1. Examples, Trends, and Potential for Application
Daniel Faggella, CEO at TechEmergence
AI in Pharma - Where it Matters
@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
2. Forward-LookingTrends of AI in Pharma
3. Issues with Applying AI in Pharma
4. Becoming “Hype-Proof” - What to Pay
Attention to in the Future
@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. 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 guarantee 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

7. =
ROI
Criterion
@danfaggella

8. =
Miscon-
ceptions
@danfaggella

9. Use-Cases of AI in Enterprise
Note that pharma / healthcare are not listed as commonplace at this time.

10. Pharma - Trends (1)
“Milking” Existing Drugs Are the Focus
• Creating new drugs is massively expensive and time consuming
• Hence, current efforts seem to be geared more towards
matching patients to drugs (and not creating new drugs per
patient)
• Drug repurposing is a potentially important use case
Vendors: Qrativ, Berg Health
@danfaggella

11. Pharma - Trends (1)
SOME Co’s / Partnerships are Focused on New Drugs
• Sanofi and Exscientia
• GSK and Exscientia
• Roche and Berg Health
• ^These applications seem to have a longer turn-around
time to determine ROI, but could help inform drug-dev
initiatives
@danfaggella

12. Pharma - Trends (2)
Oncology is the Source of Some of the Richest Data
• Oncology initiatives by the US government seem to be
among the richest and most robust pharma data sets
• Immune system insights from oncology data seems to be “low
hanging fruit” for “connecting the dots” for personalized
medicine
Vendors: IBM Watson for Drug Discovery, GNS Healthcare
@danfaggella

13. Pharma - Trends (3)
Acquisitions Will Likely be Common
• Barriers to entry are huge for these startups, in biomedicine
these companies often get built to sell… it’s so hard to go to
market
• Large, old,“stodgy” businesses (drug giants) aren’t predicated
on data science (like modern Bay Area media or eCommerce
firms)… so buying that talent / innovation will probably be
the norm
@danfaggella

14. Pharma - Issues (1)
Accessing the Data is Hard
• Different data types (genetic, health records, image data, etc)
often exist in “silos”, and cleaning / accessing this data is often
very challenging in healthcare
• Any developments that help to make data accessible and
useable for AI will be very meaningful in pharma (including
systems that improve electronic health records)
@danfaggella

15. Pharma - Issues (2)
The AI Talent Crunch is Real
• Google,Amazon, and Facebook will often have the cash and
appeal to attract top AI talent
• Pharma companies will probably need to retrain and
“upgrade” the skills of their existing data science / statistics
teams
@danfaggella

16. • 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

17. • 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).
Cardinal Sin of AI Applications
@danfaggella

18. 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

19. 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

20. That’s All, Folks
Email info@techemergence.com and I’ll reply in the coming
week. Feel free to include any relevant questions from this
presentation.
@danfaggella

21. Resources (1)
• https://www.techemergence.com/machine-learning-drug-discovery-applications-
pfizer-roche-gsk/
^ Good examples of existing AI applications and initiatives by five of the
largest pharma giants.
• https://www.techemergence.com/ai-in-pharma-and-biomedicine/
^ More direct coverage of the AI use-cases at US pharma giants
• https://www.techemergence.com/qrativs-murali-aravamudan-whats-possible-ai-
drug-discovery/
^ A detailed interviews about the potential for drug repurposing with artificial
intelligence

22. Resources (2)
• 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.