Where AI is (and isn’t) Helping Companies Scale and Profit
Artificial Intelligence Impact

Take-Aways from This Talk
• How and when you might use AI to further your business
goals
• Ideas will be based on examples of real business use cases,
and the opinions of experiences executives and investors

Outline of the Talk
• Background
• What Investors Look for in AI Applications (1, 2, 3)
• Examples of AI in Industry
• Applying AI toYour Own Business Model
• End

Points of Note
• Write down companies and use cases that are interested in to
you
• See if ideas or presidents of AI’s use might apply to your
business now or in the future
• After the “Investors Perspective” section and the “Examples”
section, we’ll have some time to riff on these ideas and have you
ask questions as we move
• If you have questions as we move along, please feel free to just
chime in

Background Brief
• I’m Dan Faggella, CEO/Founder atTechEmergence.com
• Interview top execs and researchers, Conduct unique
market research in AI space, Connect AI vendors to buyers
through a content network

What Investors Want
• https://www.techemergence.com/how-investors-feel-about-
artificial-intelligence-from-29-ai-founders-and-executives/

What Investors Want (1)
• Business Problem First,Technology Second.
• If the term “artificial intelligence” gets an investor excited,
they’re a poor investor, and their stake in your company may
be a hinderance to raising future rounds. Smart investors
want a specific business problem solved for a large
addressable market. If you have no compelling case as to why
AI is the best method for solving this business problem, an
intelligent investor will not care.

What Investors Want (2)
• The Proprietary Data Plume of Defensibility.
• Involves the ongoing creation of data through business
operations (IE: not buying it from outside sources)
• Involves adequate collection and organization of that data so
that it can be put to use
• Implies that this date is useful in improving operations
• Implies that this data is inaccessible or near-inaccessible to
your competitors

What Investors Want (2)
• The Proprietary Data Plume of Defensibility.
• Ex:Tracking the average time and miles to get from point
(a) to point (b) within a city, controlling for time of day,
holidays, weather
• Ex:Tracking logins, views, ratings, and stick-rate Netflix, while
also including demographic info
• Ex:Tracking engagement, usage, while also tracking all in-app
purchases and customer lifetime value

What Investors Want (2)
• The Proprietary Data Plume of Defensibility.
• What we need: (a) Are we tracking to the end result we
are after? (b) Are we tracking the necessary contextual
factors that will give us an accurate picture

What Investors Want (3)
• Improvement with Use / Winner-Takes-All
• Can your normal operations create a positive feedback
loop for more and more effective operations? Can your
interactions with each user yield results and lessons that will
translate to improved experience for or profitability from
future users?

What Investors Want (3)
• Improvement with Use / Winner-Takes-All
• Ex: InsideSales (collects data it can’t share, but can apply
those lessons across industry)
• Ex: CaseText (can’t access law data others can’t… but can
determine user behavior around their product and make
the most helpful, intuitive, efficient)
• Ex: Google (collects data others cannot about searches /
clicks on searches and ads)

Examples of AI in Industry
• Some businesses will need AI right away, some may use it over
time to beat out competitors and get a competitive
advantage.Most startups will fall into the latter category due to:
• Extreme rarity of AI talent needed to build something legit
• Extreme expense of AI talent needed to build something
legitimate
• The fact that most business problems don’t initially require
AI as the only or best solution

Examples of AI in Industry
• In some businesses,AI is completely required in order to get the business
off the ground in the first place. Businesses like these can’t exist without AI:
• PatternEx (anomalie detection)
• NervanaSys (core AI tech)
• Nuance (translation technology, specifically… classification of sounds)
• Viv (classification of sounds)
• Zebra Medical Imaging (classification of images)
• UpTake (pattern recognition)

Examples of AI in Industry
• For some businesses,AI is not totally necessary, but can aide functionality
over time:
• Consumer multi-sided platforms:AirBNB / UpWork / Uber (multi-sided
platform matching optimization / recommendations)
• Consumer social media platforms: LinkedIn / Facebook (engagement
optimization)
• Consumer commerce and media: NetFlix / Pandora / Amazon
(recommendations based on individual / group activity)
• B2B: InsideSales / PatternEx / SiftScience / UpTake (more customers
using it = more learnings for other customers)

Lenses of Improvement
• First, don’t do “toy” applications, and don’t “tinker” with AI.Too
much time / resources are required. If you don’t see any
evident reasons to implement AI, and you don’t have the
money, time or organization to do it… then shelf it as an idea
for now. So don’t take this and go DO anything with it unless
you must… otherwise you won’t be able to justify completing
the project and you’ll waste investor money.

=
ROI
Criterion

=
Miscon-
ceptions

Lenses of Improvement
• Ask yourself:
• Are we collecting information from individual users that could be used
to improve results for all users?
• Outward-facing: PatternEx
• Inward-facing: Uber
• Are we handling and classifying large volumes of text, audio, image, or
video data - which might be low-hanging-fruit for AI automation?
• Neither of these should be done until the value of their
implementation validates their cost in time and resources

That’s All, Folks!
• Feel free to stay in touch with questions or speaking requests:
• @danfaggella
• dan@techemergence.com
• TechEmergence.com