CEOs caught between the fear of disappearing and the temptation to adopt technology without transforming the business
What is the Kodak syndrome?
In 1975, engineer Steven Sasson developed the first functional prototype of a digital camera inside Kodak. The device captured electronic images of just 0.01 megapixels and took 23 seconds to record a photograph, but it represented a radical shift in how images could be captured and stored.
The paradox is well known: the company that had built a global empire around chemical photography had unknowingly invented the technology that would ultimately erode its own business model.
For decades, Kodak operated under a nearly perfect economic logic: sell relatively affordable cameras to drive recurring purchases of film and photo development services. Every photograph generated revenue. Digitalization eliminated that cycle, allowing images to be reproduced infinitely at virtually no cost.
The company’s leadership understood the potential of the innovation, but also recognized that rapid adoption threatened the financial core of the business.
While Kodak hesitated, companies like Sony, Canon, and Nikon advanced in the development of digital sensors and electronic cameras. By the time Kodak reacted, the market had already changed.
In 2012, Kodak filed for bankruptcy under Chapter 11.
Harvard professor Clayton Christensen explained this phenomenon in his book The Innovator’s Dilemma: leading companies often fail not because they ignore innovation, but because their current business model incentivizes them to delay technologies that could destroy their existing profitability.
Today, that same logic is resurfacing in the corporate debate around artificial intelligence.
The new corporate fear: being left behind in the AI revolution
Unlike digital photography, generative artificial intelligence is emerging simultaneously across multiple industries: technology, finance, marketing, retail, media, and manufacturing.
The competitive pressure is evident. According to the McKinsey Global Institute, generative AI could add between $2.6 and $4.4 trillion annually to the global economy. Meanwhile, a Goldman Sachs analysis estimates the technology could increase global GDP by up to 7% over the next decade.
In this context, no CEO wants to be remembered as the executive who ignored a technological revolution.
Yet the result of that pressure has been paradoxical. In many organizations, artificial intelligence appears first in corporate messaging rather than in operational processes.
Pilot projects, strategic announcements, and internal presentations multiply while the structure of the business remains largely unchanged. This phenomenon already has a name: “PowerPoint AI” — strategies where artificial intelligence exists more in presentations than in operations.
The hidden cost of integrating AI
The dominant narrative often presents artificial intelligence as a tool capable of reducing costs and automating tasks. In practice, companies implementing it deeply are discovering the opposite: in the short term, AI increases technological complexity and operational costs. Adopting it seriously means redesigning entire systems.
Organizations must invest in intensive computing infrastructure, massive data storage, advanced technology architecture, and specialized talent in machine learning, data engineering, and cybersecurity. Moreover, artificial intelligence doesn’t just change tools — it reshapes entire workflows.
In marketing, for example, its impact goes far beyond generating text or images. It can transform the entire value chain: audience research, data analysis, creativity, content production, A/B testing, and real-time campaign optimization. Integrating AI at that level requires reconfiguring how the organization operates internally — a threshold many companies have yet to cross.
The companies betting big on AI
While some organizations experiment with isolated pilots, others have decided to integrate artificial intelligence as a strategic infrastructure layer, even at the cost of multibillion-dollar investments.
One of the most visible cases is Microsoft. The company has invested more than $13 billion in developing its generative AI ecosystem, integrating these capabilities into its enterprise productivity suite and cloud platform through tools such as Microsoft Copilot.
The goal is not to launch experimental products, but to redefine digital productivity in the corporate environment.
A similar approach can be seen at Amazon. Through Amazon Web Services, the company is building one of the most robust global infrastructures for developing AI-based applications. This includes specialized data centers, chips designed for machine learning, and platforms that allow other companies to build AI systems.
In the financial sector, JPMorgan Chase has begun deploying internal artificial intelligence tools for tens of thousands of employees. These solutions automate financial analysis, report generation, and complex operational tasks.
In retail, Walmart is using artificial intelligence to optimize inventory, logistics, dynamic pricing, and customer experience by integrating predictive analytics across its global operations.
In all these cases, the difference is not the technology itself, but the depth of its implementation. Artificial intelligence is not appearing as an experimental add-on, but as a structural layer of the business.
The real dilemma for CEOs
The Kodak syndrome is often interpreted as a warning about the need to adopt new technologies. But its real lesson is far more uncomfortable.
Leading companies often fail because their current model works too well. When an innovation emerges that can transform the market, adopting it requires accepting something difficult for any organization: the model that generated its success may become irrelevant.
Artificial intelligence presents exactly that dilemma. It can improve efficiency, but it may also force companies to redesign entire processes, alter organizational structures, and invest massive resources before seeing results. At that point, the companies experimenting with technology become distinct from those willing to reinvent their business.
The strategic question that will define the next decade
At this historic moment, the question CEOs face is not whether they should adopt artificial intelligence. The market has already answered that. The real question is far deeper: which part of the business are we willing to transform because of it?
Business history shows that technological revolutions rarely destroy companies that completely ignore innovation. Instead, they often destroy those that understand it too late — or adopt it without transforming their business.
Kodak feared destroying its film-based model. Today, many companies fear being left behind in the AI revolution.
Between those two fears lies the question that will define the next corporate decade: Will AI become just another tool — or the beginning of a corporate reinvention?
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