In this talk, I discuss how AI impacts the business, what are the challenges and what are the opportunities.

1. AI IN BUSINESS:
OPPORTUNITIES &
CHALLENGES
TATHAGATVARMA
VP STRATEGY, WALMART GLOBAL TECH
PHD SCHOLAR (EFPM), INDIAN SCHOOL OF BUSINESS (ISB)

2. DISCLAIMER THESE ARE MY
PERSONALVIEWS!

3. PERSPECTIVES ON AI

4. PROJECTED
ECONOMIC
IMPACT OF
$15 TRILLION
BY 2030…!!!
http://preview.thenewsmarket.com/Previews/PWC/DocumentAssets/476830.pdf

6. VIRTUALLY ALL
ASPECTS OF
MOST
BUSINESSES…
https://info.algorithmia.com/2021

7. BUT,THERE’S
ALSO A LOT
OF HYPE!!!
https://www.gartner.com/en/articles/the-4-trends-that-prevail-on-the-gartner-hype-cycle-for-ai-2021

8. …WITH ALARMINGLY HIGH FAILURE RATES!
• Gartner: 85% of AI projects will fail and deliver erroneous outcomes through 2022.
• MIT SMR: 70% of companies report minimal or no impact from AI.Among the 90%
of companies that have made some investment in AI, fewer than 2 out of 5 report
business gains from AI in the past three years.
• VentureBeat: 87% of data science projects never make it into production.Tom
Siebel says 99% of internal AI projects fail!
• Andrew Ng (HBR): It is not unusual for teams to celebrate a successful proof of
concept, only to realize that they still have another 12-24 months of work before the
system can be deployed and maintained.
https://research.aimultiple.com/ai-fail/

9. …AND LOW ROI & LONG PAYBACK PERIODS!
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/technology/artificial-intelligence-roi.html

10. SO,WHAT IS AI?
HINT:THERE IS NOTHING REALLY “INTELLIGENT” ABOUT IT!

11. WHAT IS “ARTIFICIAL INTELLIGENCE”, OR AI?
Prof John McCarthy, Father of Artificial Intelligence:
• Intelligence is the computational part of the ability to achieve goals in the
world.Varying kinds and degrees of intelligence occur in people, many animals
and some machines.
• Artificial Intelligence is the science and engineering of making intelligent
machines, especially intelligent computer programs. It is related to the similar
task of using computers to understand human intelligence, but AI does not have
to confine itself to methods that are biologically observable.
http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html

12. AI EVOLUTION TIMELINE…
https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

13. EVOLUTION OF AI, ML, DL
https://www.stateofai2019.com/summary

14. MACHINE LEARNING
• Term coined by Arthur Samuel in 1959
• Machine learning is a branch of artificial intelligence (AI) and computer
science which focuses on the use of data and algorithms to imitate the
way that humans learn, gradually improving its accuracy.
• Through the use of statistical methods, algorithms are trained to make
classifications or predictions, uncovering key insights within data mining
projects.These insights subsequently drive decision making within
applications and businesses, ideally impacting key growth metrics.
https://www.ibm.com/cloud/learn/machine-learning

15. POTENTIAL OF AI
•AI is the new electricity – Andrew Ng
•AI is more profound than fire or electricity –
Sundar Pichai
•People should stop training radiologists – Geoff
Hinton, 2016, https://youtu.be/2HMPRXstSvQ

16. WHERE TO USE AI?
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

17. SO,WHY NOW?
After decades of start/stops, finally AI seems to be at the cusp of its (third) resurgence.

18. AI IS THE NEW SOFTWARE!
• Machine learning was a huge leap from programmed instructions and if-then statements
that merely simulated the very human process of thinking and making decisions.
• With machine learning, the machine no longer needs to be explicitly programmed
to complete a task; it can pour through massive data sets and create its own
understanding. It can learn from the data and create its own model, one that represents
the different rules to explain relationships among data and use those rules to draw
conclusions and make decisions and predictions.
• A machine learning algorithm is a mathematical function that enables the machine to
identify relationships among inputs and outputs.The programmer’s role has shifted
from one of writing explicit instructions to creating and choosing the right
algorithms.
Rose, Doug.Artificial Intelligence for Business:WhatYou Need to Know about Machine Learning and Neural Networks.

19. HOW IS AI DIFFERENT FROM SOFTWARE?
Traditional Software AI Software
Reasoning Deductive Inductive
Inputs Data + Program Data + Output
Logic Manually pre-programmed to perform a specific task on
a given dataset
Programmed to automatically keep learning rules from a
given dataset
Output Output Models, Rules
Learning Learns one-time from the programmer “Learns” constantly being the data
Resource Code Data
Solutions Deterministic Probabilistic
Output Consistently remains the same Can improve with usage (or degrade over time)
Business model One-time development efforts, followed by multiple
sales, and small maintenance effort (optional)
Each project is one-off, and needs full lifecycle
management mandatorily

20. FAILURE MODES OF AI
• Brittleness
• Embedded Bias
• Catastrophic Forgetting
• Explainability
• Quantifying Uncertainty
• Common Sense
• Math
Source: https://spectrum.ieee.org/ai-failures

21. CHALLENGES
• Unavailability of Skills &Talent
• Technology is quite good, but still maturing
• Data is often siloed or not available, or poor quality, etc.
• Business case, and alignment of strategy and business model to an AI-firm
• Societal concerns like ethics, privacy, transparency, bias, surveillance, etc.
• Governance issues such as data management, regulations, legal frameworks, etc.
• …..and many many more…!!!

22. BUSINESS
BENEFITS OF
AI
https://hbr.org/2018/01/artificial-intelligence-for-the-real-world

23. AI-POWERED
ORGANIZATION
https://hbr.org/2019/07/building-the-ai-powered-organization

24. RECAP
• Today’s AI is not really “AI”! At most, it is what we call as “Machine Learning” (ML). But, let’s
stick with AI for now J
• In the broadest sense, it is a form of a “General Purpose Technology” (GPT). Just like fire,
electricity or computers.
• AI has huge potential to solve problems that could benefit from high-speed and at-scale data-
based decision-making for routine and repetitive tasks , especially for “noisy data” conditions.
• Perhaps the first trillion-dollar economy that won’t require natural resources, financial capital,
or human labor.
• Challenges with adoption notwithstanding, there are huge opportunities for businesses to
leverage AI to boost productivity and deliver financial returns.

25. FINALLY…