The document discusses several important considerations for companies looking to implement artificial intelligence, including developing an AI transformation playbook, assessing an organization's AI maturity, anticipating costs and timing, deciding whether to build or buy AI solutions, and addressing important legal and ethical issues around explainability, privacy, fairness, and safety. The document provides guidance on how companies can effectively lead their organization into the AI era by establishing the right strategies, processes, and safeguards.
Global Lehigh Strategic Initiatives (without descriptions)
Build AI maturity and transformation playbook
1. Build AI in your company
management, culture, legal, ethics, governance
Orange Belt - Session 3
1
2. What we have seen so far
1. What is AI
2. What can AI do and what it can’t do
3. How to select a project
4. What are the steps necessary for a first successful ML
project
2
3. AI is not traditional software
A totally different lifecycle
3
6. We saw this last week
01
03
02
06
04
05
Monitoring & Updates
Have the right talents & solutions
Maintenance
Select the right question
Choose the performance metric
Decide the level of explainability
Identify
Use the right architecture
Have the talents in place
Deploy
Find the right data
Structure annotate data
Clean Data
Data
Decide on an acceptable error
Test on the right scope
Evaluate
Select the right algorithm
Tune the model
Model
6
8. Plan for today
0 (Base on the case seen in the assignment) What does it feel like to work on a
complex ML project ?
1. AI Transformation Playbook
2. AI Maturity of your company
3. Perspectives on costs & roadmap
4. Build vs Buy
5. Legal & ethics considerations
6. Human and AI interactions
8
9. 1. AI Transformation Playbook
How to lead your company into the AI era
9
Source : https://landing.ai/ai-transformation-playbook/
10. AI Transformation Playbook
1. Execute pilot projects to gain momentum
2. Build an in-house AI team
3. Provide broad AI training
4. Develop an AI strategy
5. Develop internal and external communications
10
11. 1. Execute pilot project to gain
momentum
• More important for the initial project to succeed rather than
be the most valuable
• Show traction within 6-12 months
• Can be in-house or outsourced
11
12. 2. Build an in-house AI team
Centralized AI Platform
12
13. 3. Provide broad AI training
Role What they should learn Nb hours
of training
Executives and senior
business leaders
- What AI can do for the company ?
- AI Strategy
- Resource allocation
>= 4 hours
Leaders of divisions
working on AI projects
- Set project direction (both technical and
business diligence)
- Resource allocation
- Monitor progress
>= 12
hours
AI engineer trainees - Build and ship AI software
- Gather data
- Execute on specific AI projects
>= 100
hours
13
14. How to
document
yourselves
AI for Everyone, by Andrew Ng
Deep Learning Specialization
3blue1brown
Siraj
OpenAI
ImportAI
https://towardsdatascience.c
om/
14
15. 4. Develop an AI strategy
• Build several difficult AI assets that are broadly aligned with
a coherent strategy
• Leverage AI to create an advantage specific to your industry
sector.
• Design strategy aligned with the “Virtuous Cycle of AI”
AI plays a role here
15
16. 4. Develop an AI strategy
• Consider creating a data strategy
• Strategic data acquisition
• Unified data warehouse
• Create network effects and platform advantages
• In industries with “winner take all” dynamics, AI can be an
accelerator
• What about more traditional strategy framework ?
• AI can allow a low cost strategy
• AI can allow a high value product strategy
16
17. 5. Develop internal and external
communications
• Investor relations
• Government relations
• Consumer / user education
• Talen / recruitment
• Internal communications
17
18. AI pitfalls to avoid
Don’t : Do :
- Expect AI to solve everything - Be realistic about what AI can and cannot do,
given limitations of technology, data and
engineering resources
- Hire 2-3 ML engineers and count solely on
them to come up with use cases
- Pair engineers with business talent and work
across cross-functional team to find valuable
projects
- Expect the AI project to work the first time - Plan for AI development to be an iterative
process, with multipe attemps needed to
succeed.
- Expect traditional planning processes to apply
without changes
- Work with AI team to establish timeline
estimates, milestones, KPIs, etc.
- Think you need superstar AI engineers before
you can do anything
- Keep building the team but get going with the
team you already have 18
19. Some initial steps you can take
• Start learning (with this course)
• Start brainstorming projets
• Hire a few ML/DS people to help
• Hire or appoint an AI leader (VP AI, CAIO, …)
• Discuss with CEO/Board possibilities of AI Transformation
• Will your company be much more valuable and/or more effective if it
were good at AI ?
19
20. Exercise
What is the first step in the AI Transformation Playbook for helping
your company become good at AI?
20
21. Exercise
Of the following options, which is the most important trait of your
first pilot project?
A) Succeed and show traction within 6-10 months
B) Drive extremely high value for the business
C) Be executed by an in-house team
D) None of the above
21
22. Exercise
Say you are building the DropBox OCR system, and want to
accumulate data for your product through having many users.
Which of these represents the “Virtuous circle of AI” for this
product?
22
27. 3-steps roadmap to maturity
Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
DATA
PEOPLE
LEGALÐICS
PRODUCT
27
28. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
DATA
No infrastructure
Data Silos Descriptive analytics
PEOPLE
No data scientists
No education
LEGALÐICS
No legal compliance No
principles or processes
PRODUCT
Business cases but no
development
28
29. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
PEOPLE
No data scientists
No education
LEGALÐICS
No legal compliance No
principles or processes
PRODUCT
Business cases but no
development
29
31. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos
Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding value
PEOPLE
No data scientists
No education
LEGALÐICS
No legal compliance No
principles or processes
PRODUCT
Business cases but no
development
31
35. Data annotation pipeline
To go at scale, you need guidelines, internal and external annotators.
Even pre-annotation with machine learning that can be validated
DOG
CAT
35
36. Standardise quality check
Find the relevant metrics
Exemple Sound: signal to noise ratio / cross-talk / silence detection
36
39. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding AOV
PEOPLE
No data scientists
No education
A few data scientists
Global acculturation / education
Chief Data Officer
SWAT Team
Use case specialists
LEGALÐICS
No legal compliance No
principles or processes
PRODUCT
Business cases but no
development
39
40. Example roles
• Software Engineer
• Build user interface, web & mobile applications, back end
operations, …
• Machine Learning Engineer
• Input (A) to Output (B)
• Machine Learning Researcher
• Extend state-of-the-art in ML
40
41. Example roles
• Data Scientist
• Examine data and provide insights
• Make presentation and communicate to team / executives
• Data Engineer
• Organize Data
• Make sure data is saved in an easily accessible, documented and
cost effective way
• More and more required as the amount of data managed by
companies increases
• AI Product Manager
• Help decide what to build; what’s feasible and valuable
41
42. Getting started with a small team
• 1 Software Engineer, or
• 1 Machine Learning Engineer / Data Scientist, or
• Nobody by yourself
42
47. Exercise
Suppose you are building a trigger word detection system, and want
to hire someone to build a system to map from Inputs A (audio clip) to
Outputs B (whether the trigger word was said), using existing AI
technology. Out of the list below, which of the following hires would
be most suitable for writing this software?
47
48. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding AOV
PEOPLE
No data scientists
No education
A few data scientists
Global acculturation / education
Chief Data Officer
SWAT Team
Use case specialists
LEGALÐICS
No legal compliance No
principles or processes
GDPR Compliant
Core explanations & policies
Ethical principles
Corporate practice
Part of incentive programs
Core product advantage
PRODUCT
Business cases but no
development
48
49. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding AOV
PEOPLE
No data scientists
No education
A few data scientists
Global acculturation / education
Chief Data Officer
SWAT Team
Use case specialists
LEGALÐICS
No legal compliance No
principles or processes
GDPR Compliant
Core explanations & policies
Ethical principles
Corporate practice
Part of incentive programs
Core product advantage
PRODUCT
Business cases but no
development
Use of APIs
Discrete proof of concepts
Pilots
AI is core product
& core competency
49
50. Exercise : create your own roadmap
Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding AOV
PEOPLE
No data scientists
No education
A few data scientists
Global acculturation / education
Chief Data Officer
SWAT Team
Use case specialists
LEGALÐICS
No legal compliance No
principles or processes
GDPR Compliant
Core explanations & policies
Ethical principles
Corporate practice
Part of incentive programs
Core product advantage
PRODUCT
Business cases but no
development
Use of APIs
Discrete proof of concepts
Pilots
AI is core product
& core competency
50
62. To sum up
1. Anticipate the cost drivers (data, accuracy, problem difficulty)
2. There is a big upfront cost to anticipate as opposed to regular projects
3. The cost scales nonlinearily with the accuracy requirements
4. The time it takes to get that accuracy is nonlinear as well
62
63. Exercise
What is the biggest cost of your current idea?
What do you think will be the bottleneck?
What type of strategy could you devise to
anticipate that?
63
66. With a consultant you don’t know, always look to start with a small
proof of concept deliverable to prove to yourself that this consultant
knows their stuff. Work with the consultant to come up with a project
that is a low hanging fruit. Something that they can deliver on quickly
without much development effort (e.g. based on existing code they
already have, and data you have already collected). If this first step
goes well, then you can confidently move to a bigger project scope.
66
68. Access to resources
(cloud, ML libraries,
production systems)
Access to talent Proven success
stories to get you up
and running in no time
So why would you do everything yourselves ?
Don’t try to run faster than the train
Collaborate with Startups!
68
69. 5. Legal & Ethics
What should you be careful about
69
82. 1. AI systems must be transparent.
2. An AI must have a “deeply rooted” right to the
information it is collecting.
3. Consumers must be able to opt out of the system.
4. The data collected and the purpose of the AI must be
limited by design.
5. Data must be deleted upon consumer request.
Bernhard DebatinProfessor and Director of the Institute for Applied and Professional Ethics
Guidelines for ethical privacy
83
83. 1. Clear consent requests to customer (and easy out)
2. Breach notifications within 72h
3. Right to access all personal data upon request
4. Right to be forgotten
in practice
84
91. ‘build a face recognition service that can detect male/female genders, with pre-
defind specific age groups, and these specific subset of races, and ethnicities in
the requirements document (which is grounded in a standard taxonomy from a
neutral organization such as the United Nations Race and Ethnicity taxonomy))
with at least 90% accuracy on ‘these’ given specific test datasets’ where ‘these’
test datasets were carefully crafted by the offering management team to have an
even distribution of all the genders, age groups, specific races, and ethnicities for
which the model is supposed do well’
Specific, focus & measurable
92
98. To summarise
1. Specify a robust goal
2. Make sure you respect privacy
3. Make it explainable
4. Make it secure
5. Make it transparent
99
99. Exercise
Can you identify the most sensitive data in your organizations?
Do you already have a strategy to be compliant?
Do you need a big explainability or would you rather get better
performance?
100
100. 6. Towards better HCI
Human-Computer interactions have to be reconsidered
101
107. Human vs Machine
• Machines never forget
• Crunch numbers and scan fast
• Never bored or impatient or tired
• People have better emotional nuances + humanity
• People have better common sense
• People can tackle new tasks easily
108
112. Keep human psychology in mind
Lots of false positive = irritating
Introducing without consultation = resentment
Most often good decision = overtrust (automation bias)
…
113
113. Exercise
Try to imagine the end product of your project, leveraging both AI and
humans using the type of collaborations we just talked about
What would the user be the most sensitive about ?
114