Artificial intelligence (AI) is finally coming into its own. Technologies such as ChatGPT, DALL-E, driver-assistance, and autonomous robots are clear signs of an AI-driven market shift. AI technologies, in particular machine learning (ML), are being applied in all sectors of the economy. Your organization is likely to soon be running projects to apply and even develop AI if it isn’t already doing so. Are you ready? This talk overviews AI and how AI/ML initiatives work. We also explore several critical challenges, including the experimental nature of AI initiatives, that data quality is critical to your success, the high failure rate of AI initiatives, and the ethical considerations surrounding AI. We examine the implications of these challenges and work through strategies to address them. Agenda: 1. What is(n’t) AI? 2. AI terminology in a nutshell 3. Are you ready for AI? 4. The lifecycle of an AI/ML initiative 5. Overcoming the data quality challenge 6. Ethical considerations with AI 7. Business implications of AI 8. Success and failure factors for AI initiatives

1. Artificial
Intelligence for
Project
Managers
Are You Ready?
Scott W. Ambler
Consulting Methodologist
Ambysoft Inc.
© Ambysoft Inc.
1

2. Scott Ambler
© Ambysoft Inc. 2
• scott@scottambler.com
• X: @scottwambler
• linkedin.com/in/sambler/
Consulting Methodologist
Ambysoft.com
Thought Leader
AgileData.org
Thought Leader
AgileModeling.com
Co-Creator
pmi.org/disciplined-Agile

3. Agenda
3
1.What is(n’t) AI?
2.AI terminology in a nutshell
3.Are you ready for AI?
4.The lifecycle of an AI/ML initiative
5.Overcoming the data quality challenge
6.Ethical considerations with AI
7.Business implications of AI
8.Success and failure factors for AI
initiatives
© Ambysoft Inc.

4. WhatIs(n’t)AI?
Artificial intelligence is software, not magic
© Ambysoft Inc. 4

5. Defining Artificial Intelligence
© Ambysoft Inc. 5
Artificial Intelligence (AI)
Computer systems able to perform tasks normally requiring human intelligence.
Machine Learning (ML)
Computer systems with the ability to learn without being explicitly programmed. Training of ML
models often involve significant data curation, sometimes including data labeling.
Deep Learning (DL)
Computer systems with ”brain-like” logically structured algorithms called artificial neural
networks (ANNs).

6. Categories of Artificial Intelligence
© Ambysoft Inc. 6
Artificial Narrow Intelligence (ANI)
• Specializes in one area and solves one problem.
Artificial General Intelligence (AGI)
• As smart, or smarter, than a human with a wide range of
abilities.
Artificial Super Intelligence (ASI)
• An intellect that is much smarter than the best human in
practically every field.
We are here è
We are afraid of this è
And really afraid of this è

7. (More)
AITerminology
It’s confusing at first, but you’ll get it
© Ambysoft Inc. 7

8. Additional AI Terms That I May Use Today
© Ambysoft Inc. 8
• Data labeling. The process of identifying raw data, such as images or text, and adding one or more
meaningful labels to it to provide context so that a machine learning (ML) model can learn from it.
• Foundational model. An AI model trained on broad data at scale that can then be adapted to a
wide range of downstream tasks. Can provide a basis from which to start your own model
development. Examples: BloombergGPT and BioGPT.
• Semi-supervised learning. A model training approach that uses both labeled and unlabeled data, a
hybrid technique between supervised and unsupervised learning. Typically only a small
percentage of the data is labeled, as labeling tends to be expensive. Also called weak supervised
learning.
• Supervised learning. A model training approach where the model is provided examples of inputs,
it predicts outputs, and the model is corrected accordingly.
• Unsupervised learning. A model is trained without known outputs. The model is given data and
the goal is to find interesting patterns in it. Since we don’t know what those patterns will be, there
is no obvious way to provide corrections.

9. AreYouReadyForAI?
Likely not
© Ambysoft Inc. 9

10. © Ambysoft Inc. 10
Does your leadership understand AI?
Does your organization have
a clear AI strategy?
Do you have AI expertise available,
including data scientists?
Do you have a realistic vision for
what you want to achieve with AI?
Do you understand your current level
of data technical debt?
Can you staff teams with dedicated, diverse,
and interdisciplinary people?

11. TheMachineLearning
Lifecycle
You need a disciplined hybrid strategy
© Ambysoft Inc. 11

12. Four Uncomfortable Observations and a Claim
© Ambysoft Inc. 12
This isn’t a project. Treating this as a series of projects MIGHT work, but it’s most likely just going to
inject needless cost, risk, and time into your initiative.
A “predictive” approach to an ML initiative has exactly zero chance of success.
A “pure agile” approach to an ML initiative has very little chance of success.
A “pure lean” approach has a better chance of success, but still isn’t ideal.
Claim: A disciplined hybrid strategy will provide your highest chance of success.

13. A Very High-Level View of the ML Lifecycle
© Ambysoft Inc. 13
Build the AI
(Training)
(Construction)
Operate the AI
(Make Inferences)
(Production)
Feedback

14. Adding Details to Construction
© Ambysoft Inc. 14
Construction
Make
Inferences
Production
Feedback
Prepare the
Data
Train the
Model
Validate the
Model
Choose a
Training
Strategy
Deploy the
Model

15. Construction is Iterative and Incremental
© Ambysoft Inc. 15
Construction
Make
Inferences
Production
Feedback
Deploy
the AI
Validate the
Model
Choose a
Training
Strategy
Prepare
Data
Evolve
Reqs.
Train
Model

16. A Machine Learning Lifecycle
© Ambysoft Inc. 16
Construction
Make
Inferences
Production
Feedback
Deploy
the AI
Validate the
Model
Envision
Explore
Usage
Explore
Data
Explore
Viability
Choose a
Training
Strategy
Prepare
Data
Evolve
Reqs.
Train
Model

17. Common Lifecycle Risks
© Ambysoft Inc. 17
Construction
Make
Inferences
Feedback
Deploy
the AI
Envision
• Misaligned
stakeholders
• Inadequate data
• Biased data
• Difficult to
create diverse,
cross-disciplined
team
• Non-viability
• Insufficient
exploration
• “Detached”
exploration
• Data technical debt proves worse than originally believed
• Creation of a biased model
• Creation of an inadequate model
• “Never-ending” edge cases è Unpredictable timeline
• Non-viability
• Biased usage
• Non-viability
• Unprepared
stakeholders
• Model drift
• Function creep

18. OvercomingtheData
Quality(DQ)Challenge
It is time to pay the data piper
© Ambysoft Inc. 18

19. Potential DQ Issues Faced by ML Teams
© Ambysoft Inc. 19
• Biased data
• Insufficient data
• Semantic differences across sources
• Missing data values
• Inconsistent data values
• Unclear/unknown sources of record
• Ownership limitations
• Privacy/security
• Data poisoning
• Data drift
• and many more…

20. Data Quality Techniques for ML Teams
© Ambysoft Inc. 20
Data
cleansing
Data
stewards
Data
architecture
Automated
regression testing
Data
labeling
Data
masking
AI data cleansing
(e.g. for MDM)
Database
modeling
Transformation
(as in ETL)
Reviews (design,
architecture)
and more….
Data guidance
(standards, guidelines, …)
Database
refactoring
Defect
logging
Executable business
rules (in the db)
Synthetic training
data
Manual regression
testing

21. EthicalConsiderations
withAI
AI magnifies and accelerates your existing biases
© Ambysoft Inc. 21

22. © Ambysoft Inc. 22
Ethical Consideration:
Safety
Have you considered the potential of harming people?
Have you considered physical harm, mental harm, financial harm, … ?

23. © Ambysoft Inc. 23
Ethical Consideration:
Human Dignity
Do you understand the overall process that the AI is part of?
Does your model take societal values into account?
Does you model take multi-cultural considerations into account?

24. © Ambysoft Inc. 24
Ethical Consideration:
Fairness
Have you actively considered bias in the development of your model?
Is your model appropriately gender/race/culture/age neutral?
How do you know?

25. © Ambysoft Inc. 25
Ethical Consideration:
Accountability
If the AI makes a decision, who will be held responsible for it?
Can you explain how a decision was made?
Do you know the provenance of the data used to train your model?

26. © Ambysoft Inc. 26
Ethical Consideration:
Trustworthiness
Can your model’s inferences/predictions be challenged?
Are they explainable?
Are they fair?

27. Business
ImplicationsofAI
AI will force you to rethink everything
© Ambysoft Inc. 27

28. © Ambysoft Inc. 28
Potential Risks of AI
• Rapid, dramatic, and public failure
• Reputational risk
• Unwarranted trust in AI-generated results
• Unrealistic expectations of AI capability
• Biased decision making
Potential Benefits of AI
• Augment the capabilities of your staff
• Provide new/improved offerings to your
customers
• Enhance existing business processes
• Replace existing business processes
• Increase the reach and scale of your
customer offerings
• Increased operational efficiency
• Increased consistency in decision making
• Improved governance

29. SuccessandFailure
FactorsofAIInitiatives
Are you ready?
© Ambysoft Inc. 29

30. Are You Ready for an AI Initiative?
© Ambysoft Inc. 30
Do you have realistic expectations?
Do you understand the ethical issues surrounding AI?
Are your leaders capable?
Is your data ready?
Can you build diverse, cross-disciplinary teams of dedicated staff?
Have you defined your way of working (WoW) for the initiative?

31. Would you like this presentation
for your chapter, user group, or organization?
Reach out to me at ScottAmbler.com
© Ambysoft Inc. 31

32. Thank You!
© Ambysoft Inc. 32
• scott@scottambler.com
• X: @scottwambler
• linkedin.com/in/sambler/
Consulting Methodologist
Ambysoft.com
Thought Leader
AgileData.org
Thought Leader
AgileModeling.com
Co-Creator
pmi.org/disciplined-Agile
Download these slides at Slideshare.net/ScottWAmbler/

33. Additional Slides
© Ambysoft Inc. 33

34. The Agile Data Mission
To share proven agile and lean strategies
for data initiatives.
Learn more at AgileData.org
© Ambysoft Inc. 34

35. The Agile Modeling Mission
To share proven and effective strategies for
modeling/mapping and documentation.
Learn more at AgileModeling.com
© Ambysoft Inc. 35