The Future of Artificial Intelligence (AI) Organisations

In this talk delivered internally, I teased out some of the key changes from my research that will mark the future of organisations in an AI world.

1. The Future of
AI-DrivenAI-LedAI-First
AIOrganizations
Dr. Tathagat Varma
“Theory of Cognitive Chasms: A Grounded Theory of GenAI Adoption”©, 2025
Executive Fellow in Management (EFPM) Co’25, Indian School of Business (ISB), India

2. Imagine a firmin 202x…
HR uses ChatGPT to
prepare Job Postings
Developers use
GitHub CoPilot
for Software
Development
Managers use
Gemini for
writing
performance
appraisals
Finance uses CoPilot for
complex Excel queries
Marketing
uses Tableau
for Vibe
Analytics
Junior Developers
use Lovable for
Vibe Coding
Leaders use Adobe for
summarizing documents
Media uses Nano
Banana to create
graphics
Store Managers use AI
Agents to keep track of
perishable inventory
Procurement writes AI
Agents to optimize
procurement process
Data Scientists use
NotebookLM to research the
latest developments in GenAI
Finance uses Payroll
Software for paychecks
Product
Manager use
Customer
Support uses
chatbot for 24/7
service
Comms uses
Canva
creating
social media
and visual
content
UX Designers use
Figma instant
prototyping
Field Sales uses Salesforce for
automated prospecting and lead scoring
Your favorite
GenAI Use Case!
Your
Manager’s
favorite GenAI
Use Case!!
Your Board’s
favorite GenAI Use
Case!!!

3. What are the most likely outcomes?
Time:
Task Time? Cycle Time?
Lead Time? Takt Time?
Efficiency:
Process Efficiency? Flow
Efficiency?
Throughput:
#Builds? #Deploys?
Rollbacks?
Costs:
Bottomline? Inventory?
Fixed Costs? Sunk Costs?
Non-linearity?
Sales:
CAC? CLV? Sales Growth
Rate? Sales Cycle Length?
Average Deal Size? Topline
Revenue?
Finance:
Gross Profit Margin? Net
Profit Margin? EPS? ROE?
Cash Flow? A/R Turnover?
Inventory Turnover?
Employee:
eNPS? Turnover?
Retention? Productivity?
Customer:
DAU / MAU? CSAT? NPS?
CES? Churn Rate? Repeat
Customer? User
Engagement? Referrals?
Quality:
Defect Rate? Scrap Rate?
First Pass Yield? MTTR?
CFR?
Creativity:
#Ideas? Fluency?
Flexibility? Originality?
Elaboration? CAT? CPSS?
Innovation:
Idea Flow?
Experimentation?
Innovation Rate?
Implementation Efficiency?
Net Products / Services?
Business Model:
New Revenue Streams?
Sustainable Competitive
Advantage? Moat?
Paradigm Shift? The New
Normal?

4. Is that ok?

5. When was the last you heard of…
•“Electricity-driven Organization”?
•“Computer-led Enterprise”?
•“Internet-powered Firm”?
•“5G-driven Corporation”?
•“Wifi-first Company”?
•…
•So why “AI Organizations”?

6. What’s a “General Purpose
Technology” (GPT)?
•Has a wide-ranging impact on society and the economy, transforming multiple
industries, sectors, and aspects of daily life. Not limited to one field, they enable
many new products, services, business models, and create spillover effects across
entire economy.
•Key characteristics:
•Pervasiveness:Used across a wide range of industries and sectors
•Continuous improvement: They get better and more efficient over time
•Innovation spawning:They enable or require the development of many
complementary innovations or applications
•Transformative impact:They drive major shifts in productivity, how businesses
compete, and even social structures.
•Examples: Fire, Wheel, Steam Engine, Electricity, Computer, Internet, AI…

7. What does it mean to say AI as a
“GPT”?
•AI is not just a niche innovation or a specialized tool, but a foundational technology with
broad, transformative impact across the entire economy and society.
•Here’s what that implies:
•Pervasiveness:AI can be (and is being) used in almost every sector—healthcare, finance, manufacturing,
education, logistics, retail, creative industries, science, and more. Like electricity or computing, it is not limited to
one field, but underpins countless applications.
•Continuous Growth and Improvement: As a GPT, AI is expected to keep improving over time, spawning new
capabilities, tools, and business models as it evolves. Its progress leads to increased productivity and opportunities
across many domains.
•Platform for Innovation:AI enables the creation of entirely new products, services, and processes that
previously weren’t possible. It also accelerates the development of complementary innovations—much like the
steam engine enabled trains and factories, or the internet enabled e-commerce and social media.
•Economy-wide Transformation:Like other general purpose technologies in history, AI drives major
economic and social change by reshaping workflows, automating tasks, augmenting human skills, and even
creating new industries. Its diffusion can raise national productivity, alter job requirements, and transform how
people and organizations interact with technology.

8. So, what’s the “end game” then?
Maybe, AI as a “boring technology”?
Thenotion of a technology being “boring”does not usually mean it is dull, outdated, or
unimportant—instead, it refers to technologies that arewell-established, proven, reliable, and
widely understood. In the tech industry, “boring” technology is often seen as avirtuefor many
practical reasons:
•Reliability and Stability:Boring technologies have been used by many organizations over
time, so their failure modes are well known, help is abundant, and most bugs have already
been found and fixed. This makes them dependable choices for large-scale and mission-critical
systems.
•Maturity and Support:They often have strong documentation, a big community of users,
and lots of libraries and tools available, making it easier to hire, learn, and maintain.
•Predictability:Because they are tried and tested in all kinds of scenarios, boring
technologies behave in expected ways and integrate well into complex systems.
•Lower Cost and Risk:Lack of hype and “latest-and-greatest” features means boring tech is
usually cheaper and introduces fewer surprises or integration issues.

9. AI Stats
•95% Fail to Deliver Financial
Returns (MIT ‘25)
•87% Fail to Reach Production
(VentureBeat '19)
•85% Deliver Erroneous
Outcomes (Gartner '18)
•78% Fail to Scale (McKinsey ‘20)
•75% Don’t Achieve Significant
EBIT Impact (McKinsey ‘22)
•70% Report No/Minimal Impact
(MIT/BCG '19)

10. The GenAI Divide: State of AI in
Business 2025, MIT NANDA
•Despite $30–40 billion in enterprise investment into GenAI, 95% of organizations are
getting zero return.
•Just 5% of integrated AI pilots are extracting millions in value, while the vast majority
remain stuck with no measurable P&L impact. This divide does not seem to be driven
by model quality or regulation but seems to be determined by approach.
•Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations
have explored or piloted them, and nearly 40 percent report deployment. But these tools
primarily enhance individual productivity, not P&L performance.
•Meanwhile, enterprise-grade systems, custom or vendor-sold, are being quietly rejected.
Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot
stage and just 5 percent reached production. Most fail due to brittle workflows,
lack of contextual learning, and misalignment with day-to-day operations.

11. The Big Questions
Why: GenAI
“fails” often?
What: is
“successful”
adoption?
How: to
deliver
successful
outcomes?

12. Theory of Cognitive Chasms© (§8)

13. Cognitive Decision-Making (§6.6)
It is the organizational capability to make
data-informed and algorithmically curated
decisions based on emergent and pervasive
learning that leads to the cognitive
empowerment of humans and agents in roles
involving cognitive collaboration.

14. The 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.

15. Multistage Scaling Strategy (§7.1)
Stage 1:
Explore
Stage 2:
Experiment
Stage 3:
Improve
Stage 4:
Transform
Stage 5:
Innovate

16. Multistage Scaling and
Consequents:
Stage 1:
Explore
Stage 2:
Experiment
Stage 3:
Improve
Stage 4:
Transform
Stage 5:
Innovate

17. Cognitive Chasms (§7.2)
GenAI Adoption Failure
Modes
Represent the potential
discontinuities in adoption
4 distinct chasms represent
exponential risks and rewards

18. Hype / Reality
(H/R) Chasm
•"Expectation
s Gap"
•Right
technology
must work
Pilot /
Production
(P/P) Chasm
•“Scaling Gap”
•Pilots must be
scalable
Technology /
Business (T/B)
Chasm
•“Value Gap”
•Business need
must exist
Business /
Social (B/S)
Chasm
•“Safety Gap”
•Societal
interests
sacrosanct
Cognitive Chasms (§7.2)

19. Multistage Scaling and Cognitive
Chasms
Hype / Reality
(H/R) Chasm
Pilot / Production
(P/P) Chasm
Technology / Business
(T/B) Chasm
Business / Societal
(B/S) Chasm

20. The Learning Mindset : Key
Principles (§7.3.1)
Technology is
rapidly evolving
Experimental and
experiential learning
provides the biggest
opportunities
Learning needs to be
both emergent and
pervasive

21. The Purpose Mindset: Key
Principles (§7.3.2)
Delivering superior
business outcomes is the
starting point and the
end goal with no finish
line
Efficiency improvements,
even though important,
are just the low-hanging
fruits
The ability to innovate
continuously constitutes
the necessary end
condition of successful
adoption

22. The Decision Mindset : Key
Principles (§7.3.3)
Data forms the core
of decision-making
Embedding of
GenAI inside the
core value creation
for the firm
Scaling of GenAI
capabilities
pervasively across
the firm

23. The Human Mindset : Key Principles
(§7.3.4)
GenAI systems are
designed to
maximizing the
Human Experiences
(HX)
Firms prioritize
societal safety over
the profit motive
Humans always
retain executive
control of GenAI
implementation

24. Human Mindset
Decision Mindset
Purpose Mindset
Cognitive Mindsets are sequential,
accretive, and cumulative
Learning Mindset

25. Bridging The Chasm: Multistage Scaling,
Cognitive Chasms and Cognitive Mindsets
Human Mindset
Decision Mindset
Purpose Mindset
Learning Mindset

26. Theory of Cognitive Chasms (§9.4)
Business
Alignment is key
to realize full
potential of GenAI
Multistage
Scaling Strategy
proposes change as
a process
Cognitive
Chasms describe
failure modes
Cognitive
Mindset can help
reduce adoption
failures
Cognitive
Decision-
Making marks
successful
adoption
Human Agency
is core to
successful GenAI
adoption

27. 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.

28. Theory
-of
-
Cognitive
Chasms©
Coming
in
2025/26
Follow updates at: https://www.linkedin.com/company/cognitivechasm

29. References
•Theory of Cognitive Chasms: A Grounded Theory of GenAI Adoption: My doctoral dissertation at Indian School of Business
(ISB): https://eprints.exchange.isb.edu/id/eprint/2340/
•PhD Colloquium at Indian Institute of Management, Bangalore (IIMB), Dec 2024:
https://www.youtube.com/live/UBmkl7Vpr0Q?si=lqw2JJUVhUI8cWL7
•GenAI: The Missing ROI: My keynote at SPM Summit at UC Berkeley, 2025: https://www.slideshare.net/slideshow/generative-ai-
the-missing-return-on-investment-roi/283078743
•Cognitive Chasms: A Typology of GenAI Failure Modes: My talk at Walmart: https://www.slideshare.net/slideshow/cognitive-
chasms-a-typology-of-genai-failure-failure-modes/279832748
•Theory of Cognitive Chasms: Lecture at Sam Walton College of Business, University of Arkansas:
https://www.slideshare.net/slideshow/theory-of-cognitive-chasms-failure-modes-of-genai-adoption/278463456
•GenAI Value Spectrum – delivering the “true value” from GenAI initiatives: Keynote at IEEE Bangalore Technology
Conference (BTC) 2024: https://www.slideshare.net/slideshow/genai-value-spectrum-delivering-the-true-value-from-genai-
initiatives/273734671
•The role of Cognitive Mindset in GenAI Adoption: Invited talk at Project Management Institute (PMI) Bangalore Chapter
Footprints:https://www.slideshare.net/slideshow/the-role-of-cognitive-mindset-in-genai-adoption-pdf/274055306
•Managing Fast-Evolving GenAI Adoptions: Invited Keynote at Siemens Agile Conference
2024:https://www.slideshare.net/slideshow/managing-fast-evolving-genai-technology-adoptions/273470070