rules.‘Collab-oratories’ across industry, government,

academia. New AI and Data Regs & Ethics

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

Three key takeaways


@manojsaxena

Definition: What is Artificial Intelligence?


“AI is the science and engineering of making intelligent

computer programs/machines that learn from patterns”

AI&MachineIntelligenceTechnologies

@manojsaxena

In 2017 AI became the new frontier for Digital Transformation

“AI will be as

transformative

to human kind as

fire and electricity”

Google CEO

“Human-AI partnership

can help solve society’s

challenges and release

human creative potential”

Microsoft CEO

“In five years every

decision will be impacted

by Cognitive Computing”

IBM CEO

@manojsaxena

AI is already disrupting everything from coffee to cancer

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

@manojsaxena

What most people think of AI


@manojsaxena

What we think of AI

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


@manojsaxena

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


@manojsaxena 10©CognitiveScale.AllRightReserved.|Confidential

HardwiredNon-learning

Systems

AdaptiveSystems

Human in the Loop No Human in the Loop

Augmented intelligence: AI systems that help people make better decisions and actions while learning continuously.

Assisted intelligence: AI systems that assist humans. Hard wired, No learning. E.g. Rules based software

Autonomous intelligence: AI systems that can act and adapt autonomously without human assistance. E.g. Driverless cars

Automated Intelligence: AI systems that replace humans by automation of manual or cognitive tasks that are routine or non-routine. No learning from interactions. E.g. Robotic Process Automation

Our focus: Augmenting Human Intelligence by pairing Humans+Machines

@manojsaxena

Our Customers

@manojsaxena

AUGMENTEDINTELLIGENCE

Systems that pair humans and machines to AUGMENT and extend human

cognitive functionsAI powered Business Processes and Insights

How do these various categories relate to one another?


Source: CognitiveScale Enterprise Software Continuum

Analyzing and visualizing business information

Building ML algorithms that detects patterns, anomalies

Building self-learning and self-assuring processes, new products/businesses

Bus

ines

s va

lue

AI + BlockchainData Analytics

COGNITIVECOMPUTING

Intelligent machines that MIMIC the human brain

AI Robots,Smart Devicese.g. Alexa, Siri

MACHINE LEARNING

Systems that automatically SPOT PATTERNS in large amounts of data

Machine LearningToolkits, Algorithms

Systems that ANALYZElarge, multi-structured data

sets

BIG DATAANALYTICS

DataLakes

BUSINESS INTELLIGENCE

Systems that analyze and visualize structured dataData

Warehouses

RAW DATA

@manojsaxena

Agenda

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


3. Why is AI being seen as a strategic business capability? Examples?


5. Discussion


@manojsaxena

AI has become the new frontier for digital transformation

Cloud

Social

Mobile

Big Data

Analytics A.I.

MILLENNIALS

Machine Learning

Cloud

IOT

Social

Big Data

Blockchain

AI PoweredBusiness

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

@manojsaxena

Where is AI implementation today in the Enterprise?

AI = Artificially Inflated

AI = Accelerated Innovations


@manojsaxena

Hype: AI is very poorly understood and implemented

1. AI is going to take my job 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


@manojsaxena

How does AI create value in the Enterprise?


©&2015&Forrester&Research,&Inc.&Reproduction&Prohibited 10

Continuous$learning

Digital$insights

What$you$need$is$a$link$between$data,$insight$and$action

All&possible&data

All&possibleactions

Rightdata

Effectiveactions

Source:&April&27,&2015,&“Digital&Insight&Is&The&New&Currency&Of&Business”&Forrester&report

AI = Self learning, Blockchain = Self assuring

“Note to CEOs: AI is too important to be left to just technologists”

@manojsaxena

Imagine: AI powered Healthcare Care Management

18

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


@manojsaxena

Imagine: AI powered P&C Insurance Claims Process

19

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?

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

© 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

@manojsaxena

Agenda





5. Discussion


@manojsaxena

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.AllRightReserved.|Confidential21

@manojsaxena

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


@manojsaxena

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

@manojsaxena©CognitiveScale.AllRightReserved.|Confidential 24

And it is occurring at an accelerated pace

Pokemon Go à 14 days

@manojsaxena

Agenda





5. Discussion


@manojsaxena

Good practices for capitalizing on AI and Data

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


@manojsaxena

Three Barriers to Enterprise AI Adoption

Lack of c-suite understanding and support for AI as a business capability

Unclear use cases and scaling strategy for AI in the Enterprise

Unable to align and scale Data Science and AI DevOps


@manojsaxena

How do I improve 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

@manojsaxena

Step 2: Activate using these good practices

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 down your AI Business foundation, Ethical AI Framework Run three parallel tracks of Production, Innovation, Center of Excellence for Skills/Processes

5. Evaluate and build Partner ecosystemsBuild a AI ecosystem not just an IT platform. Partner with both nimbler and large companies


MAKING AI WORK FOR YOUR INDUSTRY @manojsaxena

Step 2: Evaluate and understand AI Vendor Landscape

• AI lifecycle management with Composability, Explainability, Continuous AI Model Optimization

• E.g. CognitiveScale

• High value, Industry specific processes powered by AI

• Industry AI Accelerators• E.g. CognitiveScale

MACHINE LEARNINGTOOLKITS

DATA SCIENCEPLATFORMS

MACHINE LEARNING MODELS

AUGMENTED INTELLIGENCE PLATFORMS

INDUSTRY AI SYSTEMS

Data Exploration

Model Building

Atomic AI Services

Custom APIs/Models

Design & Compose

Orchestrate & Control Industry optimized

AI full-stack

AI poweredProcesses,Products

CORTEX 5INDUSTRYAI CLOUD

Role:Data Scientists

Role:App Developers

Role:Enterprise AI Developers

Goal:AI Powered

Business

Role:Machine Learning

ResearchersRole:

Enterprise AI Developers

• New algorithm development• Open source ML software libraries• 1st party and 3rd party proprietary

algorithmsE.g. TensorFlow, R, AWS SageMaker

• Data exploration and wrangling• ML Model Building & Selection• ML Model DeploymentE.g. Domino Data Labs, Data Robot, AWS SageMaker, H20.ai, TensorFlowServing

• Language (Text)• Vision, Speech• Emotion, ChatbotsE.g. IBM Watson, Microsoft Cognitive Services, Salesforce Einstein

@manojsaxena©CognitiveScale.AllRightReserved.2017|Confidential

Step 3: Scale Al Portfolio aligned with Business and IT strategy

Prioritized AI Portfolio for Execution

MAKING AI WORK FOR YOUR INDUSTRY @manojsaxena© CognitiveScale 2017. All Rights Reserved | Confidential | Do Not Use Without Permission 32

Step 3: Standup and operationalize your AI Innovation Cloud

Enabling Tech:

• Cloud

• Big Data

• Analytics & ML

• AI Lifecycle

• SI Services

@manojsaxena

Rising concerns about AI, Data and Ethics


Our focus

ETHICALLY ALIGNED DESIGNA Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems

Version 2 - For Public Discussion

How do we address human morality in the digital age?

@manojsaxena

Operationalize AI Ethics Framework in all projects

1. To address human morality in the digital age

• Avoid unintended consequence from self-learning systems

• Drive holistic human prosperity and well-being

• Realize full benefits from emerging technologies

2. The AI Ethics Switch will provide software designers and developers with an open specifications

based implementable process and tooling layer that allows for:

• Shaping and controlling the coming "intelligence explosion" that could give rise to self-improving AIs that could

vastly more powerful than humans

• Defining, measuring and controling the benefit we wish from Data and AI systems while avoiding negative

unintended consequences that could diminish human well-being

• Designing ways to mitigate, accidental or intentional harms caused by their creations and building fairness into

machine-learning systems

@manojsaxena

What should an AI Ethics switch enable?

1. Improve trust in AI generated outcomes (Explainability) Ensure that any AI generated insights and data usage are consistent with the societal and business

policies and have built in explainability and evidence to build verification and trust

2. Ensure Data and Model ownership, traceability, and transparency (Assurance)Ensure visibility around where data and models lives, who has access to it, and what it is being used

for as we move through the DIAL Loop (Data, Insights, Action, Learning)

3. Speed up national and global policies for responsible AI development (Collaboration)Facilitate the emergence of national and global policies that are human-centric and serve humanity’s

values and ethical principles and do so in a positive, non-dogmatic way.

@manojsaxena

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


@manojsaxena

Thank you!

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Using AI and Data to create business advantage · AI is already disrupting everything from coffee to cancer ... KNOW ME My history, my environment, ... How can we learn and act continuously?