Artificial Intelligence is definitely having its moment, but if you’re like most companies, you haven’t yet been able to capture ROI from these exciting technologies. It seems complicated, expensive, requires specialized talent, crazy data requirements, and more. Your boss may have dropped a vague missive onto your desk asking you to “figure out how AI can help enhance our business.” You have piles and piles of unstructured content—contracts, documents, feedback, but you haven’t been able to drive value from your data. Where to even start? We’ll show you how. Hear Indico’s CEO Tom Wilde and Intellyx’s Jason Bloomberg's perspectives in this valuable and practical webinar to start your AI journey to success. In this webinar you will learn: - An understanding of the “alphabet soup” of AI and which technology is right for you—including Machine Learning, Deep Learning, Transfer Learning, and more - A framework for developing use cases that can benefit from AI - The building blocks for AI success - A methodology for designing in ROI from the outset

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2. Getting to AI ROI
Jason Bloomberg
President
jason@intellyx.com
@theebizwizard
Copyright © 2018, Intellyx, LLC

3. About Jason Bloomberg
• President of
industry analyst
firm Intellyx
• Latest
book The Agile
Architecture
Revolution
• Published the Agile Digital
Transformation Roadmap poster
Copyright © 2018, Intellyx, LLC3

4. AI: The Big Picture
• AI for automation
– Tasks: make people more efficient,
‘augmented’ intelligence
– Roles: make businesses more efficient,
requires reskilling
• AI for business insights
– Data crunching that improves over time
– Predictive insights
• AI for simulating human behavior
– Virtual assistants, chatbots, etc.
– Natural language processing essential
Copyright © 2018, Intellyx, LLC4
PhotoCredit:ITUPictureshttps://www.flickr.com/photos/itupictures/

5. The Growth of AI: Investment
Copyright © 2018, Intellyx, LLC5 2017 AI Index Report, http://aiindex.org/

6. The Growth of AI: Skills
Copyright © 2018, Intellyx, LLC6 2017 AI Index Report, http://aiindex.org/

7. The Growth of AI: Revenues
Copyright © 2018, Intellyx, LLC7
https://www.statista.com/statistics/607612/worldwide-artificial-intelligence-for-enterprise-applications/

8. AI Challenges
• Bad PR
– Killer robots?
• Stalled innovation
– Another AI winter?
• Ethical issues
– Who’s to blame for
autonomous vehicle crashes?
• Data issues
– Data veracity, data
governance, etc.
Copyright © 2018, Intellyx, LLC8
PhotoCredit:narcosislabshttps://www.flickr.com/photos/narcosislabs/

9. Who is Using AI
• IT
– Threat detection, problem
resolution, root cause
analysis, runbook automation,
next-best action
recommendations
• Vertical industry data analysis
– Well data in oil & gas, wear &
maintenance analysis in heavy
industry, etc.
• Marketing
– Purchase recommendations,
social media analysis, media
buying, promotion
customization
• Finance
– Automated trading
• Customer Service
– Call distribution, automated
voice response, virtual
assistant technology
Copyright © 2018, Intellyx, LLC9

10. Why AI is So Difficult
• Esoteric theoretical basis
– Demand for skills exceeds supply
• Machine learning requires massive
quantities of ‘good’ data
• State of the art constrained by
context
– General purpose AI still well out of
reach
Copyright © 2018, Intellyx, LLC10
PhotoCredit:edwardhblakehttps://www.flickr.com/photos/eblake/

11. Key Takeaways
• AI is providing real value
today
• AI is increasingly
important for overall
competitiveness
• Forget Hal and Skynet!
Copyright © 2018, Intellyx, LLC11
PhotoCredit:RobertCouse-Bakerhttps://www.flickr.com/photos/29233640@N07/

12. Jason Bloomberg
President, Intellyx
jason@intellyx.com
@theebizwizard
Download poster at AgileDigitalTransformation.com
Thank You!
Copyright © 2018, Intellyx, LLC

13. Introduction to Enterprise AI
History, Opportunities & Challenges

14. Tom Wilde
CEO | Indico
tom@indico.io
25 Years in Enterprise Search
industry and industry expert in
unstructured content
technologies and solutions.
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AI:
A Brief
History
>
Source: Nvidia

16. State of the Market
16
No shortage of
hype, but what is
Enterprise AI and
how can we benefit
today?

17. State of the Market
17

18. unstructured
content
challenges
>
$$$ Expensive
Error Prone
Manual

19. What’s the Big Deal?
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20. What’s the Big Deal?
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21. Artificial Intelligence –
Any computer program which
automates a process typically
assumed to require human
intelligence. This may be
achieved through any number
of tools including, but not
limited to, machine learning
and deep learning
Let’s Start with Some Definitions
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Let’s Start with Some Definitions
Data Science –
A generic set of skills including
machine learning, deep
learning, and transfer learning
used to produce enterprise
value from data through
understanding, automation,
and optimization.

23. Machine Learning –
A field of computer science
that focuses on “teaching”
machines to make decisions
and determinations based
on data rather than relying
on explicit programming
23
Let’s Start with Some Definitions

24. Deep Learning –
A set of machine learning
algorithms based on neural
networks that have become
increasing popular in recent years
due to their near-human levels of
performance for tasks involving
unstructured data – primarily text,
image, and audio data.
24
Let’s Start with Some Definitions

25. Transfer Learning –
A deep learning
method where a
model developed for
a task is reused as
the starting point for
a model on a second
task.
25
Let’s Start with Some Definitions

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Building Blocks for Success
1. Data
• Data prep is single most
important aspect of ML
• Labeled data that
sufficiently captures the
desired outcome is critical

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Bulding Blocks for Success
2. Compute
• ML systems can run on
CPU
• Deep Learning systems
require GPU

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Building Blocks for Success
3. Expertise
• Subject matter experts who
understand the business
problem
• Some level of data science
understanding to interpret
and refine approach

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Building Blocks for Success
4. Definition of Success
& ROI Hypothesis
• A defined outcome (with consensus
across stakeholders) connected to
a tangible business benefit
• Ability to define the costs of the
current process- both hard costs
and opportunity costs
• Patience- several iterations will be
required. Scientific method.

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The “Prime Elements” of Enterprise AI
Classification and
Regression
Unsupervised
Discovery
Comparison
Search
and
Extraction
IncreasingDifficulty

31. Use Cases for Unstructured Content
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Content Analytics
● Resume Screening and Analysis
● Content and Image Classification
● Customer Feedback and Sentiment Analysis
Process Automation
• Content Process Automation
• Automated contract analysis
• RFP analysis and enhancement
• Risk & Compliance analysis
>

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In Summary: Getting to AI ROI

33. Questions?
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