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GenAI in
Business
DR. TATHAGAT VARMA
EXECUTIVE FELLOW, INDIAN SCHOOL OF BUSINESS (ISB)
“COGNITIVE CHASMS: A GROUNDED THEORY OF GENAI
ADOPTION”
Disclaimer:
Personal
views!
GenAI: The New
Shiny Penny?
 Several Trillion $$$s of
economic impact
projected.
 30% growth expected in
2024: Over $20bn VC
funding in the first three
quarters.
 Capex of $1tn+
expected in the coming
years!
Evolution of AI: Is “third time” a
charm?
https://www.americanscientist.org/article/intelligence-may-not-be-computable
The field of AI:
continually
evolving and
rapidly growing.
Also
manufacturing
serious hype
since 1956!
1956
“Every aspect of
learning or any other
feature of intelligence
can in principle be so
precisely described
that a machine can be
made to simulate it.”
– John McCarthy
1967
“Within a
generation...the
problem of creating
'artificial intelligence'
will be substantially
solved.”
– Marvin Minsky
2016
“People should stop
training radiologists...it
is just completely
obvious that within 5
years deep learning will
do better than
radiologists.”
– Geoffrey Hinton
2019
“Next year, for sure,
we'll have over one
million robotaxis on the
road”
– Elon Musk
The healthy tradition
continues…and so do the results!
Is it working?
And how’s the ROI?
But the show goes on…
GenAI Hype
Source: Gartner, https://www.gartner.com/en/articles/hype-cycle-for-genai
What is Generative AI?
Ability to create
“new” content using
simple prompts in
English
Content could
include text, images,
audio and video,
etc.
Utilizes advanced
neural network
architectures
Leverages vast
amount of existing
data for pre-training
Types of Generative AI
Generative
Adversarial
Networks (GANs):
Two neural
networks, the
generator and the
discriminator,
compete against
each other to
create new
outputs.
Variational
Autoencoders
(VAEs): Good for
exploring variations
in data in a specific
direction.
Diffusion models:
Create detailed
and complex
images by
gradually refining
random noise into
structured images.
Transformer
models: Use a self-
attention
mechanism and a
feed-forward
network to identify
and memorize
patterns in data.
Deep Generative
Models (DGMs): A
combination of a
generative model
and a neural
network.
Autoregressive
models: Suitable for
tasks such as
language
generation and
image synthesis.
Large Language
Models (LLMs):
Statistical models of
language that
estimate the
probability of a
sequence of words
appearing within a
particular context.
Popular Tools…growing daily!
Text
• Conversation:
ChatGPT,
Claude
• Content:
Jasper
• Writing:
Grammarly,
Anyword
• Email:
Shortwave
• Notes: Mem
Image
• Generation:
Dall-E,
Midjourney
• Enhancement:
Adobe
Photoshop
Audio
• Voice
Generation:
Wellsaid,
Respeecher,
Altered, Murf
• Voice
Cloning:
ElevenLabs
• Music: Suno
• Text-to-
Speech:
Speechify,
Natural
Reader
Video
• Generation:
Runway
• Editing:
Descript
• Social Media:
Flicki
• Digital
Avatars:
Synthesia
• Content
Marketing:
Peech
• Script to
video: Visla
Technology
• Coding and
debugging:
Copilot
• App building:
Microsoft
Power Apps,
Wix, Framer,
Pico
Key Benefits
Productivity
Automate tasks that
are manual, boring,
repetitive, dangerous
Allow humans to
focus on higher
value-adding tasks
Efficiency
Reduce lead time,
process overheads,
wastage
Optimize operational
costs and improve
bottomline
Experiences
Deliver personalized
experiences
Enhance creativity
Innovation
Design new products,
services and
experiences
Improve topline and
innovate the business
model
Decision-making
Enhance decision-
making based on
data-driven insights
Ability to incorporate
pervasive emergent
learning and achieve
market-scale in near-
real-time
Industries…virtually every industry!
Healthcare:
Diagnostics,
Patient Care,
Retail: Inventory
management,
Pricing, Virtual try-
ons, etc.
Entertainment:
Scriptwriting,
Animation,
Office: Productivity
solutions
Functions…literally every function!
Customer Service
Chatbots
Marketing
Content creation: Ads,
Blogs, Creatives
Personalized
campaigns, Data
Analysis, Culling insights,
etc.
Product Development
Prototyping, Design
Technology: automated
coding and debugging
People
AI-driven individual
learning journeys
Hiring, career
progression, etc.
Major Limitations
Bias Accuracy Ethical Cost Data
Addressing the
Limitations…a
first-principles
approach
Human
Learning
Data
Governance
Theory of Cognitive Chasms©
GenAI Value Spectrum
Optimize (“10%”)
•Task-level
adoption and
process-level
“vertical
scaling” lead to
individual
productivity
hacks that might
make no real
impact for the
firm, while
process
efficiency could
improve the
bottomline.
Innovate (“1X+”)
•Firm-level
“horizontal
scaling” across
the entire value
chain leads to
business value
realization and
superior
customer
experiences that
improve the
topline better
than the normal
growth.
Transform (“10X”)
•Harness the
power of data-
informed
algorithmically-
supported
“cognitive
decision-
making” to
transform the
business model
and deliver
exceptional
human
experiences that
are order of
magnitude
superior.
Evolve (“∞”)
•Industrial and
societal
adoption lead
to market
disruptions and
planet-scale
solutions for
social good
when applied
judiciously with
appropriate
human controls,
but might wreak
havoc when
applied
indiscriminately
without
adequate
forethought and
safeguards.
Theory
- of -
Cognitive
Chasms©
Coming
in 2025…
Follow updates at: https://www.linkedin.com/company/cognitivechasm
Recap
Enterprise-wide Adoption: GenAI is a “general purpose technology”. To realize its full
value, it must be seen as a “horizontal technology”, i.e., enterprise-wide adoption.
Strategic Value Creation: Successful adoption must strategically integrate GenAI to
a firm’s value chain and deliver tangible value to the firm. True value of GenAI lies in
transforming the internal capabilities and business model of the firm.
Transformation Roadmap: Theory of Cognitive Chasms provides a roadmap for firms
looking at systematic change management while mitigating risks and delivering
tangible value.

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Generative Artificial Intelligence (GenAI) in Business

  • 1. GenAI in Business DR. TATHAGAT VARMA EXECUTIVE FELLOW, INDIAN SCHOOL OF BUSINESS (ISB) “COGNITIVE CHASMS: A GROUNDED THEORY OF GENAI ADOPTION”
  • 3. GenAI: The New Shiny Penny?  Several Trillion $$$s of economic impact projected.  30% growth expected in 2024: Over $20bn VC funding in the first three quarters.  Capex of $1tn+ expected in the coming years!
  • 4. Evolution of AI: Is “third time” a charm? https://www.americanscientist.org/article/intelligence-may-not-be-computable
  • 5. The field of AI: continually evolving and rapidly growing. Also manufacturing serious hype since 1956! 1956 “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” – John McCarthy 1967 “Within a generation...the problem of creating 'artificial intelligence' will be substantially solved.” – Marvin Minsky 2016 “People should stop training radiologists...it is just completely obvious that within 5 years deep learning will do better than radiologists.” – Geoffrey Hinton 2019 “Next year, for sure, we'll have over one million robotaxis on the road” – Elon Musk
  • 9. But the show goes on…
  • 10. GenAI Hype Source: Gartner, https://www.gartner.com/en/articles/hype-cycle-for-genai
  • 11. What is Generative AI? Ability to create “new” content using simple prompts in English Content could include text, images, audio and video, etc. Utilizes advanced neural network architectures Leverages vast amount of existing data for pre-training
  • 12. Types of Generative AI Generative Adversarial Networks (GANs): Two neural networks, the generator and the discriminator, compete against each other to create new outputs. Variational Autoencoders (VAEs): Good for exploring variations in data in a specific direction. Diffusion models: Create detailed and complex images by gradually refining random noise into structured images. Transformer models: Use a self- attention mechanism and a feed-forward network to identify and memorize patterns in data. Deep Generative Models (DGMs): A combination of a generative model and a neural network. Autoregressive models: Suitable for tasks such as language generation and image synthesis. Large Language Models (LLMs): Statistical models of language that estimate the probability of a sequence of words appearing within a particular context.
  • 13. Popular Tools…growing daily! Text • Conversation: ChatGPT, Claude • Content: Jasper • Writing: Grammarly, Anyword • Email: Shortwave • Notes: Mem Image • Generation: Dall-E, Midjourney • Enhancement: Adobe Photoshop Audio • Voice Generation: Wellsaid, Respeecher, Altered, Murf • Voice Cloning: ElevenLabs • Music: Suno • Text-to- Speech: Speechify, Natural Reader Video • Generation: Runway • Editing: Descript • Social Media: Flicki • Digital Avatars: Synthesia • Content Marketing: Peech • Script to video: Visla Technology • Coding and debugging: Copilot • App building: Microsoft Power Apps, Wix, Framer, Pico
  • 14. Key Benefits Productivity Automate tasks that are manual, boring, repetitive, dangerous Allow humans to focus on higher value-adding tasks Efficiency Reduce lead time, process overheads, wastage Optimize operational costs and improve bottomline Experiences Deliver personalized experiences Enhance creativity Innovation Design new products, services and experiences Improve topline and innovate the business model Decision-making Enhance decision- making based on data-driven insights Ability to incorporate pervasive emergent learning and achieve market-scale in near- real-time
  • 15. Industries…virtually every industry! Healthcare: Diagnostics, Patient Care, Retail: Inventory management, Pricing, Virtual try- ons, etc. Entertainment: Scriptwriting, Animation, Office: Productivity solutions
  • 16. Functions…literally every function! Customer Service Chatbots Marketing Content creation: Ads, Blogs, Creatives Personalized campaigns, Data Analysis, Culling insights, etc. Product Development Prototyping, Design Technology: automated coding and debugging People AI-driven individual learning journeys Hiring, career progression, etc.
  • 17. Major Limitations Bias Accuracy Ethical Cost Data
  • 20. GenAI Value Spectrum Optimize (“10%”) •Task-level adoption and process-level “vertical scaling” lead to individual productivity hacks that might make no real impact for the firm, while process efficiency could improve the bottomline. Innovate (“1X+”) •Firm-level “horizontal scaling” across the entire value chain leads to business value realization and superior customer experiences that improve the topline better than the normal growth. Transform (“10X”) •Harness the power of data- informed algorithmically- supported “cognitive decision- making” to transform the business model and deliver exceptional human experiences that are order of magnitude superior. Evolve (“∞”) •Industrial and societal adoption lead to market disruptions and planet-scale solutions for social good when applied judiciously with appropriate human controls, but might wreak havoc when applied indiscriminately without adequate forethought and safeguards.
  • 21. Theory - of - Cognitive Chasms© Coming in 2025… Follow updates at: https://www.linkedin.com/company/cognitivechasm
  • 22. Recap Enterprise-wide Adoption: GenAI is a “general purpose technology”. To realize its full value, it must be seen as a “horizontal technology”, i.e., enterprise-wide adoption. Strategic Value Creation: Successful adoption must strategically integrate GenAI to a firm’s value chain and deliver tangible value to the firm. True value of GenAI lies in transforming the internal capabilities and business model of the firm. Transformation Roadmap: Theory of Cognitive Chasms provides a roadmap for firms looking at systematic change management while mitigating risks and delivering tangible value.