"Got AI?"

By now, every salesperson for every technology firm is probably being asked “do you have AI” by prospects. And most answers, in most cases, will require the salesperson to ask a further question: “what is your use case?” 

Because right now, most technology buyers are likely reacting to the hype and “FOMO” (Fear of Missing Out). The pressure for businesses to "do something with AI" is going to lead to many deployments that fail to deliver the expected results, as often is the case for new technology. 

Of course, the other typical pressures also exist. Vendors and consultants suggesting AI as a “must-have” will drive such buyer requests. 

But firms might also be engaging in “innovation signaling;” trying to appear innovative to investors, customers, or partners by touting AI initiatives, regardless of substantive application.

Study/Source	Technology	Key Findings on Adoption Behavior	Use Case Clarity
McKinsey Global Survey on AI (2025)	AI, GenAI	Rapid AI adoption; many orgs use AI in multiple functions; limited enterprise-wide impact	Often unclear; many deployments exploratory
IBM AI Business Use Cases (2024)	AI	Lists productive AI use cases; highlights need for business alignment	Stresses importance of defined use cases
NBER Working Paper: Adoption of New Technology	ATMs, Telecom, etc.	Adoption driven by scale, network effects, and competitive pressure	Use cases often emerge post-adoption
Technology Adoption Lifecycle Model	General	Early adoption often driven by hype, FOMO, and signaling	Use case clarity increases over time
Forbes: AI as Fastest Adopted Tech (2023)	AI	AI adoption outpaces previous technologies; driven by hype	Use case definition lags adoption
Harvard Baker Library: Tech Adoption & Economies	Multiple	Adoption lags, driven by economic and social factors	Use cases sometimes secondary

Essentially, the “wasted effort and capital investment” happens because it typically takes some time and experience, plus business process change, before any important new technology can produce measurable outcomes. 

Study Name / Article	Date	Publisher	Key Conclusions
The Gartner Hype Cycle Approach: Understanding the Technology Adoption	2023-12-16	LinkedIn / Gartner	Outlines five phases: innovation trigger, peak of inflated expectations (hype/FOMO), trough of disillusionment, slope of enlightenment, plateau of productivity where useful applications emerge3.
The Role of FOMO in Digital Transformation	2021-01-27	MIT Press: Harvard Data Science Rev.	FOMO drives rapid, sometimes poorly planned digital adoption; over 70% of digital projects fail to deliver intended impact; useful applications emerge only with strategic alignment and learning6.
Technology Adoption Lifecycle - from hype to reality	2022-02-08	THE WAVES	Adoption often starts with hype or fear of missing out, followed by a crash in expectations, and eventually stabilizes as practical, value-driven uses are found7.
Technology Adoption Life Cycle-redefined	2023-12-06	THE WAVES	Adoption unfolds in stages among different user groups; initial hype/FOMO is replaced by economic justification and practical applications as technology matures2.
The Role of Fear of Missing Out and Experience in the Formation of ...	2022	ScienceDirect	FOMO is a significant driver of early technology adoption, but prior experience helps organizations move from hype to rational, use-case-driven adoption1.
Technology Adoption: Escaping the Hype to Maximize Decision ...	2023-01-12	HG Insights	The Hype Cycle model helps organizations distinguish between hype and real business value, guiding them toward effective, use-case-driven adoption8.

AI is not going to be much different, in that regard. 

AI: Correlation is Not Causation

Is productivity higher for people and firms that use artificial intelligence software? And, if so, did the AI "cause" the changes?

Anthropic's Economic Index takes a look at where Claude is being used, and for what purposes, by consumers and businesses across the world.  The implication is that AI use has some positive impact. But we might not be able to make that claim, yet.

Nor will we conclusively be able to claim that the AI produced the observed outcomes.

For the moment, we might only be able to observe increased usage, and be watching for outcomes to change.

Education and science usage shares are on the rise, while the use of Claude for coding continues to dominate the sample at 36 percent of total instances. But Claude use for  educational tasks increased from 9.3 percent to 12.4 percent, while use for scientific tasks from 6.3 percent to 7.2 percent.

Anthropic also notes a shift towards autonomy. “Directive” conversations, where users delegate complete tasks to Claude, grew from 27 percent to 39 percent. The study also notes increased use in coding (+4.5 percentage points) and a reduction in debugging (-2.9 percentage points). 

But we might also note the difference between correlation and causation, as there will be a tendency for value chain suppliers to argue that AI usage “produces” or “causes” observed performance gains (revenue, income, profit margin, productivity). 

In fact, quite the opposite could be happening. High AI usage occurs in industries, countries or by individuals who are already wealthy, well educated and working in settings where cognitive or intangible products are an important part of the output. 

In other words, high AI adoption follows firm and industry success, rather than “causing” it. It’s similar to the “correlation versus causation” argument we might have about home broadband “causing” economic development. 

Some might note that high-quality home broadband tends to be deployed in areas of higher density, higher wealth, higher income and higher education. Quality home broadband (“fastest speeds”) does not cause the wealth, income or educational attainment.

Rather, such characteristics create the demand for such services. 

source: Anthropic 

Many studies have noted the tension between correlation and causation when evaluating the impact of new technologies. 

• Acemoglu et al. (2023) “Advanced Technology Adoption: Selection or Causal Effects?” Firms adopting advanced technologies had higher productivity before adoption, suggesting selection effects rather than pure technological causationLongitudinal firm-level analysis using Census dataPre-existing firm characteristics → Technology adoption

• Autor, Levy & Murnane (2003) “The Skill Content of Recent Technological Change” Computer adoption correlated with pre-existing skill demands rather than creating new skill requirements. 

• Caselli & Coleman (2001) “Cross-Country Technology Diffusion: The Case of Computers” Countries with higher skilled labor adopted computers faster; computer adoption didn't independently increase skill premiums. 

• Krueger (1993) “How Computers Have Changed the Wage Structure” Workers using computers earn higher wages, but much of the premium reflects selection of skilled workers into computer-using jobs. 

• DiNardo & Pischke (1997) “The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?” Computer wage premium largely reflects unobserved worker heterogeneity, as similar premium exists for pencil use.

• Beaudry, Doms & Lewis (2010) “Should the Personal Computer Be Considered a Technological Revolution?” Computer adoption followed rather than preceded productivity gains in most industries.

• Forman, Goldfarb & Greenstein (2012) “The Internet and Local Wages” Internet adoption increased wages more in cities with complementary skilled workforce and business services

• Akerman, Gaarder & Mogstad (2015) “The Skill Complementarity of Broadband Internet” Broadband access increased demand for skilled workers but only in firms/regions with existing high skill levels

• Bloom, Sadun & Van Reenen (2012) “Americans Do IT Better: US Multinationals and the Productivity Miracle” Management practices explain technology adoption and productivity gains; technology alone insufficient

• Cariolle (2021) “International Connectivity and the Digital Divide” Submarine cable connections improve economic outcomes primarily in countries with existing institutional capacity

• Hjort & Poulsen (2019) “The Arrival of Fast Internet and Employment in Africa” Fast internet increased employment in skilled jobs but decreased it in unskilled jobs

• Jensen (2007) “The Digital Provide: Information Technology, Market Performance, and Welfare” Mobile phone adoption by fishermen improved market efficiency, but required existing market infrastructure

• Aker (2010) “Information from Markets Near and Far” Mobile phone coverage reduced price dispersion only in markets with existing trading relationships

• Duflo & Saez (2003) “The Role of Information and Social Interactions in Retirement Plan Decisions” Retirement plan participation increased after information sessions, but mainly among already financially sophisticated employees

• Kling & Liebman (2004) “Experimental Analysis of Neighborhood Effects on Youth” Moving to better neighborhoods improved outcomes, but families that moved had different characteristics than non-movers

• Malamud & Pop-Eleches (2011) “Home Computer Use and the Development of Human Capital” Home computers had mixed effects on student achievement; benefits concentrated among students with higher initial ability

• Vigdor, Ladd & Martinez (2014) “Scaling the Digital Divide: Home Computer Technology and Student Achievement” Computer and internet access at home had negative effects on student achievement for disadvantaged students

Study (Year)	Subject	Key Findings	Direction of Causality
Bils and Klenow (2000)	The Causal Impact of Education on Economic Growth	Correlation between education and growth may be due to reverse causality; richer, faster-growing states find it easier to increase education spending.	Primarily from economic growth to education, with a feedback loop.
Comin et al. (2012)	How Technology Adoption Affects Global Economies	The rate at which nations adopted new technologies centuries ago strongly affects whether they are rich or poor today. Technology adoption lags account for a significant portion of income differences.	Technology adoption has a long-term causal effect on economic prosperity.
Nazarov (2019)	Causal relationship between internet use and economic development in Central Asia	A unidirectional causality exists from GDP per capita to Internet use, suggesting that economic growth stimulates technology adoption.	From GDP per capita to technology use.

Experts Say Metaverse Will Not be Common in Consumer Life in 2040. Why?

Experts surveyed by Pew Research believe that augmented and mixed-reality applications will dominate full virtual reality environments in 2040. But half of the experts also believe the “metaverse” will not be common in the lives of most consumers by that point. 

source: Pew Research 

This will be unwelcome news for many metaverse proponents. But it is historically realistic. 

Major technology transitions typically take much longer than proponents expect. One common facet of new technology adoption is that change often comes with a specific pattern: a sigmoid curve such as the Gompertz model or Bass model. 

S curves explain overall market development, customer adoption, product usage by individual customers, sales productivity, developer productivity and sometimes investor interest. It often is used to describe adoption rates of new services and technologies, including the notion of non-linear change rates and inflection points in the adoption of consumer products and technologies.

In mathematics, the S curve is a sigmoid function. It is the basis for the Gompertz function which can be used to predict new technology adoption and is related to the Bass Model.

Such curves suggest a longish period of low adoption, followed by an inflection point leading to rapid adoption.

That leads supporters to overestimate early adoption and vastly underestimate later adoption. Mobile phone adoption, and smart phone adoption, illustrate the process. You might think adoption is a linear process. In fact, it tends to be non-linear.

Also, the more fundamental the change, the longer to reach mass adoption. Highly-useful “point technologies” such as telephones, electricity, mobile phones, smart phones, the internet and so forth can easily take a decade to reach 10-percent adoption. Adoption by 40 percent of people can take another decade to 15 years. And adoption by more than 40 percent of people can take another decade to 15 years. 

source: MIT Technology Review 

That suggests a 30-year adoption cycle for a specific innovation that has high value to be used by 40 percent to 70 percent of people. Something such as metaverse, which is far more complicated, could easily take 30 years to reach 40 percent of people in ordinary use. 

That might mean at least a decade before metaverse apps are in common use by 10 percent of people. Even then, use cases are likely to be dominated by gaming, business communications and video entertainment. 

source: Robert Patterson 

The sigmoid function arguably is among the most-important mathematical expressions one ever encounters in the telecom, application and device businesses. It applies to business strategy overall, new product development, strategy for legacy businesses, customer adoption rates, marketing messages and  capital deployment, for example. 

The sigmoid function applies to startups as well as incumbents; software and hardware; products and services; new and legacy lines of business. 

source: Innospective

The concept has been applied to technology adoption in the notion of crossing the chasm of value any technology represents for different users. Mainstream users have different values than early adopters, so value propositions must be adjusted as any new technology product exhausts the market of early adopters. Early adopters can tolerate bugs, workarounds or incomplete on-boarding and support experiences. They tend to be price insensitive. 

It always takes longer than one expects for a major new innovation to become ubiquitous. Metaverse, being a complicated development, might take longer than any point innovation.