1. - an Overview of Impact
Daniel Faggella, CEO at TechEmergence
AI in Restaurants and Food Services
@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 SMBsToday
2. Forward-LookingTrends of AI in Food
Services
3. Becoming “Hype-Proof” - What to Pay
Attention to in the Future
@danfaggella

4. Expectations for This Talk
This is not a pep talk about AI.
MOST of what’s happening in AI should be ignored by SMBs in
food services, and only a few trends are probably worth taking
seriously in long-term strategic planning.
I’ll be showing a representative set of AI applications - some of
which you might not have seen before - but the ROI of this talk
will be in helping you “tune out” what doesn’t matter, and hone
in on what does.
@danfaggella

5. Expectations for This Talk
Also - don’t worry about taking fast notes, because:
•I will be sticking around after the presentation to chat
•I will make my email available so that you can get the full list of
“resources” and past articles where we explore all these use-
cases in greater depth
@danfaggella

6. The State of AI in SMBs
• Make no mistake about it: It’s mostly pilots, testing (not
concrete ROI)
• 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)
@danfaggella

7. • Vendor applications are almost exclusively being developed
for larger firms (bespoke, custom, complex applications that
require data at scale)
• Talent, budget, time required to build one’s own AI
applications (never mind robotics applications) is gargantuan
• “Food services” isn’t getting that much attention as it’s own
distinct niche, not nearly as much as domains like eCommerce
or Pharma or other industry segments
AI Challenges for SMBs in
Food Services
@danfaggella

8. Use-Cases of AI in Enterprise
Note that restaurant is not listed specifically, though customer
service and marketing applications are relevant to this sector.

9. Roughly:
‘Old’ artificial intelligence: “Baking” human knowledge into
if-then rules, allowing machines to replicate decision-making in
a way similar to humans within a limited domain. Does not
respond to data in the real world. (Examples)
Machine learning:Training a set of “nodes” to detect
underlying patterns in reams of data in order to predict
outcomes or take action. Responds to real-world information.
(Examples)
What is “AI” and What is
“Machine Learning”?
@danfaggella

10. Robotics
1.Delivery (Domino’s)
2.Food Prep (Miso Robotics)
@danfaggella

11. Robotics
3. Cooking (Moley Robot - Intended for home use)
@danfaggella

12. Robotics - Synopsis
• Absolutely not relevant for SMBs in the near term.This is far
from viable and scalable even for the biggest companies in
food services.
@danfaggella

13. Robotics - Synopsis
• What will lead to more investment and startups in this space?
Whenever the minimum wage is raised.
@danfaggella

14. Kiosk Technology
1.McDonald’s: Recommend different products depending on
season and weather
2.Wendy’s: No clear AI use-case here, though the company did
open up an emerging tech lab
3.KFC: In Asia they’re working on making product
recommendations based on the age, gender, and mood of the
customer in line
@danfaggella

15. Kiosk Technology
There is a small number of companies now working on smart
Kiosk test.“Bite Inc” is one such firm - but they only have 2
employees, one is a developer in Poland.This trend is far from
broad adoption.
@danfaggella

16. Kiosk Technology - Synopsis
• For more SMBs, these applications are extremely likely to use
AI in any way. Recommendations / optimization of purchase
orders with AI in Kiosks seems to be speculative even for the
big players.
@danfaggella

17. Kiosk Technology - Synopsis
• What will lead to more investment and startups in this space?
Whenever the minimum wage is raised.
@danfaggella

18. Chatbot / Conversational
Interfaces
This is a massive area of focus, and doesn’t necessarily need
AI in order to be useful.All restaurants want to “own” their
customer relationship, and if people get used to ordering on
an app before lunch, that might garner more market share
and even less human order-taking.
I suspect almost all of the current food services giants here
are referencing Starbucks as the technology leader here.
@danfaggella

19. Chatbot / Conversational
Interfaces
Anytime you lead a new technology adoption, it’s HARD
work. Starbucks did that with their app, they’re doing to try to
do it with their app - beginning withVoice ordering, but other
than their press release, it’s unclear if the new interface has
traction with customers.
@danfaggella

20. Chatbot / Conversational
Interfaces - Synopsis
While there are plenty of pilots of these kinds of applications,
and some functioning applications, it’s unclear how long it will
take until the medium of “Chat” becomes a commonplace
channel for ordering food, delivery or otherwise.
^ Domino’s is probably worth following when it comes to
new user interfaces for delivery orders.
What is clear is that large companies will be the ones with
the budget and the data volume to build effective bots of this
kind.
@danfaggella

21. Consumer Platforms /
Recommendation Tech
• AI will probably intersect with the SMB restaurant space first
through consumer AI tech, not restaurant AI tech. Examples:
• Existing platforms likeYelp, Google Maps, and their
competitors will likely begin adopting technology of this
kind in the years ahead.
• One such example of a new app is Halla, a
recommendation engine for restaurants based on location
and user preferences.
@danfaggella

22. Consumer Platforms - Synopsis
This will impact many restaurants in the next 2-3 years, but
“adapting” to these apps probably won’t be much different
than when restaurants learned to make “findable” listings on
Yelp, Google Maps, etc.
No real data science work will be needed on the part of
restaurants themselves.
@danfaggella

23. Analytics Solutions and AI
There is almost no attention on this space in terms of press or
current applications that we can find, but it’s low-hanging fruit.
We don't think it will be long until products like UpServe,
OpenTable,Venga, PosIQ use predictive analytics to help with
insights such as:
• Predicting visitor traffic
• Predicting food orders / inventory needs
• Better forecasting revenues / costs
@danfaggella

24. How AI Will Make it’s Way into
SMB Food Services
• In the near-term, it won’t.
• Large firms will use their huge budgets and longer time
horizons to explore what’s viable and what isn’t for AI
applications (we still have no idea what AI applications will
become “norms” in this space).
• Eventually, applications will be trained on huge volumes of big
company data, and this data will be made use-able for smaller
companies (Example).
@danfaggella

25. How AI Will Make it’s Way into
SMB Food Services
• In the interim, consumer AI applications will impact the
restaurant industry, but this won’t involve data science on the
part of the companies themselves.
• Anything that smells like a “gimmick” is one.“Build-it-yourself”
chat-bots, etc…
• There may be some marketing automation tools for
restaurants that will integrate AI in the next 2-3 years, but it
won’t involve data science chops to use them.
@danfaggella

26. 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

27. Adoption Curve
90% of SMB food services companies will be in the late majority,
with only some in the early majority.There’s nothing wrong with
that.
@danfaggella

28. Concluding Thoughts:
Wastes of Time
1. Reading about AI applications via blogs and social
media and feeling a sense of missing out (it’s a brag fest
to seem “innovative”)
2. Seriously considering hiring in-house data-science
talent at scale (especially for firms under $20-50MM
per year)
3. Building a “chatbot” because “everyone else is doing
it” (don’t allocate funds for “fear of missing out” and
nothing else)
@danfaggella

29. Concluding Thoughts:
To Dos
1. Stay aware of which apps and consumer platforms are gaining
popularity - and carefully allocating time to platforms that might
deliver value to your firm (this involves no AI talent, no data science)
2. Keep tabs on which AI applications are becoming “norms”, fleshed-
out use-cases for public companies. Particularly:
a. Conversational interfaces / chatbots
b. AI for marketing and advertising
c. Business intelligence, inventory management, predictive
analytics
@danfaggella

30. That’s All, Folks
For questions about this talk, or to learn about our strategic
advisory and market research services, contact:
Daniel Faggella - dan@techemergence.com
@danfaggella

31. Resources (1)
• TechEmergence articles about retail AI:
• https://www.techemergence.com/ai-in-restaurants-food-services/
• https://www.techemergence.com/fast-food-robots-kiosks-and-ai-use-cases/
• https://www.techemergence.com/ai-in-food-processing/
• Machine learning in marketing:
• https://www.techemergence.com/machine-learning-marketing/
• Applying AI to Business Problems (General Understanding):
• https://www.techemergence.com/how-to-apply-machine-learning-to-business-
problems/

32. 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.