Course support for Technology and Innovation

1. Digital Technologies and Innovation
Introduction
April 2019
http://DSign4Methods.com

2. “For every complex problem, there's a solution that is simple, neat, and
wrong." H.L. Mencken
Is
AI
the future of
innovation?

3. • Data is nothing more than answers
waiting for you to ask the right question
• Behind one door I’ve hidden the key to a
great career, and a beer behind the
other two
• You can choose any door
• To lend you a hand, I will show you one
bottle of beer.
• What are your chances of finding the
key?
What do we mean
by a better
decision?

4. ©2019 L. SCHLENKER
Agenda
Introduction
Administrative Details
Artificial Intelligence and Innovation
The Building Blocks
Introduction

5. Module Facilitator
I work with managers to help them
understand how enterprise applications,
web and mobile technologies can enrich
their careers.
The client portfolio in the ICT industry
includes Microsoft, Apple, Ernst & Young,
France Telecom, HP, IBM, Oracle and SAP
.
The work with the IT industry in Europe
has included fifty partner and customer
conferences, a dozen case studies, and
various marketing support activities.
Prof. Lee SCHLENKER,
The Business Analytics Institute
Mail : lee@lhstech.com
Skype : leeschlenker
Web : www.leeschlenker.com
Introduction

6. Working to help
management take
better decisions
People-based
approach to Data
Science
Applied Research,
Consulting, ExecEd
modules, Boot Camps

8. Course Portal:
http://DSign4Methods.com
©2019 Business Analytics Institute
The objective of this course is to
build the students’ knowledge of the
practice of innovation in a variety of
industrial settings
Introduction

9. This a place where managers and
students of management can discuss
and debate best practices in the digital
economy, new developments in data
science and decision making. Ask
questions and get practicable
answers, and learn how to use data in
decision making.
Analytics for Management
https://www.linkedin.com/
groups/13536539
Introduction

10. 10©2019 LHST sarl
• Analyze the context of each case to document the
key processes of the organization or the market
• Qualify the data at hand to understand the nature of
the business challenges
• Apply the appropriate methodologies in your
predictive and prescriptive analyses, and
• Integrate elements of visual communications in
transforming the data into a call for collective
action
In this module , you will
www.Dsign4methods.com
Adminstration

11. 11
Innovation is a State of Mind
©2019 LHST sarl
Introduction
Session 1 The Building Blocks
Session 2 Digital Economics
Session 3 The Internet of Value
Session 4 Decision Making
Session 5 Innovation
Session 6 Data Ethics

12. Grading Scale
Participation: 50% of your grade will be based upon your innovation project
Final exam: 50% of your grade will be based upon your results on the final
multiple choice exam.
Confirm or infirm the hypothesis that AI is a lever of
innovation using an example from your experience.
Insure that your analysis includes:
• the conflict or the opportunity (why should your
audience care about your story?)
• the context (what skills, knowledge or experience
has permitted this problem/opportunity to arise?)
• the roadmap (how does this product, service, idea
influence customer experience)
• the happy end (how will your audience evaluate your
story?)
Introduction

13. ©2019 LHST sarl
Introduction

14. Introduction

15. • Management is all about taking better
decisions
• What do better decisions mean (faster,
more impressive, more precise) ?
• Is it observable – how is something more
precise answer to a problem?
• The challenge is deciding what we want to
measure
Lewis Mumford, Technics and Civilization
Decision
Making
©2019 L. SCHLENKER

16. To help us understand the motivations, experience and
objectives of the internal and external clients of the
organization
 ROI
 Real time data
 ...
Stockholders
 Competition
 “made in”
“made by”
 ...
The State
 Peu de
barrières
d’entrée
 Acquisitions,
OPA...
Partners
 Loyalty
 Real costs
 ...
Clients
The Enterprise
 Mobility
 Empowerment
 ...
Employees
Introduction

17. Lee SCHLENKER
Results
Actions
Knowledge
Context
Data
Process
Interprets
Decisions
Measures
Obtain
Define
Require
Drive
The ladder of initiatives™
Introduction

18. • Inputs
• Prediction
• Evaluation
• Actions
• Outcomes

19. • Scan the context
• Qualify the data at hand
• Choose the right method
• Transform data into action
Introduction

20. Introduction
What is AI?
Machine Learning Artificial Intelligence
Nature Knowledge Intelligence
Vision Self-learning
Algorithms
Mimic human
Behavior
Use Scenario Learns from data Solve Complex
Problem
Aim Sufficient Solution Optimal Solution
Evaluation
Metrics
Accuracy Success

21. Introduction
What does better mean?
• Rule-based automation
• Produce probabilistic
predictions based on
the data
• Combine prediction
with judgement to
learn
• Generate art and
design
• Extract and quantify
emotional states
• Develop mental
constructs
• Adapt to
environmental needs
The Machine Intelligence Continuum

22. Application
Technology and Innovation
Perception
How do define
the problem?
Prediction
What evidence
will allow us to
act?
Evaluation
How do we
judge what
better means?
Action
How does our
decision
translate into
action?
Training
How does past
data support
our analysis?
Input
What types of
data do we need
to capture?
Feedback
How do we
correct our
errors
Explication
What have we
learned from
practice?
Challenge : Data :
Human
Inteligence
Machine
Learning
HI/AI
©2018 BAI

23. • Properties - digital experiences put in place to
enrich organizational conversations
• Platforms – digital technologies that create
proximity between those that produce, and those
that consume, experience
• People – the managerial mindset
• Practice - the operational realities of management
Schlenker (2015)
Introduction