What Executives Get Wrong About AI

Ninety-five percent of AI projects fail. MIT’s Project NANDA has made the number famous, and it now opens countless strategy sessions and keynote addresses. Leaders naturally ask how they can join the 5% that succeed. But that question ignores the real issue, which is that if you scope your project using the wrong objectives, you might build the “right” tools and hit the “right” milestones—but still set yourself up to lose. That’s what’s happening with AI today: Companies are rushing to adopt it but aren’t thinking about it as a tool for reinvention. They’re buying the shiny new thing and bolting it on to yesterday’s business model. They’re thinking of it merely as an accessory, not as a powerful new lever for redesigning their whole system of value creation. To behave in that way is a recipe for failure. If you’re thinking about experimenting with AI, it’s time to stop obsessing with adoption and start thinking hard about reinvention. In this article, we’ll discuss historical examples of how these two very different approaches to new technology have played out—and we’ll show how they lay out a way forward for leaders wondering how best to engage with AI. Image Capture Consider the infamous story of Kodak, which is usually told like this: In 1975, the company invented the first digital camera but didn’t recognize the technology’s transformative potential and so only adopted it late, after other, more prescient companies had already taken over the space. But adoption wasn’t actually the issue for Kodak. By 2005, in fact, the company was the leading seller of digital cameras in the United States and a leading provider of digital-printing services. Nevertheless, just seven years later, it went bankrupt. To understand what happened, you have to remember that film photography was built on the scarcity of captured images, which were labor-intensive and expensive to produce. As a result, film and printing carried margins of 60% to 70%. Digital photography eliminated that scarcity. In a world where images could be produced without limits and digital cameras were a low-margin hardware business, value migrated away from the act of taking photos to the systems that organized, shared, and stored them. Kodak didn’t adapt to that new reality. After adopting digital cameras, the company treated them as a simple product substitution for film cameras—and in so doing dismantled the high-profit engine that had sustained it for decades. Even more important, it failed to invest in the value-generating complements that would define the new ecosystem: photo-sharing platforms, editing tools, storage services. Kodak did acquire Ofoto, an early online photo-sharing startup, but it reframed it as a printing service, rather than a social or sharing platform. All of this resulted in a double blow: The substitution mindset eroded profits, and the absence of complements left the company without new profit pools to migrate to. By adopting a new technology with the wrong objectives, the company accelerated its own decline. Kodak’s competitor Fujifilm responded very differently to the same challenge. Recognizing that the days of using film as an engine for profits were over, the company drew on its deep expertise in chemicals, imaging, and materials science and quickly expanded into adjacent domains, using antioxidants it had developed for film preservation to create cosmetics and skincare products, applying its imaging capabilities to medical diagnostics, and leveraging its materials-science expertise to develop pharmaceuticals and LCD screen components. At the same time, the company aggressively reduced its reliance on the legacy film business. By 2012, when Kodak filed for bankruptcy, Fujifilm had successfully reinvented itself: It was profitable and growing, with revenues exceeding $25 billion and with strong earnings from healthcare, document solutions, and electronic materials. The lesson that these stories teach is clear: Merely adopting a new technology does not guarantee advantage. The key is to determine where the new value lies, how to redeploy its distinctive capabilities toward capturing that value, and how to invest in the right complements. Taking Stock Today gen AI and its image models pose a threat to stock photography that’s as existential as the threat that digital cameras posed to film. Most stock-photo platforms that license photos for commercial use are built on the assumption of scarcity of high-quality images, but that scarcity no longer holds, because anybody using a gen AI model can generate the images they want with simple prompts at virtually no cost. Stock-photo platforms have responded mainly in one of two ways, either by adopting AI as a tool to more efficiently tag and categorize the photos in their archives, or by rejecting the technology altogether. Both of these responses are anchored in the old economics of scarcity, and both fail to recognize that the real challenge confronting stock agencies is not competition from cheap synthetic images but the way in which they lead to a collapse of trust, authorship, and rights, which are critical for content to be commercially licensed. Shutterstock, confronted with the same shift, didn’t treat gen AI as a tool to be adopted or a threat to be avoided. Rather than defending its traditional business in an era of synthetic content, the company shifted its focus to the management of trust, authorship, and rights. Next, it applied its strengths in metadata and rights management, along with its relationships with contributors and enterprises, to build a system that makes AI-generated content safe for enterprise use. It then built the complements required to make that system work: It established data-licensing partnerships with OpenAI and NVIDIA, and, most important, set up compensation models that ensure fair payment for contributors and indemnification frameworks that protect customers from copyright risk. Through that combination of reframing, redeployment, and complement-building, Shutterstock has moved from being an aggregator of photos to a governance engine. In a world of infinite photos, it sets the rules of participation for both AI-model developers and content users. Model developers, OpenAI among them, now depend on Shutterstock for rights-cleared access to its photo library as a training set. Enterprise buyers depend on Shutterstock for legal protection, while contributors trust it for fair compensation and visibility. A Reallocation of Power In every technological shift, real advantage shows up as not just higher profits or new business lines but also a reallocation of power within the value chain. That’s what happened in the case of the U.S. retailers Walmart and Kmart. When barcode technology was adopted in retail in the mid-1970s, both companies embraced it early, but with very different results. Kmart used the technology to speed up checkout lanes but didn’t recognize that it could also make possible a new organizational model. Decision-making was decentralized, with store managers responsible for purchasing and inventory, and the company kept it that way. Walmart, by contrast, treated barcodes as not only an efficiency tool but also a system-level capability that could rewire information and coordination across its entire retail network. It invested in complementary infrastructure, including a satellite-communications network, an electronic-data interchange with suppliers, and centralized logistics. This new architecture allowed the company to restock proactively and forecast demand with precision, which gave it the power to standardize supplier practices. Suppliers now had to operate on Walmart’s terms, conforming to its delivery standards and pricing expectations. . . . Most companies today have not learned the lesson that these stories teach about new technologies, which is that unless you get the framing right, adoption alone may simply accelerate your decline. Each technological shift redistributes power, as we noted in the examples above, but winners rarely emerge merely by adopting technology first. The winners, all too often, are those who use the technology to reimagine the architecture of their business—how decisions are made, how capabilities are deployed, and how value and control are redistributed across the value chain. Kodak and Kmart adopted the right technologies but aimed them at the wrong problems. Fujifilm, Shutterstock, and Walmart, by contrast, reimagined their game in light of what the new technology made possible. With the wrong objectives, a company can execute successful pilots and adoption programs but fail to create advantage. With AI, as with previous new technologies, reinvention, not adoption, should be your goal. What matters most is whether your experiments with the technology will help you reframe work, redeploy distinctive capabilities, and assemble the complements that create durable advantage. Unless you do that, AI will accelerate change—but not necessarily in your favor.

What Executives Get Wrong About AI

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