By Natalia Martinez-Kalinina
There is a strange tension in corporate America’s relationship with artificial intelligence right now. Companies are under enormous pressure to demonstrate that they understand how consequential the technology is, while there is considerably less agreement about what, exactly, they should be doing with it. The result has been a familiar feature of technology cycles: the appearance of innovation sometimes moves faster than the underlying innovation itself.
In a recent New York Times opinion piece, Julie Averill, the former CIO of Lululemon, takes aim at this phenomenon. Her critique is not that AI is useless or that companies should ignore it; she believes the opposite, as do we. Her concern is that AI has become so important to investors, boards and the broader market that companies now have an incentive to describe fairly ordinary corporate decisions through an AI lens. Existing automation gets relabeled as AI. Cost reductions become AI efficiencies. Perhaps the most revealing example is layoffs: companies have announced reductions in headcount based on productivity gains they expect from AI systems that have not yet been fully developed or deployed.
This has come to be described as “AI-washing,” and it is not a new corporate behavior. Companies have always been capable of learning the vocabulary of a technological shift faster than they learn how to extract value from it. AI simply makes the incentives particularly strong. Telling investors that a company is becoming leaner because management overhired or wants to improve margins is a very different story from saying that artificial intelligence has enabled a new operating model; the latter places the company on the right side of a technological transition, even when the actual transformation remains mostly prospective.
A Wall Street Journal article recently offered what is really an argument for much of the same discipline: an announcement that Chili’s (yes, the restaurant chain) has deliberately declined to go “all in” on AI. Its technology team brainstormed dozens of possible applications and found only a handful worth seriously investigating. Meanwhile, CIO Chris Caldwell has spent much of the past two years investing in decidedly unglamorous technology: replacing outdated devices, rebuilding Wi-Fi across roughly 1,200 restaurants, improving payment and kitchen systems, and fixing technology that employees and customers actually encounter every day. Chili’s has experimented with AI where the use case makes sense, but Caldwell’s standard is straightforward: Does the technology materially improve the business or the customer experience? If it does not, the fact that it contains AI is beside the point.
These articles are describing two sides of the same problem. Averill is concerned with companies using AI as a narrative before it has become an operating reality. Caldwell offers an example of what happens when a company refuses to do that. Both point toward a principle that has become surprisingly easy to lose in the current environment: Technology strategy should begin with the needs and constraints of the business, not with the technology that happens to be receiving the most attention.
We think that principle matters because AI has intensified a problem that existed in corporate innovation long before ChatGPT arrived. Companies have often begun with the mechanism rather than the problem. An organization decides it needs an innovation lab, an accelerator, a venture program or a digital transformation initiative, and then has to determine what meaningful business problems that machinery is supposed to solve. Today, the predetermined answer is increasingly AI.
Once leadership decides that an organization needs an “AI strategy,” teams understandably go looking for AI use cases. Boards request AI roadmaps, executives announce AI initiatives, and business units inventory tasks that might be automated. There is nothing inherently wrong with those activities, but they can produce a subtle inversion in the innovation process: The technology becomes the starting point rather than one possible answer to a business problem. A company can end up with perfectly functional AI pilots that have surprisingly little bearing on its most important constraints or opportunities.
Our work at Misfit Labs has spanned both sides of this problem. We work with established companies looking for new sources of growth and innovation, and we build companies from the ground up. In both contexts, we have found it more useful to begin by understanding the problem, why it remains unsolved, and what is preventing the existing organization or market from solving it well; only then does it make sense to determine what combination of technology, people, capital and organizational design might provide a better answer.
That does not mean companies should be conservative about AI. Starting with the problem should create room to think much more expansively about what new technology makes possible. The opportunity may be to make an existing process faster or cheaper, but that is only one possibility; a change in technological capability might also make a previously uneconomic customer segment viable, allow expertise inside an organization to be used differently, or create an entirely new source of revenue.
We explored this in an earlier piece, The $1.4 Billion Lesson, through the example of IKEA. What interested us was not simply that IKEA had automated some customer-service work, but what the company chose to do with the capacity that automation created: moving employees into a different customer-facing business and generating new revenue from it. The technology changed the economics of the existing work; what to do with the capacity it created was still a management decision.
This is why we think the current skepticism around corporate AI is healthy. Companies should have to demonstrate that AI is actually improving the business, and technology leaders should be comfortable reaching the conclusion that, for the problem in front of them, better Wi-Fi is a more important investment than another AI application.
None of this is an argument for thinking less ambitiously about AI. Once AI stops being the required answer, companies can begin asking more interesting questions about what the technology actually changes. The answer may be an efficiency, but it may also be a new capability, a new market, a new business model or a company that could not previously have existed.
The alternative to AI-washing is not AI skepticism; it is doing the harder work of figuring out what problem is actually worth solving, and then being ambitious about the answer.
You can go deeper into why AI is not a strategy here.
Natalia Martinez-Kalínina, a Harvard- and Columbia-trained organizational psychologist and strategist, is the co-founder of Base, a venture-backed membership community, and a Venture Partner at Misfit Labs, an AI-native venture studio based in Miami. She is also the Founder and Principal of NMK Group, a consulting practice focused on human capital strategy, leadership development, organizational design, community architecture, and cross-sector innovation.
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