Artificial Intelligence holds tremendous promise, but moving from idea to implementation is far from easy. The reality is that the majority of AI projects end up failing today. In this discussion, we will explore the best practices and provide specific guidance on how to successfully implement AI projects within your organization. We will talk about designing your AI strategy, selecting the best projects, building successful multifunctional teams and delivering results. This talk is based on decades of experience and hundreds of implementations.

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Alex Ermolaev, Director of AI, Change Healthcare
OCTOBER 2019
FROM IDEA TO IMPLEMENTATION:
LAUNCHING AI INITIATIVES
SUCCESSFULLY

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2
Agenda
• The Next Industrial Revolution: An Introduction to Artificial Intelligence
• The Journey to Artificial Intelligence Adoption
• From Adoption to Selection: Choosing “Right Fit” Projects
• Optimization Through Successful Project Build & Management
• A Capable Organization: Transforming for Artificial Intelligence
• Learning Through Healthcare Examples

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3
The Change Healthcare Intelligent Healthcare Network™

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4
The Next Industrial Revolution:
Introducing Artificial Intelligence (AI)
Section 1.0

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5
The Next Industrial Revolution: The Intelligence Age
Metallurgy
Railroads
Ships
Steel
Machinery
Chemistry
Automobiles
Planes
Electronics
Computing
Telecom
A.I.
Biotech
NanotechTextiles
Intelligence
Age
Information
Age
Manufacturing
Steam
Electrical
Internal
Combustion
2015+1971+1771+ 1829+ 1875+ 1908+

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6
AI: Fiction vs. Science
Artificial General Intelligence
x intelligent robots
Artificial Narrow Intelligence
✓ pattern matching technology

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AI Accuracy Skyrockets After 2012
60%
65%
70%
75%
80%
85%
90%
95%
100%
1990 1992 1995 1997 2000 2002 2005 2007 2010 2012 2015 2017
Source: Microsoft Corp Speech Recognition Accuracy
Human-Level Accuracy

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8
The Journey to Artificial
Intelligence Adoption
Section 2.0

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AI Adoption as of 2018
1 in 20
companies has incorporated
AI extensively in offering or processes
94%
of companies believe AI is
key to competitive advantage
78%
of AI projects stall before
deployment
96%
of AI projects experience
data-quality issues
Majority
of AI POCs never become
full AI projects
Source: Paul Taylor, IBM Fellow, ODSC West 2018, https://www.businesswire.com/news/home/20190523005183/en/Survey-96-Enterprises-Encounter-Training-Data-Quality

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10
McKinsey Study
Companies adopting AI create significant
economic benefits and world impact.
AI has the potential to deliver an estimated $13
trillion in additional global economic activity by
2030, or about 16% higher cumulative GDP,
compared with today.
Source: McKinsey Global Institute https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy

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The AI Adoption Journey
Getting Started
• Experiment!!!
• Understand your data
• Few hires/consultants
Selecting and
Managing projects
• Choosing projects
• Project pipeline
• AI development cycle
Building AI-capable
organization
• CEO/Board
commitment
• Central AI team
• Educating your
organization
Scaling
• One-click train
• One-click deploy
• Conveyor model

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12
From Adoption to Selection:
Choosing “Right Fit” Projects
Section 3.0

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13
Not All AI Projects are Viable Today
Technically
Feasible
Economically
Feasible
Budget
Deadline
Source: An Empirical Model of Large-Batch Training, https://arxiv.org/pdf/1812.06162.pdf

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14
Key Considerations for Selecting AI Projects
Problem Pattern Data

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Make Sure the Problem Is…
Big Core Expensive

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AI Needs a Pattern That Is…
Existing Strong Complex

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17
Is Your Data…?
Big Clean Labeled

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18
Optimization Through Successful
Project Build & Management
Section 4.0

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Before You Get Started With Your AI Project…
Centralize all
necessary data
Develop metrics for
your current
performance
Define your goal.
What does success
look like?
Be willing to
tolerate new
probabilistic
technology
Be willing to
change/break
existing business
processes
Verify business and
technical viability
Complete
legal/compliance
review, if necessary
Assemble the
dream team with
right skill/
experience mix

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20
Typical Project Development Process
Data
Preprocessing
AI Model
Development
Model
Productization
Data Engineers Data Scientists Application Engineers
Learn and
Adjust
Project
Selection

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21
The Best AI Project Team…
Is
Centralized
Has Strong
Lead(s)
Has a Mix of
Skills

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22
Build Infrastructure That Is…
Scalable Centralized Efficient

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23
A Capable Organization:
Transforming for Artificial
Intelligence
Section 5.0

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Andrew Ng’s “AI Transformation Playbook”
Execute pilots to
gain momentum
1 2 3 4 5
Build an in-house
AI team
Provide broad
AI training
Develop an
AI strategy
Develop internal
and external
communication

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Broad AI Training – Change Healthcare Example
AI training available to all
Change Healthcare employees:
• Lvl 1 – introduction (5 min)
• Lvl 2 – eLearning class (1 hour)
• Lvl 3 – seminar (1 day)

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PwC’s Global Artificial Intelligence Study: Sizing the Prize
37%
Ready to go
Medical insurance and
smarter scheduling
(e.g., appointments
and operations)
23%
Medium-term potential
Data-driven diagnostics
and virtual-drug
development
40%
Longer-term potential
Robot doctors carrying
out diagnosis and
treatment
Source: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html

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Build? Buy? Partner?
• Is this core to your business?
Someone else’s core business?
• Who has the largest
amount of data?
• Who has the capabilities
to get the job done?
• Other considerations: scale,
reputation, trust, etc.
You Partner Someone
else
? ? ?

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28
Learning Through
Healthcare Examples
Section 6.0

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AI in Healthcare
Early Diagnosis Health Monitoring Medical Imaging
Medical
Intelligence
Precision Medicine Personalized Treatment Plans Clinical Trials
Personalization
Smart Electronic
Health Records
Payment Accuracy
Patient Identification
and Retention
Optimized
Healthcare
System

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AI in Healthcare Challenges
Embrace
Variety
Respect
Data
Understand
Workflow

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“In order to understand trust in the relationship between humans and
automation, we have to explore trust in two dimensions: trust in the
technology and trust in the innovating firm.”
Harvard Business Review
https://hbr.org/2017/04/to-get-consumers-to-trust-ai-show-them-its-benefits

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