Every few years a new technology or tool shows up. People debate it. Some dismiss it. Some overreact to it. Eventually it either becomes normal infrastructure or it fades away.
That is what happened with cloud. That is what happened with mobile. And now it is happening with AI.
As AI started showing up inside everyday tools, I began hearing more discussion around a Chief AI Officer or Chief Augmentation Officer. My first instinct was skepticism.
Then I began integrating AI deeper into my own workflows and into how I manage my team.
That is when the conversation shifted for me.
AI Is Already Embedded
AI is not a future initiative. It is already sitting inside email platforms, CRMs, accounting tools, support systems, developer environments, and analytics dashboards. Even very basic software now ships with AI features enabled by default. That means we are no longer asking whether AI is central to our business. We are asking where it exists, what it touches, and how exposed we are.
That is a different question.
The Small Company Illusion
It is easy to believe that a small startup can manage this informally. The team is small. Communication is fast. Everyone feels aligned. But the risk does not disappear just because the headcount is low. When individuals independently adopt AI into their own workflows without clearly communicating how and where they are using it, risk accumulates quietly. Prompts get reused. Data gets pasted. Automations get built. Vendor features get enabled.
Nothing dramatic. Just incremental exposure. The difference between a small company and a large company is not whether the risk exists. The difference is the compounding effect.
As the organization grows, the surface area expands. More tools. More data. More workflows. More independent decisions. Without visibility and ownership, complexity multiplies.
This Is Not About Hype
I do not see a Chief AI Officer as a trendy title. I see it as a response to operational reality.
Someone has to understand:
- Where AI is being used
- What data is flowing through it
- What vendors are doing with that data
- What policies govern its use
- What changes when new features are introduced
This is not theoretical. It is practical. It is day-to-day operational awareness.
Do We Need a Separate Executive?
I am not sure there is a universal answer. But in my opinion, attaching this responsibility to the CTO or COO often thins the focus of that executive.
The CTO already owns architecture, delivery, reliability, and technical staffing. The COO already owns operations, execution, and performance. Adding cross-department AI governance on top of those responsibilities risks turning this into a side concern instead of a primary one.
This role is not purely about technology.
Technology is the tool.
What this position is really examining is how work happens. How decisions are influenced. How data moves. How exposure grows. How policies translate into behavior. How AI is actually being used across the company. How users are trained. How prompting improves. How new tools are discovered and evaluated. How successful workflows in one department can be applied to another without duplicating effort or data.
This is not only governance. It is enablement. This role and this department look at how to properly use AI across multiple departments. They help teams write better prompts. They surface better techniques. They evaluate new software. They identify what is working in one area and standardize it so others can benefit.
For example, a workflow might begin in sales and end in finance or accounting. If those departments are not aligned, data can be duplicated, systems can conflict, and breakage can occur. This is where the AI executive and team step in. They see across departments. They ensure workflows connect cleanly. They prevent fragmentation before it spreads.
It is about maximizing value while reducing redundancy. Standardizing which tools are used. Standardizing how information is processed. Preventing a patchwork of disconnected prompts and overlapping AI systems. This is why I increasingly see it as more than a single executive. It is a team. Not scientists. Not researchers. But people who understand where AI touches the business, how to improve its use, how to coordinate departments, and how to make the entire organization smarter in how it applies AI.
We do not expect the CEO or CTO to personally run HR programs. HR systems are essential infrastructure, but their governance and day-to-day oversight sit with a dedicated function. AI is starting to look very similar.
That is operational governance combined with operational improvement.
The question should not begin with the title. It should begin with an honest audit:
- How much AI are we using?
- Where does it exist?
- What is our exposure?
- How fast is that exposure compounding?
If the answers show depth and complexity, concentrated ownership makes sense.
Where I Land
As I continue integrating AI into how I build systems and manage teams, I see one thing clearly. AI multiplies capability. It also multiplies exposure.
At small scale, that exposure hides easily. At larger scale, it compounds.
Titles are secondary. Ownership is not.
Companies that take the time to understand where AI resides within their business and assign clear accountability will move forward deliberately.
Those who assume it is merely another feature will eventually discover that it was infrastructure all along.