Dear CIO,

In the early days of electronic computing, it was not obvious that IBM would dominate the industry. In fact, if you were placing bets right after World War II, you might have chosen the team behind ENIAC, J. Presper Eckert, and John Mauchly. They had credibility. They had visibility. They had proven that large-scale electronic computation actually worked. ENIAC outperformed electro-mechanical systems by orders of magnitude. It was a breakthrough. From ENIAC to EDVAC concepts to UNIVAC, and eventually into what became associated with Sperry, they looked like the future. And yet, when computing became a mainstream enterprise platform, IBM became the default choice for corporations and governments.

So what happened? For CIOs navigating the AI era, this story is more than history. It is a warning.

Best Regards,
John, Your Enterprise AI Advisor

Being First Is Not Enough

World War II created a unique environment where money was available, talent was concentrated, and urgency was non-negotiable. ENIAC became public proof that electronic machines could outperform electromechanical systems by orders of magnitude. However, in the mid-1940s, “computer” was not even a settled category. Some people saw these machines as specialized scientific devices, and others imagined them as general-purpose information systems that could reshape administration and business. Eckert and Mauchly were firmly in that second camp.

Eckert and Mauchly believed in the latter. UNIVAC signaled that computing would move beyond military ballistics and into census bureaus, insurance firms, and enterprises drowning in data. They were early. They were bold. They were right about the future. However, being right about the future does not guarantee control of it.

As soon as computing shifted from research breakthrough to enterprise procurement decision, the criteria changed.

The question stopped being: “Who proved this works?”

And became: “Who can make this safe, scalable, and sustainable?”

IBM understood that transition better than anyone.

Large organizations make high-cost, high-risk decisions carefully. They do not just buy hardware. They buy predictability. IBM had decades of experience with punch-card systems. It had field organizations, service networks, executive relationships, and institutional trust. When customers were making multimillion-dollar bets on unfamiliar technology, IBM felt safer.

Sound familiar? Today, your board is not asking whether AI works. They are asking whether it is safe, compliant, scalable, and governable.

Early computers were not easy to operate. They were temperamental and required skilled operators and constant attention. IBM, however, built field engineering teams, training programs, documentation standards, and installation processes. It institutionalized support making each deployment made the next one smoother. That kind of operational discipline compounds over time.

Contrast that with many AI deployments today, isolated pilots, shadow AI projects, and teams spinning up Kubernetes clusters without full integration into enterprise controls. As we have seen in discussions around shadow AI and technical debt, innovation without integration creates fragility.

If AI remains a collection of experiments instead of an enterprise system, someone else will industrialize it.

One of the biggest fears for any enterprise buyer is getting stuck. What happens when the system needs to grow? What happens when a better model comes out? IBM’s later platform strategy, most famously the System/360, focused on compatibility and continuity. Customers could scale without rewriting everything or abandoning their prior investment. That promise reduced risk dramatically.

In the AI era, the same question looms: Will today’s models, pipelines, and vector databases trap you in brittle architectures? Or are you building upgrade continuity into your AI platform strategy?

IBM also understood that procurement friction can kill adoption. Leasing models and flexible deal structures lowered upfront costs and made decisions easier. When technologies are close in capability, the vendor who makes the buying process smoother often wins.

Today, the same dynamic applies internally. If your AI governance model creates friction, business units will bypass you. If it enables safe acceleration, you become indispensable. You cannot delegate this responsibility. You must own it.

Eckert and Mauchly had an invention advantage, but IBM built an ecosystem advantage. It created training pipelines, standardized job roles, procurement familiarity, and a growing universe of software and tools around its platforms. Once that ecosystem solidified, the choice was about organizational fit and long-term stability. That is a powerful position to occupy.

We are watching the same ecosystem battle unfold in AI. The winners will standardize AI operating models, embed security and observability, align DevOps, DevSecOps, and SRE with AI delivery, and create repeatable governance. As we have seen in the evolution of AI in the enterprise, real transformation happens when AI becomes embedded into the operating fabric.

Sperry and UNIVAC absolutely deserve their place in history. ENIAC proved what was possible. UNIVAC signaled commercialization, but commercial dominance rarely goes to the first mover.

It goes to the organization that reduces buyer risk, scales delivery, institutionalizes support, creates upgrade continuity, and establishes the operating standard. IBM built conditions under which enterprises could safely adopt machines at scale. That is the deeper lesson.

We are in the ENIAC phase of AI. There is brilliance. There is speed. There is an invention. Invention opens markets, but industrialization wins them. If AI becomes fragmented across business units, driven by isolated CAIO mandates, or built without integration into core infrastructure, you risk replaying history in reverse. You become the organization with breakthrough pilots, but no durable platform.

The CIO’s role is not to slow AI down. It is to industrialize it. That means:

Being first may secure your place in the story, but the AI race is not just about who innovates fastest. It is about who industrializes best.