The problem no one is naming
Most AI projects do not fail because the technology does not work. They fail because no one in the organization owns them. Not really.
The CTO treats it as an IT problem. The CMO uses it for copy. The CEO approves the budget and watches demos. The VP of Operations says it is interesting but we need to ship product. And so the initiative drifts. A vendor gets hired. A proof of concept gets built. Six months later, the proof of concept is still a proof of concept.
Meanwhile, somewhere — in a competitor's office, in a company you have not heard of yet — someone is running their operations with AI agents. Not piloting. Running. The difference between those two states is not the technology. It is whether there is a person in the building who has made this their job.
Spring 2026 is not a moment to study the problem. It is a moment to move. The companies that operationalize AI in the next six months will have systems that have been learning for six months when the rest of the market catches up. That gap compounds. You do not close a six-month compounding advantage with a better vendor.
What building an AI system actually requires
Here is the part that surprised me.
Building an AI agent is not like installing software. It is not like deploying a tool. At inception, it is a deeply personal act. You have to give it a personality. You have to decide what it knows, how it communicates, what it values, what it escalates and what it handles. You are not writing code — you are making decisions about character.
The instincts required are not technical instincts. They are management instincts. The same things that make someone a good manager — knowing what to delegate, how to set context without over-specifying, how to hold someone accountable for outcomes rather than process — those are the same instincts that determine whether an AI agent actually works. You have to be able to teach. You have to be patient when it gets something wrong, understand why it got it wrong, and rebuild the context until it does not get it wrong again.
The other thing no one tells you: the hardest part is not building the first agent. It is building the second, third, and fourth — and making sure they work together. An organization running on AI is not a single bot. It is a set of agents with defined responsibilities, handoffs, and shared context. Designing that is an organizational design problem. It requires someone who has thought hard about how work actually flows through a company, where decisions get made, where information gets lost, and what the real cost of a bad decision is.
What a Chief AI Officer actually does
This role does not belong to the CTO. A CTO thinks about systems architecture, infrastructure, and engineering teams. Those are the right things for a CTO to think about. AI operations requires something different: an understanding of what decisions actually cost, how business processes connect to financial outcomes, and where the highest-leverage interventions are in an operating company.
It does not belong to the CIO either. Information systems and AI systems have different rhythms, different failure modes, and different success criteria. Confusing them is expensive.
The right profile is someone who has run a P&L. Someone who has sat in an operations meeting and understood immediately which problem, if solved, changes the quarter. Someone who can look at a supply chain or a sales motion or a finance process and see not just where AI could help, but where it could change the structure of how the work gets done.
The CAiO is also the person who knows how to fail fast without burning the organization's trust in the process. Bad AI implementations do not just fail — they create skepticism that slows down everything that comes after. The right person manages that risk the same way they would manage any transformation risk: by scoping correctly, communicating honestly, and delivering visible wins before pushing for structural change.
The cost of not having one
I want to be direct about this, because the industry tends to soften it.
Not having someone in this role is not a feature gap. It is not the equivalent of being late to adopt a new software tool. The compounding nature of AI systems means that organizations that move now are not just ahead for now — they are building advantages that become harder to replicate over time. An AI agent that has been running daily operations for a year has absorbed a year of institutional context. It has made mistakes, been corrected, and learned from the correction. It is not the same agent it was at launch.
The companies watching right now are not playing it safe. They are making a bet — that the advantages being built today will not be too large to overcome when they finally move. That bet has historically not aged well in technology transitions. The companies that waited on e-commerce, on mobile, on cloud infrastructure — most of them spent years trying to catch up. Some never did.
AI is not the same as those transitions in every dimension. But it shares the compounding dynamic. And the window to get ahead of the compounding is not indefinite.
What this person looks like
They have operated before. Not just advised — operated. They have been accountable for a number. They understand what it means when a process breaks at 11pm on a Tuesday before a board meeting.
They are comfortable with ambiguity without being paralyzed by it. The AI landscape changes fast enough that anyone who needs certainty before moving will always be behind. The right person moves with partial information, monitors closely, and adjusts.
They can work at both altitudes: strategic enough to see where AI changes the company's competitive position, and operational enough to get into the details of a broken workflow and fix it. Most people are strong at one or the other. This role requires both.
They understand that their job is to create organizational capacity, not dependency. The goal is not to be the only person in the company who understands AI. The goal is to build a company where AI fluency is distributed — where the team operates at a higher level because the tools are embedded and the people know how to use them.
Finally — and this is underrated — they have empathy. Not as a soft skill but as a functional requirement. Building AI systems requires understanding how people think, what they resist, and how to introduce change without triggering the immune response that kills most transformation initiatives.
What I have learned
I have been doing this work at a public company for the past two years. I did not start with a playbook. I started with a belief that AI was going to change how organizations operate, and a willingness to figure out what that meant in practice, inside a real company, with real stakes.
What surprised me most: the technology was the easy part. The hard part was the organizational design — figuring out what the agents needed to know, how to give them that context, how to structure the handoffs between human judgment and automated execution, and how to build trust in the system so that people actually used it.
The executives who will navigate the next five years well are the ones building this capability now — not buying it, not waiting for it, but building it. That requires someone in the seat who owns it. The title matters less than the accountability.
If you are thinking about this for your organization, I am reachable at hello@jabondano.co.