1.
BUILDING AN
“AI STARTUP*”
( * Y E A H … W H AT E V E R
T H AT M E A N S ! )
TAT H A G AT VA R M A
S T R A T E G Y & O P E R A T I O N S , W A L M A R T
D O C T O R A L S C H O L A R , I N D I A N S C H O O L O F
B U S I N E S S

2.
DISCLAIMER
T H E S E A R E M Y P E R S O N A L V I E W S !

3.
TOP 3 REASONS TO
“BUILD” A STARTUP
(ANY STARTUP !)
1. Solve a real-world problem!
2. Solve a real-world problem!!
3. Solve a real-world problem!!!
PS: there are other equally meaningful and
“rewarding” ways to pursue interesting ideas,
complex problems, hobbies, and passions, etc.
Let’s just don’t call them a “startup”!

4.
GENERAL PRE-CONDITIONS TO SOLVING
ANY PROBLEM BY BUILDING A STARTUP
THERE IS A PROBLEM
WORTH SOLVING!
SOMEONE WANTS IT
SOLVED!
YOU ARE INTERESTED
IN SOLVING IT!

5.
WHY
STARTUPS
FAIL?

6.
WHY IS THE MARKET IMPORTANT?
• Markets that don’t exist don’t care how smart you are.
• In a great market - a market with lots of real potential
customers - the market pulls product out of the startup.
• The #1 company-killer is lack of market.
- Marc Andreesen
https://web.stanford.edu/class/ee204/ProductMarketFit.html

7.
SO WHAT KIND OF PROBLEM TO
SOLVE?
Potentially
affects many
people, or a
hard or a long-
term problem
Affects only a
few people, or
an easy or a
short-term
problem
People can or want to solve it
themselves (or even willing to
live with the status quo)
People potentially ready to pay
others or buy a solution or
service to solve their problem
Touch Me Not:
Even if the problem is interesting
enough, you probably have no market
because people are willing to solve in
“Do ItYourself” mode, or even live with
the pain but not buy a solution or pay
someone else to do it!
Self-Help Groups:
Perhaps a limited market, but might offer
interesting ideas to study, understand and
maybe even develop it further!
Ideal Opening:
Despite looking like a small
market, maybe offers
interesting opportunity ahead,
and a “natural” MVP testbed
to try out some ideas
Perfect Problem:
Ideal end-state scenario, but maybe
already too crowded (it is it too good,
expect cutthroat competition
already!), and hence not a great
choice for entry strategy

8.
WHAT TO BUILD?
“Don’t sell what you can make; make what
you can sell.And that means figuring out
what people want to buy.”
- Alistair Croll and BenjaminYoskovitz, Lean Analytics: Use
Data to Build a Better Startup Faster (Lean Series)

9.
SO, HOW TO KNOW WHAT PEOPLE
WANT?
Primary Research Secondary Research
User
reviews
Interviews
Observations
Polls
Focus
Groups
Surveys
Literature
survey
Internet
Industry
reports
Demographic
data
Customer
Feedback
Trials
Experiments
Ethnographic
Research

10.
HOW DO YOU KNOW YOU HAVE FOUND
THE RIGHT PROBLEM?
The key to qualitative data is patterns and pattern recognition. Here are a few
positive patterns to look out for when interviewing people:
• They want to pay you right away.
• They’re actively trying to (or have tried to) solve the problem in question.
• They talk a lot and ask a lot of questions demonstrating a passion for the
problem.
• They lean forward and are animated (positive body language).
- Alistair Croll, and BenjaminYoskovitz. Lean Analytics: Use Data to Build a Better Startup Faster

11.
SO, WHAT’S AN “AI COMPANY”?
• Clearly, no one single definition!
• AI company,AI-enabled company,True AI company,AI-led
company,AI factory,AI-first company….
• Bigger question to ask is why even bother about an “AI
company”…after all, do you ever say it is an “electricity
company” (rather than a lighting company) or a “timber
company” (rather than a furniture chain) or a “milk
company” (rather than a sweets shop), a “currency company”
(rather than a financial services), etc.

12.
STILL, WHAT’S AN AI COMPANY?
“A firm that assists, augments and potentially
replaces human judgment in decision-making
by delivering socially-acceptable and
appropriately explainable prediction solutions
by data-driven continuous learning.”
- My view (WIP!)

13.
HOWEVER…NOT EVERYTHING THAT
GLITTERS IS GOLD!!!
• A new report from London-based venture capital firm
MMCVentures found no evidence that artificial
intelligence was an important part of the products offered
by 40 percent of Europe’s 2,830 AI start-ups.
• The findings raise questions about how the term AI has
become a blanket phrase for start-ups looking to attract
investments.
https://www.cnbc.com/2019/03/06/40-percent-of-ai-start-ups-in-europe-not-related-to-ai-mmc-report.html

14.
WHAT KIND
OF PROBLEMS
SHOULD AI BE
USED FOR?
“If a typical person can do a mental
task with less than one second of
thought, we can probably automate it
using AI either now or in the near
future.”
- Andrew Ng,
https://hbr.org/2016/11/what-artificial-
intelligence-can-and-cant-do-right-now

15.
CHOOSING PROBLEMS FOR “AI
STARTUPS”
“Investors are interested in startups that are building tailored AI
solutions for previously unsolvable problems,” said Jay Srinivasan, who is
the CEO and co-founder of atSpoke.“So focus on areas where there
are many inefficiencies and repetitive human processes, such as
call centers and back-office paperwork processing. Investors want
successful AI solutions that address specific workflows and problems,
such as a legal document review.” -
https://www.forbes.com/sites/tomtaulli/2020/08/08/how-to-create-an-ai-
artificial-intelligence-startup/?sh=44cd988719a4

16.
WHAT ARE YOUR “PAIN METRICS”
• Hollinger, Ph. D., who directs the National Retail Security
Survey, retailers lost 1.7 percent of their total annual
sales to inventory shrinkage last year.The surveyed portion of
the retail economy transacts over $1.845 trillion dollars
annually, making the loss worth over $31.3 billion.
• Based on a number of different ecommerce studies, the
average shopping cart abandonment rate is 68.81% with the
most recent study showing 74.52%.
• Indian public sector banks collectively owed
approximately 6.17 trillion Indian rupees in non-performing
assets in fiscal year 2021.

17.
SOLVING THE DATA ISSUE!
• Problem domain, customers, etc.
• Government (data.gov, data.gov.in, data.worldbank.org, etc.)
• Companies (PhonePe Pulse,
https://cloud.google.com/solutions/datasets, etc.)
• Data brokers
• Synthetic data
• …

18.
ESTABLISH THE “FINISH LINE”
“Without productivity objectives, a business
does not have direction.Without
productivity measurements, it does not have
control.”
- Peter Drucker, Management:Tasks, Responsibilities,
Practices

19.
LEAN ANALYTICS
APPROACH
Much of Lean Analytics is about
finding a meaningful metric, then
running experiments to improve it
until that metric is good enough
for you to move to the next
problem or the next stage of your
business.
- Alistair Croll and BenjaminYoskovitz. Lean
Analytics: Use Data to Build a Better Startup
Faster (Lean Series) (p. 27). O'Reilly Media. Kindle
Edition.

20.
ADOPTION ISSUES GALORE!
• 87% pilots never make it to production -
https://venturebeat.com/2019/07/19/why-do-87-of-data-
science-projects-never-make-it-into-production/
• 70% of companies report minimal or no impact from AI.
• It was estimated that 85% of AI projects will fail and deliver
erroneous outcomes through 2022.

21.
SOME FAILURES OF AI…
• IBM’s “Watson for Oncology” Cancelled After $62 million and
Unsafe Treatment Recommendations
• Microsoft’s AI Chatbot Corrupted by Twitter Trolls
• Apple’s Face ID Defeated by a 3D Mask
• Amazon Axes their AI for Recruitment Because Their Engineers
Trained It to be Misogynistic
• Amazon’s Facial Recognition Software Matches 28 U.S.
Congresspeople with Criminal Mugshots
https://www.lexalytics.com/lexablog/stories-ai-failure-avoid-ai-fails-2020

22.
ROI IS ELUSIVE!
The ROI for AI projects varies greatly, based on how much
experience an organization has. Leaders showed an average
of a 4.3% ROI for their projects, compared to only 0.2%
for beginning companies. Payback periods also varied, with
leaders reporting a typical payback period of 1.2 years and
beginners at 1.6 years.
https://www2.deloitte.com/us/en/insights/industry/technolog
y/artificial-intelligence-roi.html

23.
BEWARE
OF THE
TECH
HYPE!
https://www.gartner.com/en/articles/the-4-trends-that-prevail-on-the-gartner-hype-cycle-for-ai-2021

24.
SCALING-UP ISSUES
• Firm-wide end-to-end use cases
• Experimental learning,A/B testing, feedback
• Model performance, Concept drift
• Data governance, bias
• Explainability, transparency, traceability
• Social, ethical and safe
• Industrialization of AI, MLOps
• Internal organization design commensurate to delivering the goals of
an AI-led ROI
• …

25.
RECAP
• Technology ≠ Startup! Proving a technology in
the lab is rather “easy”, building a startup around
the idea is an altogether different ball game!
• Think ROI! While today’s AI is all about data,
think of what business problem you want to solve
and why?
• Data delivers value! While data is the biggest
source of competitive advantage for AI
companies, it is also the bane. However, there are
options available.
• Outside-inThinking! Think of building your AI
startup “outside-in” rather than “inside-out”