In this talk, I introduce the idea of "Cognitive Chasms" in adoption of AI in enterprises.

1. Cognitive Chasms

2. Disclaimer
• These are my personal views.
• The ideas expressed here are
part of my ongoing research
work, and as such, likely to be
incomplete, incorrect or change.

3. Technology adoption Patterns
The Pace of Technology Adoption is Speeding Up – Rita Gunther McGrath, Nov 25, 2013,
https://hbr.org/2013/11/the-pace-of-technology-adoption-is-speeding-up

4. Technology Adoption Lifecycle
Diffusion of Innovations – Everett Rogers, 1962
Innovators: Venturesome, and likely to be
cosmopolites. Seek new ideas. Desire for the rash, the
daring, the risky.
Early Adopters: Respect. Likely to be localites.
Highest degree of opinion leadership. Provide legitimacy
to a new idea by adopting it.
Early Majority: Deliberate. Follow with deliberate
willingness in adopting innovations but seldom lead.
Late Majority: Skeptical. Approach innovations with
a skeptical and cautious air, and do not adopt until most
others in their system have already done so.
Laggards: Traditional. “The point of reference for
the laggard is the past. Laggards tend to be suspicious of
innovations and of change agents.

5. The revised technology
adoption lifecycle
• “A gap is introduced between any
two psychographic groups.
• This symbolizes the dissociation
between the two groups—that
is, the difficulty any group will
have in accepting a new product if
it is presented in the same way as
it was to the group to its
immediate left.
• Each of these gaps represents
an opportunity for marketing
to lose momentum, to miss the
transition to the next segment,
thereby never to gain the promised
land of profit-margin leadership in
the middle of the bell curve.”
Crossing the Chasm: Marketing and Selling Disruptive Products to
Mainstream Customers – Geoffrey Moore (1995)

6. What’s a Chasm?
Merriam-Webster: a deep cleft in
the surface of a planet; a marked
division, separation, or difference
Lexico: A profound difference
between people, viewpoints, feelings,
etc.

7. The Chasm!
• “The real news is the deep and dividing chasm that separates the early adopters from the
early majority. This is by far the most formidable and unforgiving transition in the
Technology Adoption Life Cycle, and it is all the more dangerous because it typically goes
unrecognized.
• What the early adopter is buying is some kind of change agent. By being the first
to implement this change in their industry, the early adopters expect to get a jump on the
competition, whether from lower product costs, faster time to market, more complete
customer service, or some other comparable business advantage. They expect a radical
discontinuity between the old ways and the new, and they are prepared to champion this
cause against entrenched resistance. Being the first, they also are prepared to bear with
the inevitable bugs and glitches that accompany any innovation just coming to market.
• By contrast, the early majority want to buy a productivity improvement for
existing operations. They are looking to minimize the discontinuity with the old ways.
They want evolution, not revolution. They want technology to enhance, not overthrow,
the established ways of doing business. And above all, they do not want to debug
somebody else’s product. By the time they adopt it, they want it to work properly and to
integrate appropriately with their existing technology base.”

8. The Catch-22
“Because of these incompatibilities, early adopters do not make
good references for the early majority. And because of the early
majority’s concern not to disrupt their organizations, good
references are critical to their buying decisions. So what we
have here is a catch-22. The only suitable reference for an early
majority customer, it turns out, is another member of the early
majority, but no upstanding member of the early majority will
buy without first having consulted with several suitable
references.”

9. The Chasm in High-Tech
Products…
“In sum, when promoters of high-tech products try to
make the transition from a market base made up of
visionary early adopters to penetrate the next adoption
segment, the pragmatist early majority, they are
effectively operating without a reference base and
without a support base within a market that is highly
reference oriented and highly support oriented.”

10. AI Adoption
Timeline

12. Cognitive Chasms?
AI adoption has
picked up, especially
in the last decade.
However, several
phenomena that need
closer examination.
These pertain to a
significant drop in the
number of initiatives
moving from a
previous stage to the
next stage.
I call them as
“Cognitive Chasms”

13. Why “Cognitive Chasms”?
• AI projects face unique obstacles due to their scope and popularity,
misperceptions about their value, the nature of the data they touch.
(Gartner, 2019)
• Too many times, AI fails to deliver the positive impact that businesses
really want from the technology, like more revenue, lower cost, fewer
customers lost to churn, higher manufacturing quality, and lower waste
and fraud. The mathematics behind today’s AI is impressive (just ask
any data scientist). But when it comes to making businesses more
profitable, somehow the numbers don’t add up. (Preston, 2020)

14. Key Cognitive Chasms (WIP)
• Hype/Technology (H/T)
• Technology/Reality (T/R)
• Belief/Intent Chasm (B/I)
• Pilot/Production Chasm (P/P)
• Production/Scaling Chasm (P/S)
• Scaling/Business Chasm (S/B)
• Operations/Mindset (O/S)
• Business/Societal Chasm (B/S)

15. The Hype/Technology Chasm
• Organizations need to set realistic timelines for AI projects and ensure the desire to
push forward with a popular technology doesn’t overrule realistic drawbacks and
planning. The hype itself can be a problem, alongside other logistical and strategic
challenges. (Gartner, 2019)
• When it comes to self-driving cars, the future was supposed to be now. In 2020,
you’ll be a “permanent backseat driver,” the Guardian predicted in 2015. “10
million self-driving cars will be on the road by 2020,” blared a Business Insider
headline from 2016. Those declarations were accompanied by announcements
from General Motors, Google’s Waymo, Toyota, and Honda that they’d be
making self-driving cars by 2020. Elon Musk forecast that Tesla would do it by
2018 — and then, when that failed, by 2020. But the year is here — and the self-
driving cars aren’t. (Vox, 2020)…The CEO of Volkswagen's autonomous driving
division recently admitted that Level 5 autonomy—that's full computer control of
the vehicle with zero limitations—might actually never happen. (Thedrive, 2020)

16. The Technology/Reality Chasm
• Yes, excessive automation at Tesla was a mistake. To be precise, my
mistake. Humans are underrated. (Musk, 2018)
• Through 2022, only 15% of use cases leveraging AI techniques (such
as ML and DNNs) and involving edge and IoT environments will be
successful. (Gartner, 2019)
• Only about 1 in 5 CIOs who thought they would employ AI within the
next 12 months actually achieved that, especially in the 2019-2021
timeframe. (Gartner, 2020)

17. The Belief/Intent Chasm
• In Peltarion’s recent survey of 350 IT leaders – executives responsible
for shepherding AI at companies across industries in the U.K. and the
Nordics — 99% of those surveyed believed that deep
learning, AI’s most powerful and groundbreaking
technique, would transform their industry. And yet, these
leaders aren’t exactly walking the walk just yet. The same survey found
that less than 1% of those CIOs, CTOs and Directors of IT
have deployed deep learning practices extensively across
their business. And while 28% have some kind of deep learning
projects up and running, that is greatly overshadowed by other
machine learning projects that are live at 88% of their enterprises. (AI
Forum, 2020)

18. The Pilot/Production Chasm
• Just 16% of respondents say their companies have taken deep
learning beyond the piloting stage. (McKinsey, 2020)
• 87% of data science projects never make it into production. Reasons
include lack of leadership support, lack of data, lack of collaboration
(VentureBeat, 2019)
• Through 2020, 80% of AI projects will remain alchemy, run by
wizards whose talents will not scale in the organization. (Gartner,
2019)
• Most organizations start an AI project with a plan to launch the
project within two years. However, organizations past the initial
planning process estimate it will take four years. (Gartner, 2019)

19. The production/scaling chasm
• A recent Gartner survey of global CIOs found that only 4% of
respondents had deployed AI. (Gartner, 2019)

20. The Scaling/Business Chasm
• It’s also clear that we’re still in the early days of AI use in business, with less than a quarter of respondents seeing significant bottom-line impact. This isn’t surprising—achieving
impact at scale is still elusive for many companies, not only because of the technical challenges but also because of the organizational changes required. (McKinsey, 2020)
• By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change
management programs. And yet…Through 2022, only 20% of analytic insights will deliver business outcomes. (Gartner, 2019)
• By 2022, 30% of organizations will use explainable AI models to build trust with business stakeholders, up from almost no usage today. (Gartner, 2019)
• By 2023, a Fortune 1000 antitrust case will hinge on whether tacit cooperation among autonomous AI agents in competitive markets constitutes collusion. (Gartner, 2019)
• It is difficult to determine an AI project’s ROI because most organizations are too early in the process to see any return. Most ROI will be seen in cost reduction and efficiency, as
that’s how AI is currently used. However, as enterprises evolve their AI expectations and projects, the technology will mature to have more transformative and strategic impacts.
(Gartner, 2019)
• 37% of organizations are still looking to define their AI strategies, while 35% are struggling to identify suitable use cases. (Gartner, 2018)
• Building a business case includes analyzing the expected benefits and costs associated with a project. However, in the case of AI, the answer is unlikely to be straightforward. AI
projects can appear costly without any immediate gains — particularly for loosely bound scenarios and in organizations that aren’t used to setting aside budget to develop and
deploy solutions for new business scenarios. (Gartner, 2018)
• Leading companies report significant strides in achieving business outcomes Big Data and AI investment is holding strong and the pace of investment is accelerating,
as are companies reporting successful business outcomes. This year, there was nearly universal acknowledgement -- 96.0% -- that Big Data and AI efforts were yielding results, an
increase from half that number – 48.4% -- just a half decade ago. Companies still face significant headwinds to becoming Data-Driven however Making a
commitment to data-driven transformation is one thing; executing on that commitment is quite another. A decade into these efforts, companies still have a long-way to go – only
39.3% are managing data as an asset; only 24.4% have forged a data culture within their firms; only 24.0% have created a data-driven organization. There is still much work to be
done. Leading companies still have some ways to go on their data journey. (NewVantage, 2021)
• 76% of CEOs are most concerned with the potential for bias and lack of transparency when it comes to AI adoption (PwC)
• Through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them (Gartner)

21. The Operations/Mindset Chasm
• The mindset shift required for AI can lead to "cultural anxiety"
because it calls for a deep change in behaviors and ways of thinking.
(Gartner, 2018)
• Myth No. 3: Intelligent machines learn on their own (Gartner, 2019)
• Myth No. 6: My business does not need an AI strategy…In the next
four years, 69% of what a manager currently does will be automated.
In such a disruptive environment, enterprises need a reality check on
how best they can integrate AI into their strategy and be ready for
forthcoming disruptions. (Gartner, 2019)

22. The Business/Societal Chasm
• By 2023, 60% of organizations with more than 20 data scientists will require a professional code of conduct incorporating ethical
use of data and AI. (Gartner, 2019)
• By 2021, legislation will require that 100% of conversational assistant applications, which use speech or text, identify themselves as
being nonhuman entities. (Gartner, 2019)
• By 2022, 30% of consumers in mature markets will rely on artificial intelligence (AI) to decide what they eat, what they wear or
where they live. (Gartner, 2019)
• Myth No. 4: AI can be 100% objective (Gartner, 2019)
• Myth No. 5: AI will only replace mundane jobs (Gartner, 2019)
• 5 Myths you must stop believing: AI is going to replace all jobs, Only low-skilled and manual workers will be replaced by AI and
automation, Super-intelligent computers will become better than humans at doing anything we can do, Artificial intelligence will
quickly overtake and outpace human intelligence, and AI will lead to the destruction of enslavement of the human race by superior
robotic beings (Marr)
• Consumers use more AI than they realize. While only 33% think they use AI-enabled technology, 77% actually use an AI-powered
service or device. (Source: Pega)
• 49% of consumers say they don’t think automation in healthcare is safe. (Blumberg Capital)
• According to AI statistics, 52% of consumers don’t believe AI will protect their private information. (Blumberg
Capital)
• Half of consumers believe people are already losing jobs and are being replaced by computers. (Blumberg Capital)

23. Recap • Like all previous technologies, AI is also a
“good servant but a bad master”.
• AI promises to bring in over $15Trn+
economy by 2030. However, there are
significant adoption challenges.
• While the technology is fast-maturing, there
are other factors that create adoption
challenges, or the “cognitive chasms”.
• These pertain to technology, organization,
skills and jobs, financial, business, and
several other important factors.
• The key is to recognize what causes these
cognitive chasms and evolve strategies to
bridge it.

24. References
• The state of AI in 2020, Nov 20, 2020. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020
• Why do 87% of data science projects never make it into production?, Jul 19, 2019, https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-
production/
• Our Top Data and Analytics predicts for 2019 – Andrew White, Jan 03, 2019, https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-
2019/
• AI Adoption lags ambition (surprised?) – Whit Andrews, Nov 19, 2020, https://blogs.gartner.com/whit_andrews/2020/11/19/ai-adoption-lags-ambition-surprised/
• The CIO’s Guide to Artificial Intelligence – Kasey Panetta, Feb 05, 2019, https://www.gartner.com/smarterwithgartner/the-cios-guide-to-artificial-intelligence
• Are Enterprises ready to go deep with AI? – Jan 20, 2020, https://ai-forum.com/research-items/are-enterprises-ready-to-go-deep-with-ai/
• How to build a Business Case for Artificial Intelligence – Christy Pettey, Apr 04, 2018, https://www.gartner.com/smarterwithgartner/how-to-build-a-business-case-for-artificial-
intelligence
• It’s 2020. Where are our self-driving cars? – Kelsey Piper, Feb 28, 2020, https://www.vox.com/future-perfect/2020/2/14/21063487/self-driving-cars-autonomous-vehicles-
waymo-cruise-uber
• Key Volkswagen Exec admits Full Self-Driving Cars ‘May Never Happen’ – Chris Chin, Jan 13, 2020, https://www.thedrive.com/tech/31816/key-volkswagen-exec-admits-level-
5-autonomous-cars-may-never-happen
• Elon Musk Tweet on Automation, Apr 14, 2018, https://twitter.com/elonmusk/status/984882630947753984?
• Lessons from Artificial Intelligence Pioneers – Christy Pettey, Nov 05, 2019, https://www.gartner.com/smarterwithgartner/lessons-from-artificial-intelligence-pioneers
• 6 AI Myths Debunked, Nov 05, 2019, https://www.gartner.com/smarterwithgartner/5-ai-myths-debunked
• 5 Myths about Artificial Intelligence (AI) you must stop believing – Barnard Marr, https://bernardmarr.com/5-myths-about-artificial-intelligence-ai-you-must-stop-believing/
• Big Data and AI Executive Survey 2021 – NewVantage Partners, https://www.newvantage.com/thoughtleadership
• 131 Myth-busting Statistics on Artificial Intelligence (AI) in 2021 – Cem Dilmegani, Aug 11, 2021, https://research.aimultiple.com/ai-stats/
• This much-hyped technology is failing businesses. Here’s why – R. Preston McAfee and Arijit Sengupta and Jonathan Wray, FastCompany, Jan 08, 2020,
https://www.fastcompany.com/90449015/this-much-hyped-technology-is-failing-businesses-heres-why