Using AI and Data to create business advantage · AI is already disrupting everything from coffee...
Transcript of Using AI and Data to create business advantage · AI is already disrupting everything from coffee...
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
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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
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Definition: What is Artificial Intelligence?
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“AI is the science and engineering of making intelligent
computer programs/machines that learn from patterns”
AI&MachineIntelligenceTechnologies
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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
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AI is already disrupting everything from coffee to cancer
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What most people think of AI
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What we think of AI
AI = Augmented Intelligence(Man + Machine and NOT Man vs Machine)
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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
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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
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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?
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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
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Agenda
1. What is happening with AI and Digital? 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? Examples?
4. What are some good CxO practices? Approach to AI, Data, Ethics?
5. Discussion
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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
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Where is AI implementation today in the Enterprise?
AI = Artificially Inflated
AI = Accelerated Innovations
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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
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How does AI create value in the Enterprise?
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©&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”
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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
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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
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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 $b) On site adjustersc) Identify contractor
Sat AM:Drone/adjuster dataHow many affectedWhich option best?Update knowledge
* No dependence on IBM Watson. Watson APIs can be orchestrated using Cortex OS based on Ai requirements.
1. KNOW MEMy history, my
environment, goals.
2. ENGAGE MEContextual insights with
evidence.
3. LEARN FROM MEActions and outcomes, deeper profile of one.
InsuranceAI Powered Process
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Agenda
1. What is happening with AI and Digital? Why is it important?
2. How much of this is real? How much is marketing hype?
3. Why is AI being seen as a strategic business capability? Examples?
4. What are some good CxO practices? Approach to AI, Data, Ethics?
5. Discussion
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@manojsaxena
AI and digital are powering powerful business model disruption
• World’s largest taxi company has no taxis (Uber)
• Largest accommodation provider owns no real estate (Airbnb)
• Largest phone companies own no telco infra (Skype, WeChat)
• World’s most valuable retailer has no inventory (Alibaba)
• One of the largest banks holds no cash (Bitcoin)
• World’s largest movie house owns no cinemas (NetFlix)
These are ALL technology companies that engage customers brilliantly!
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Disruption at work: Unbundling of Banks
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HERE’S$WELLS$FARGO$UNDER$ATTACK
Source:%CB%Insights%P Disrupting%Banking:%The%FinTechStartups%That%Are%Unbundling%Wells%Fargo,%Citi%and%Bank%of%America
Cloud
Social
Mobile
Big Data
Analytics A.I.
MILLENNIALS
Source: CB Insights
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Disruption at work: Unbundling of Consumer Packaged Goods
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AND$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
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And it is occurring at an accelerated pace
Pokemon Go à 14 days
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Agenda
1. What is happening with AI and Digital? 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? Examples?
4. What are some good CxO practices? Approach to AI, Data, Ethics?
5. Discussion
©CognitiveScale.AllRightReserved.|Confidential 25
@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
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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
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@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
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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
© CognitiveScale 2017. All Rights Reserved | Confidential | Do Not Use Without Permission
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
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Step 3: Scale Al Portfolio aligned with Business and IT strategy
Prioritized AI Portfolio for Execution
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Step 3: Standup and operationalize your AI Innovation Cloud
Enabling Tech:
• Cloud
• Big Data
• Analytics & ML
• AI Lifecycle
• SI Services
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Rising concerns about AI, Data and Ethics
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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
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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
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