This document provides guidance on integrating AI into organizations. It recommends aligning AI projects with business drivers to create value, focusing on scaling human capabilities with assisted and augmented intelligence, and taking a portfolio approach to AI innovation around customer needs. It also stresses the importance of developing an effective operating model with the right outcomes, team, tools, and iterative process, as well as understanding the range of AI and machine learning efforts.

1. Integrating AI into your Organization:
Business Applications
Hal Kalechofsky, Ph.D.

2. Agenda
• Some Guiding Principles
• Thinking about AI in Business
• Thinking Big about Big Data
• Some Best Practices
• Align with a Business Driver
• Innovate around Customer Needs
• Have an Effective Operating Model
• Understand the Range of Efforts
• Machine Learning and Deeper
• Algorithms and Decision-Making
• Value-Strategy-Execution-Metrics
• Enterprise AI Readiness
Intended Audience:
Executives and decision makers

3. A Few Definitions
AI is:
The ability of machines to think, learn, and act like humans
Machine learning is a type of artificial intelligence (AI) that
provides computers with the ability to learn without being
explicitly programmed. Machine learning focuses on the
development of computer programs that can change when
exposed to new data
More like:
Amplified Intelligence

4. Thinking about AI
Assisted Intelligence:
AI systems that assist humans in
making decisions or taking actions.
Hard-wired systems that do not
learn from their interactions.
Automation:
Automation of manual and cognitive
tasks that are either routine or non-
routine. This does not involve new
ways of doing things – it automates
existing tasks.
Augmented Intelligence:
AI systems that augment human
decision making and continuously
learn from their interactions with
humans and the environment.
Autonomous Intelligence:
AI systems that can adapt to different
situations and can act autonomously
without human assistance.
Human in the Loop No Human in the Loop
Image adapted from PWC: Sizing the Prize in Artificial Intelligence
Look for the A B problem (Andrew Ng).
Input A generates simple response B
“If a typical person can do a mental task with
< 1 sec of thought, it can probably be AI automated.”

5. Data is the new business model.
Think Big about Data
It is often not about who has the best algorithm, but
who has the most data.

6. AI Integration: Best Practices #1
• Align with a business case/drivers
- Business 101: Keep costs down, revenue strong, happy clients
- Relentless focus on use cases (Connect to KPI)
• No “One size fits all” solution (Understand effort)
- Orient innovation towards customer success
- Continuum of ways to implement AI for great outcomes..
• Match the solution to the problem
- Don’t be a hammer looking for a nail
- Mix business & technology in the right way
• Low-hanging fruit - Focus on what is easily do-able
- Github world-view
• Add/evolve existing platforms/systems/resources
- Leverage capital investments
• Build towards data-shares
- “What can be connected, will be connected”
- The output of your program is the input of someone else’s
AI
Business

7. AI Integration: Best Practices #2
• Focus on scaling humans
- Listen to customers and stakeholders (be consultative..)
- Assist, Augment, and Automate
• Operational or Strategic?
- Decide broad-brush if you have operational or strategic opportunities
- Eg. Robotic process automation can do a lot
• Consider a portfolio approach to AI (Broad View)
- A mix of quick-win projects tying to transformative long-term projects
- Don’t get bogged down by technology, look at what others are doing
• Invest in people (talent)
- When it gets built, it will be built by a team
- Consider partnerships and core competencies
• Embrace it - Change leadership
- People & AI together. AI is not about destruction, it is about job movement

8. Align with a Business Driver
Achieving
Operational Efficiency
Improve Customer
Satisfaction / Experience
/ Expectation
Competitive
Differentiation
Eg. Improve decisions,
improve process,
Augment humans, improve
production support,
Removing legacy
Eg. Predictive modelling,
recommendations, decrease
cycle time, anti-fraud
Eg. New business model,
Enhance business models

9. Portfolio Approach:
Innovate with AI Around Customer Needs
Business Value definition
“Who, What, Why?”
Strategic Customer/Market Needs
Focused Use Cases
Governance and Investment
Technology
Solution
Prototype
Deployment
Architectural alignment
Business à Technical requirements
Innovate everywhere
“Succeed fast or fail fast”
Executive oversight
Prioritization (value, complexity);
Agility, Domain Knowledge
Right tool for the right job
Sourcing, solution evaluation, implementation
“That which is measured, improves”
KPIs, tangible value, learnings

10. You Need an Effective Operating Model
Outcomes
The Business
Model
Data Science
Data
Data Engineering
Compute
IT
Tackle the
right problems
Build the
right team
Have the
right tools
Iterate the
right way 1
23
4
It’s about aligning the right model, data, and infrastructure with the right outcomes
Large, clearly defined business value
• What “job” would someone “hire”
your solution to do (Christensen)?
• Who is the customer?
Interdisciplinary
• Hybrid, not unicorns
• Don’t existing pattern match
Deploy platforms/pipelines for efficiency
• Be polyglot
• Data Science |= Software Development
Success is more “agile” than Agile
• Data science is exploratory
• The future is heterogenous
Adapted from Carlsson, Wang, Forrester, Anaconda, 2018

11. Understand the Range of Efforts

12. Types of Learning
• Supervised (inductive) learning
– Training data includes desired outputs
Ex.: Have labelled photos, train model to recognize new photos
• Unsupervised learning
– Training data does not include desired outputs
Ex.: Have unlabeled photos of N people, divide into N clusters
• Semi-supervised learning
– Training data includes a few desired outputs
Ex.: Supervised learning on labeled data only, then apply classifier
to unlabeled data to generate more labeled examples
• Deep Learning
- Inspired by neural brain structure, methods based on learning data
representations and abstractions, supervised or unsupervised
• Reinforcement learning
– Rewards from sequence of actions
– Ex.: Game-playing, or robot putting object in a box

13. Things that AI does and does not do well
• Classify/recognize images and sounds
• Search the Web
• Games: Chess, Jeopardy
• Translate in many languages
• Identify fraudulent trends/patterns
• Work in deadly environments
• Product recommendations
• Personal Assistants
• CRM leads, contacts, LTV predictions
• Plagiarism checkers
• Map/traffic applications
• Reasoning
• Problem solving
• Speech recognition (still a “D”)
• Moving in arbitrary environments
• High-level planning and control
• Manipulating objects or balance
AI does well AI does less well*
* But getting better fast ..!

14. Consider AI/ML Pipelines
• Most of your deployments will resemble a pipeline more or less like this
• There are large amounts of good open-source packages and models available
• There are lots of niche AI companies that do certain specifics very well
• There is also a lot of public data out there, as well as pre-crunched model APIs
(Don’t re-invent the wheel..)

15. Algorithms and Decision-Making
4 Models (Michael Schrage, HBR 2017)
1) The Autonomous Advisor
Algorithms are your strategic advisor,
with human oversight
2) The Autonomous Outsourcer
Algorithms are your business process
outsourcing
3) The Autonomous Employee
Software is a valued colleague,
“machine-learning” first enterprise
4) All-in-one Autonomy
Algorithms run company decisions;
human leadership defers to algorithms

16. Expectations
WHILE ALL THE RISKS OF AI ARE VERY REAL, THE
APPROPRIATE BENCHMARK IS NOT PERFECTION BUT
THE BEST AVAILABLE ALTERNATIVE.

17. AI-Powered “Healthcare” Enterprise Operations
Smarter, Lower cost, Better Customer Outcomes
V
E
S
5+ Years
2-4 Years
1-2 Years
M
Business Drivers: Operational Efficiency, Patient Outcomes, Competitive Differentiation
No Humans in Loop
Automation
Humans in Loop
Assisted Intelligence
Sense Comprehend Predict
A VSEM Model – Healthcare Example
Predict Act
• Machine/Human
• Data at Rest/In Motion
• EMR, Papers, Journals,…
• Sensors / IoT
• Interactions
• Observing process,
workflows
• Aggregate
• Search, Compare
• Correlate
• Troubleshoot
• Problem-solve
• Hypothesize
• Model, Simulate
• Statistical likelihoods
• Reason, Diagnosis
• Advise
• Recommend
• Evaluate
• Claim rejections
• Denials
• No-shows
• Prior authorizations
• Patient re-visits
• Wait times
• Unnecessary procs
• Post-treatments
• Clinician admin time
• User satisfaction
• Patient volumes
• Care quality
• Disease incidence
• Error rates
• Preventative care
• Disease prediction
• Rev. per Dr/Employee
• Cost per unit
• Precision medicine
• Population health
Operational Experience Financial
• Operate
• Integrate/Implement
• Continuous Improvement
• Evaluate
• Adapt, change
• Repair, fix
• Monitor, Follow-up

18. Enterprise AI Strategy Includes…
• Data Life Cycle Management
• Enterprise Knowledge
Management
• Enterprise Automation Strategy
• Infrastructure Upgrades
• Legacy Systems Deprecation
• Security
• Command Center
• Governance Structure
• Policies & Procedures
• Change Management
• Executive, IT & Business Buy In
• Skilled Resources and External
Experts

19. Enterprise AI Readiness
WHAT:
• Executive, IT & Business Buy In
• Enterprise AI Strategy
– Enterprise Data Strategy
– Data / Intelligence Lifecycle
Management
– Enterprise Knowledge Management
– Data Governance
– Policies &Procedures
• AI/ML Frameworks & Algorithms
• New AI & Automation Infrastructure &
Infrastructure Upgrades
• Enterprise Automation Strategy
• Change Management
• Security Strategy
• AI & Automation Command Center
HOW:
• Innovation Life Cycle / Innovation
Management
• Current State Assessment
• Analysis of Use Case Commonalities
• Creation of Future State AI &
Automation Architecture
• Creation of Future State phased
roadmap
• Deprecate Legacy Systems
• Skilled Resources and External Experts
• Legal, IP & Ethical Considerations