There is one concern I hear from CEOs more often than any other when the conversation turns to AI, and I think it deserves a direct answer.
The concern is control. If you let AI produce content, respond to investors, manage communications or handle any function that touches the public markets, how do you make sure it does not say something it should not? How do you prevent it from contradicting your filings, making a forward-looking statement you have not authorized, or producing something that does not sound like your company?
It is a legitimate question. Public companies operate in a regulated environment. Every piece of external communication carries risk if it is inaccurate, misleading or inconsistent with what the company has disclosed. The executives asking this question are not being resistant to change. They are being responsible.
But here is what I have learned from our own experience building and deploying AI at AGORACOM: the governance framework required to use AI responsibly is simpler than most CEOs expect. And in many cases, it actually improves the consistency and reliability of your communications rather than undermining them.
The Control Problem Most CEOs Imagine Is Not the Real Risk
When most CEOs picture AI going wrong, they imagine a rogue system publishing something catastrophic. An unauthorized earnings projection. A made-up partnership announcement. A combative response to a hostile shareholder.
In practice, that scenario does not happen when AI is deployed properly, because properly deployed AI does not have the ability to publish anything on its own. The control point is simple: AI drafts, humans approve. Nothing reaches the public without a person reviewing it first.
This is not a limitation of the technology. It is a design choice, and it is the right one for any public company. The value of AI is not that it removes humans from the process. The value is that it handles the time-consuming work of producing the first draft, so the human reviewer can focus on judgment rather than production.
When I explain this to CEOs, I often see the tension leave the room. They were imagining a system that operates independently. What they are actually looking at is a system that does the work and waits for a person to approve it. That is not a loss of control. It is a better use of the control they already exercise.
A Governance Framework That Actually Works
Let me be specific about what a practical AI governance model looks like for a small-cap public company, because I think the simplicity of it will surprise most executives.
It comes down to four things.
First, train the AI on your approved materials. This means your public filings, press releases, investor presentations, website content, approved messaging and any other materials that represent what your company has actually disclosed. The AI should produce content that is consistent with these sources. It should not speculate, extrapolate or introduce information that is not in its training set.
Second, define the boundaries. Every AI agent should know what it is allowed to do and what it is not. A content agent should know it can produce shareholder updates, social posts and investor summaries, but it should not produce guidance on financial results, comment on pending transactions, or engage with hostile commentary. An engagement agent should know it can answer questions based on public filings, but it should flag any question about undisclosed information for a human to handle.
Third, require human review before publication. This is the non-negotiable. No AI-produced content should reach an investor, a shareholder, a social media platform or a website without a qualified person reviewing it first. This review does not need to be lengthy. Once you trust the system, reviewing a well-produced draft takes a fraction of the time it takes to produce one from scratch. But the review step is what maintains accountability.
Fourth, keep a record. Every piece of AI-produced content should be traceable. Your team should know what the AI generated, what was changed during review, and who approved the final version. This is good practice for any public company communication, and it is especially important when AI is part of the process because regulators and auditors will eventually ask about it.
That is the framework. Train, define boundaries, review, and document. Most companies already do some version of this with their human communications teams. The only difference is that AI makes the process more explicit and more consistent.
What Changes as You Scale
The framework I just described works whether you have one AI agent handling a single function or multiple agents handling several.
The practical difference as you scale is not that the governance becomes harder. It is that the governance becomes more valuable. When you have one person producing all of your investor communications, the quality and compliance of that output depends entirely on that person’s judgment, energy and attention on any given day. When you have AI agents producing first drafts based on approved materials, with defined boundaries and mandatory human review, the baseline quality becomes more consistent, not less.
This is counterintuitive for many CEOs, but I have seen it play out directly. The companies using AI with proper governance often produce more compliant communications than the companies doing everything manually, because the AI applies the same standards every time. It does not have bad days, it does not forget a disclosure requirement, and it does not accidentally use language that goes beyond what the company has authorized. The human reviewer catches anything the AI misses, but the AI misses less than most people expect.
The point at which governance requires more attention is when you move from AI handling one function to AI handling several interconnected ones. At that stage, you need clarity about which agent handles which responsibility, how information flows between them, and who on your team oversees each function. But that is a normal management challenge that any growing company faces when it adds capacity. The fact that the new capability is AI rather than a person does not fundamentally change the management discipline required.
How We Handle This at AGORACOM
I am sharing our own approach not because it is the only way to do it, but because I think it is helpful for CEOs to see that this has been thought through in a real operating environment.
At AGORACOM, every AI agent operates within a clearly defined role. Connor produces content. Angela handles investor engagement. Each agent is trained on approved materials and operates within documented boundaries. Nothing an agent produces reaches the public without human review. Our editorial and compliance process applies to AI-produced content the same way it applies to content produced by a person.
When we expanded from one agent to several, we added coordination rules. Which agent handles which type of request. How information moves between agents. When something gets escalated to a human rather than handled by the system. These are the same kinds of operational decisions any company makes when a department grows. The fact that the team members are AI agents rather than people changes the technology involved but not the management principles.
The result is a system that produces more consistent output than we could achieve with a purely manual process, with better compliance discipline and without sacrificing the human judgment that public company communications require.
If you are a CEO considering AI for the first time, start with the framework I described: train on your approved materials, define the boundaries, require human review, and keep records. That is enough to begin. You can refine and expand as you go, but you do not need a complex governance plan before you start. You need a simple one that you actually follow.
