The document discusses challenges and opportunities in applying artificial intelligence (AI) solutions. It begins with an introduction of the speaker, Adam Stotz, and his experience building AI solutions. It then addresses common myths and misconceptions about AI, and discusses the main challenges organizations face in implementing AI, such as issues with data, hiring data scientists, process alignment, and software complexity. The document argues that organizations are struggling to become data-driven and that $1.2 trillion is lost annually due to a lack of insights. However, it also provides examples of successful AI implementations and suggests areas of focus to successfully apply AI, such as having a clear business case and problem statement and ensuring sufficient training data.

1. Prepared for:
• An AI Software Company with ~20 Data Scientists and Software Engineers
• Serving Electric Utility and Financial Markets in USA
• Headquartered in Buffalo, NY
Adam Stotz, PhD
TROVE CTO
15 Years Experience Building AI Solutions for US DoD,
and now Utility and Financial Markets

2. What is it?
What are the myths?
What are the challenges in applying an AI solution?
How to identify areas of opportunity?

3. © 2018 TROVE. ALL RIGHTS RESERVED.
PROPRIETARY AND CONFIDENTIAL. 3
WHAT IS AI?
Analytics vs Data Science vs Machine Learning vs AI

4. 4
© 2018 TROVE. ALL RIGHTS RESERVED.
PROPRIETARY AND CONFIDENTIAL.
STAND YOUR GROUND!
Let’s poll the audience with their opinions on AI…
Stand up
if you agree
Sit down
if you disagree
Yes!! No!!

5. 5
© 2018 TROVE. ALL RIGHTS RESERVED.
PROPRIETARY AND CONFIDENTIAL.
Yes!!
No!!
Who thinks AI will have a
transformational impact on their
business in the next 5 years?

6. 6
© 2018 TROVE. ALL RIGHTS RESERVED.
PROPRIETARY AND CONFIDENTIAL.
Yes!!
No!!
Who has realized a
transformational impact on their
business from AI to date?

7. 7
© 2018 TROVE. ALL RIGHTS RESERVED.
PROPRIETARY AND CONFIDENTIAL.
Yes!!
No!!
Who has taken some swings and
missed or has had a hard time
getting started?

8. © 2018 TROVE. ALL RIGHTS RESERVED.
PROPRIETARY AND CONFIDENTIAL. 8
MYTHS SURROUNDING AI
POP CULTURE BUSINESS CULTURE REALITY

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PROPRIETARY AND CONFIDENTIAL. 9
NARROW INTELLIGENCE = STATE-OF-THE-ART
Help Clients overcome “Crystal Ball Syndrome”
TROVE “Solver”
https://neurogadget.net/2018/03/08/difference-general-ai-narrow-ai/56652

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WHEN AI GOES WRONG
In 2017, Amazon Fired
Its Resume-Reading AI
for Sexism – after
multiple rounds of
calibration, could not
get the AI to not favor
males.
In 2017, Facebook’s AI
chatbots shut down
after they started
talking to each other in
their own language.
In 2016, a Russian
robot named Promobot
IR77 kept trying to
escape from the
research facility it was
being developed in –
even after being
reprogrammed twice

11. 11
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PROPRIETARY AND CONFIDENTIAL.
CHALLENGES PUTTING AI INTO PRACTICE
DATA
 Difficult and costly
to organize, make
discoverable, and
assess health
 Don’t know where
to begin to harvest
vast wealth of
external data
DATA SCIENTISTS
 Can’t acquire and
retain top talent
 Difficult to find
diversity of skill sets
required
PROCESS
 Poor alignment
within organization
to get beyond silos
 Don’t drive value
through big ideas
 Cultural challenges
prohibit success
SOFTWARE
 Software platforms
are too complicated
 Lack of required skill
sets to scale AI

12. 12
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PROPRIETARY AND CONFIDENTIAL.
ORGANIZATIONS ARE STRUGGLING TO BECOME
DATA SCIENCE-DRIVEN BUSINESSES.
$1.2
Trillion
Lost
Annually
“Bad news for many firms claiming to be insights-driven — the term is overused.
While many are fooling themselves, those that are truly insights-driven
businesses will steal $1.2 trillion per annum from their less-informed peers” 1
1. https://go.forrester.com/wp-content/uploads/Forrester_Predictions_2017_-Artificial_Intelligence_Will_Drive_The_Insights_Revolution.pdf
OPPORTUNITY

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WHEN AI GOES RIGHT
Outbid 2 major
networks and invested
$100M for the first two
seasons of House of
Cards without ever
seeing a pilot.
Using AI to power self-
driving features.
Forecasts value will
grow 10x to $500B due
to self-driving tech.
Invested $1B in AI-
driven asset
performance to save
$2B over the next 1-
years.

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SOME THINGS TO LOOK FOR…
Is the business case defined and the question is “How”?
Is the problem statement narrowly focused or general in nature?
Is it hard to define the business rules (IfThen) that would solve the
problem?
Is there sufficient “training data”?
Numerous examples of [Input, Desired Output] Pairs
Does the schedule allow for research and experimentation?

15. Obsessed with making data useful .
Adam Stotz
CTO
astotz@trovedata.com
716.276.3541