Ai, business, organizations, society t. evgeniou

AI, Business, Organizations, Society:  
Holistic Approach to the Humans+Machines Loop
Theos Evgeniou
Professor Decision Sciences and Technology Management,
INSEAD
theodoros.evgeniou@insead.edu

▪ Early/Mid 1990s: Enterprise Resource Planning (e.g. SAP, Oracle, etc.) –
Business Process Re-engineering, process automation and support
▪ Mid/Late 1990s: Internet – networks, global connectivity, information
capturing/sharing, increased “richness and reach”, virtual teams, etc.
▪ Early 2000s: Knowledge Management – capture, share, reuse, connect
internal knowledge, experts, best practices, etc.
▪ Early/Mid 2000s: Customer/Supplier/etc. Relationship Management –
acquire, manage, understand, cross/up-sell, retain customers/suppliers/etc.
▪ Late 2000s/Early 2010s: Big Data + Cloud – the last step before…
© T. Evgeniou, INSEAD
Technological Innovations for Business: 1990s to Today

All previous technologies were at best
decision support tools
AI can
take increasingly complex decisions

Automation (of “simple” – often repetitive – decisions and
processes) has been happening for centuries [certain
mechanization in earlier centuries, autopilots, etc.]
However, with AI we will be able for the first time ever to
“automate” decisions not imaginable today to be done by
anyone other than humans
History: from past to future

“Sociotechnical” Principles
“Science Finds, Industry Applies, Man Conforms”
Motto of the 1933 Chicago World’s Fair

Experience + Learning à (Human) Intelligence
Experience for Humans = (Big) Data for Machines
(Big) Data + Machine Learning à Artificial Intelligence
Learning is at the Core of Intelligence
Big Data vs Machine Learning vs AI [watch video]

(Big) Data
+
Computation (and Cloud)
+
Mathematics of Machine Learning (Statistical Learning Theory, etc)
à Artificial Intelligence
AI in Business: Why Now?

AI, more than any other technology, requires a more
Holistic Approach
[Watch Video]

Data Engineering and Quality Management
Machine Learning and Analytics
Project Management
IT infrastructure and Management
Society
Organizational Change
Industry Transformation
Regulation
Principles and Philosophy (e.g., Ethics)
Key Message:
AI requires a Holistic Approach

Example Use Cases
Credit Scoring  
(since the 80s – HNC’s “Database Mining Workstation”) 
 
 
Recommender Systems and cross-selling  
(Amazon/Netflix/etc. since the 90s) 
 
 
Churn Management  
(with ML/AI since the early 2000s) 
 
 
Targeted advertisement and campaigns  
(since early 2000s) 
 
 
… other Customer Relationship Management (Marketing) decisions... The Early Days...

...And now?

“We can’t rely on machine learning to stop another financial crisis - In fact, overreliance on AI and big data could lead to the next one”  
 
 

How is this all (technically) feasible?
Demystifying AI and Machine Learning…

Conceptual Model of Technology  
(“the rough knowledge of a technology we need to have in order to use it
effectively”) 
 
 
 
 
… and some Historical Perspectives 
 
 

Two types of AI
Symbolic AI Statistical AI
✓ Explicit “hand written rules” [human designs]
✓ Relatively slower update of rules
✓ Learning mainly from human experience
✓ “Limited” data usage
✓ Rules + Models from data [not only from human]
✓ Rules can evolve fast, e.g. with environment
✓ Learning mainly from data
✓ “Unlimited” data usage
[Article]

Two types of AI
Symbolic AI Statistical AI = Machine Learning

Rules... Is this how Humans (often) decide?
• (Light AND Lasting Battery) OR (Big Screen AND Good Camera ) OR …
• (Floats AND High Resolution) OR (Floats AND Small Size) OR (Floats AND Light
Weight) OR …
• (Announced Buyback AND Small Cap AND High Idiosyncratic Risk) OR (Small Cap
AND Value Stock) OR (Mid Cap AND High Momentum Stock) OR …
• (M&A Live Target AND Same Industry as Acquirer) OR (M&A Live Target AND Friendly
Acquisition) OR (M&A Live Target AND Cash Deal AND Friendly) OR …
[Article link]
[Article link]
[Article link]

Are there rules for this?

Polanyi’s Paradox
“We know more than we can tell”
Michael Polanyi, 1958
The “earlier way of thinking about AI and Computers”:
“For a computer to accomplish a task, a programmer must first fully understand the sequence
of steps required to perform that task, and then must write a program that, in effect, causes the
machine to precisely simulate these steps.”
David Autor, MIT, 2014

Symbolic AI: User Writes the Rules/Program
Computer
Data
Program
Output
Computer
Data
Output Program
(Adopted from Domingos, 2017)
Statistical AI: Machine Learns/Writes the Rules/Program

Demystifying AI and Machine Learning: 
Example from “Learning to Recognize People”
1. What is an “image” (for a machine)?
2. How does the machine “represent what it learned”?

Everything for a Computer is Data/Numbers
(12, 92, 74, 0, 12, …., 124)
Data REPRESENTATION:
• Pixel Values
• Projections on filters
• PCA
Feature Learning
(Article link)

How does the machine “represent what it learned”?
(1,13,…)
(92,10,…)
(41,11,…)
(19,3,…)
(4,24,…) (7,33,…)
(4,71,…)
A Function

“Why is it that the simple, abstract language of mathematics can
accurately capture so much of our infinitely complex world [human
intelligence]?” 
The “New Physics of Intelligence”?
Eugene Wigner, Physics Nobel laureate, 1959
“The Unreasonable Effectiveness of Mathematics in the Natural Sciences”

It is mainly (only?) about Functions...
Rules are also Functions

From “Rules” Functions to “Black Box” Functions
Most Machine Learning methods “learn” (find) functions that cannot be
expressed, and explained, with simple rules.
[the learned functions are often kind of “infinite rules”, with “infinite
number of parameters” – some may say like the “infinite” connections
between neurons developed/learned due e.g. to neural plasticity]
The “AI Polanyi’s Paradox”?
Machines, like humans, know more than they can tell

Complexitycontrol
Complexitycontrol
Complexitycontrol

What is “better”?
Machines Humans + Machines

A Booming [and old] Research Topic…

“[This] isn’t a software package; it’s a way of doing business”
Quote from an executive
“The dominant issue in computer technology will be the ability to implement human
behavior change.”
Warren McFarlan, 1968

Data Engineering and Quality Management
Machine Learning and Analytics
Project Management
IT infrastructure and Management
Society
Organizational Change
Industry Transformation
Regulation
Principles and Philosophy (e.g., Ethics)
Key Message:
AI requires a Holistic Approach
[Article 1] [Article 2]

Deep Learning and Business

Deep Learning and Business
New Business/Industry
No New Business/
Industry

What’s Next? [watch video 1, video 2]
Machines Humans + Machines

Kasparov’s Law (The “Missing Middle”)

Kasparov’s Laws (The “Missing Middle”)
[watch video]
Weak Human + Machine + Strong Process >
Strong Human
Strong Machine
Strong Human + Machine + Inferior Process

The Pivotal Management Challenge of the AI Era will be…
(INSEAD Knowledge, April 8, 2019) [watch video]
“How can we get Humans and
Machines to Best Work Together”

AsiaEurope Middle East| |
Theos Evgeniou
Professor Decision Sciences and Technology Management,
INSEAD
theodoros.evgeniou@insead.edu

Ai, business, organizations, society t. evgeniou

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