This presentation with given to the Business Executives for National Security group in Atlanta, GA. The presentation is an overview of the state of AI in enterprise, and what business leaders should do when considering artificial intelligence solutions and evaluating vendors.

1. How AI is (and isn’t) Making it’s Way into Enterprise
Daniel Faggella, CEO at TechEmergence
AI Flips and Flops
@danfaggella

2. Background Brief
I’m Dan Faggella, CEO/Founder atTechEmergence.com
We’re a market research and media firm with one goal:To cut
through hype and show business leaders the implications,
applications, and important companies in artificial intelligence.
We have business readers all over the world (biggest following
in SF, NYC, Bangalore, London).
@danfaggella

3. Outline of the Talk
1. The State of AI in the EnterpriseToday
(Including Common Use-Cases)
2. How to Decide to Adopt or Wait on AI
3. ConcludingThoughts /Take-Aways
@danfaggella

4. Why it Matters
• Machine learning will likely overhaul entire industries in the next 15
years (will be essential in security, customer service, marketing, BI/
analytics)
• We interview hundreds and hundreds of execs and researchers, and
while these all have different opinions on timelines, they agree on the
inevitability of AI transforming industry, much the same way the
internet did
• AI will impact your business, but you need to know what to pay
attention to.You will be bombarded with hype and news about AI and
you should know how to “prime your antennae”, paying attention only
to what matters, and that’s what I’ll help you with in this presentation
@danfaggella

5. State of AI in Enterprise
• Make no mistake about it: It’s mostly pilots
• For every 100 “AI companies”, we’ve found that only 1/3 is
actually leveraging AI in any serious way, and only 1/3 of those
companies are past the stage of “piloting” their product or
service (Maybe 1 in 10 “AI” companies is actually selling
something that has had a positive impact on a business)
• Pay no attention to buzzwords and marketing, but pay close
attention to real case studies and real applications of industry-
leading organizations.
@danfaggella

6. • Common cardinal sin:“Toy” applications
• “Toy” applications are technologies or projects taken on because
they use AI, not because they solve a business problem.Vendors
play into this because they need guinea-pigs to “pilot” products,
and they’ll sometimes encourage closing deals even if they aren’t
well organized
• They almost all end the same way: Lacking resources to back
them, lacking gusto to carry them through, and negatively
impacting the funds and human resources of the company (and
making the “toy” initiator into a fool).
State of AI in Enterprise
@danfaggella

7. Everyone can load their data into Hadoop… but doing something with
that data (in terms of ML applications) often reveals structural problems:
• No ML talent in place (and consultants can’t do it all for you - see
Machiavelli’s quotes on auxiliary troops)
• More importantly, no management structures in place to (a) deal with
uncertainty of AI applications (there is no guanantee on if they will
work, or when), (b) deal with the lengthy R/D process of AI, (c) getting
buy-in or understanding from the top
• Some vendors / consultants I’ve spoken with think that acquisitions will
be the way that enterprise innovates, not R/D
Biggest Challenges
@danfaggella

8. =
ROI
Criterion
@danfaggella

9. =
Miscon-
ceptions
@danfaggella

10. Examples and Use-Cases (Now)
@danfaggella

11. Examples and Use-Cases (Now)
• Currently, finance and healthcare are generally viewed as the
sectors with the most demand for AI solutions now. In finance
this is mostly in fraud and security, in healthcare, this is mostly in
discovery and diagnosis
• This coincides with the venture investment in the AI sector
(which is pouring into finance and healthcare)
• This coincides with the traffic we see on our site. For us,
interestingly enough, healthcare is the category that garners
the most traffic, and finance is #2 (with robotics, marketing,
and other sectors trailing behind)
@danfaggella

12. • Think about AI adoption the same way you would think of
adoption of any other emerging technology that you consider
to be essential to the future of your industry.You might test
and try with a little most gusto because AI is indeed
inevitable, but don’t be rash for “FoMo” sake!
• AI adoption should involve an informed, forward-looking
industry / competitive analysis (like the kind that you’d do at a
quarterly off-site), nothing less.
Where AI “Should” Be Used
@danfaggella

13. Adopt or Wait?
• For any given AI application area (marketing, business
intelligence, procurement, etc), determine where you want to
be on the adoption curve (FEW established firms must be or
should be “innovators” or even “early adopters”)
@danfaggella

14. Adopt or Wait?
• By guess is that most established firms will be somewhere in
the early or late majority, with maybe some applications in the
“early adopter” category.
• Critical vendor question 1:“How have you helped companies
like mine get results like those that I’m looking for?”
• Critical vendor question 2:“If your company was unable to
raise any more capital, could you pay your bills?”
@danfaggella

15. Concluding Thoughts
I would advise:
1. Get a firm sense of what kinds of problems AI can solve (broadly), and
what AI investments and AI ROI are happening with the biggest players in
your industry
2. Consider the domains within your business that can most benefit from
the use of AI
3. Talk to companies who have developed and implemented similar
applications and get a realistic understanding of what it would take to
implement them yourself
4. From THERE, make your decision on whether or not to invest in AI, to
develop AI, to acquire AI, etc… (NO TOYS!)
@danfaggella

16. That’s All, Folks
If you’d like to stay in touch via our newsletter, and get our latest
research on AI investment areas across industries, and what large
organizations are currently spending (and planning on spending) on AI,
then:
Email: “AI” to info@techemergence.com and I’ll reply in the coming
week. Feel free to include any relevant questions from this presentation
and I’ll aim to address them all personally on my flight home
@danfaggella

17. Resources
• https://hbr.org/2017/04/the-first-wave-of-corporate-ai-is-doomed-to-fail
^ Quote from this article:“We believe AI will indeed transform industries. But the companies that
will succeed with AI are the ones that focus on creating organizational learning and changing
organizational DNA”
• https://hbr.org/2017/04/how-companies-are-already-using-ai
^ Good article, but author is downplaying the job automation concerns of AI.All big, bloated
consulting companies do this, be wary of people-heavy companies assuring everyone that AI won’t
replace people.
• http://www.gartner.com/smarterwithgartner/artificial-intelligence-and-the-enterprise/
^ Most relevant part of this article is the third question “How will AI impact the talent needs of an
organization?”
• https://hbr.org/2017/06/if-your-company-isnt-good-at-analytics-its-not-ready-for-ai
^ Extremely useful perspective on the “baby steps” needed to begin working with AI seriously.