Ayomide

I recently worked on a project where we built what we called a CO-CEO AI agent for a marketing company. The idea was simple: instead of treating the AI as a background tool, we brought it into the decision-making process almost like another executive. Its main job was to help the CEO design marketing strategies for different clients.

Now, here’s the twist. We didn’t just let the AI spit out strategies in isolation. It was invited to client meetings, not literally of course, but through structured prompts where the full context of the client’s business, challenges, and goals was fed in. That way, its recommendations weren’t generic “playbook strategies,” but tailored to the actual discussion the leadership team was having. That made it much more of a trusted advisor than a black-box machine.

From this project, I learned that there are two layers to making AI advice truly useful and trustworthy for leaders:

1. Where It Should Assist
Pattern spotting across campaigns: Leaders don’t always have the time to compare 50 different client reports. The AI could highlight trends (e.g., “clients in retail are seeing 20% higher engagement when campaigns run mid-week”).

Scenario testing: Instead of one “best” strategy, the AI could lay out three options: low-risk, high-growth, or balanced. This gave the CEO choices rather than a single directive.

Speed on background research: Before walking into a client strategy session, the AI could summarize competitor campaigns, past results, and market conditions in minutes.

These are areas where AI’s scale and speed give leaders an advantage without replacing their judgment.

2. Checks to Keep It Reliable
Context gatekeeping: The AI was only as good as the context it had. We made it a rule that client objectives and constraints must be captured first (almost like a briefing note) before the AI gave advice. No context, no strategy.

Audit trail of reasoning: Every recommendation had to come with a short rationale, “this works because past campaigns in similar industries showed X, Y, Z.” This gave the CEO confidence in the “why,” not just the “what.”

Version control for prompts: As we refined how we asked the AI questions, we tracked changes. For example, when we shifted from “generate campaign ideas” to “act as a CMO and propose three strategies with risks and trade-offs,” we documented it. That way, if a change caused worse outputs, we could roll back quickly.
 


Human override always on: The AI was never treated as final authority. The CEO still made the call, but with stronger input in less time.

Honestly, what made this whole setup work wasn’t the AI being “super smart.” It was the way we used it. We never treated it like it was going to run the company or make the final call. Instead, it was more like a second set of eyes, someone in the room who could throw out a few options, show the risks, and spot patterns the rest of us didn’t have time to see.

The clever part wasn’t the output, it was the process: making sure it only answered once we’d given it the right context, tracking how we asked questions so we didn’t lose improvements, and always keeping a paper trail of its reasoning so it didn’t feel like magic. At the end of the day, the CEO still made the decisions. The AI just made those decisions faster and more informed.

That’s really the trick to building trust. You let the AI contribute, but you don’t hand over the steering wheel. It’s not there to replace judgment, it’s there to make good judgment easier.

Using myself as a practical example pre my knowledge of prompt engineering and my use of ChatGPT, I have used AI to write content scripts, respond to emails, draft the flow of strategy sessions for a business leader's workshop, design SOPs, training modules, and presentation scripts in a global manufacturing context. I can tell for a fact that the results I have gotten post my knowledge of prompt engineering and design shows how prompt design can elevate or limit the outcome.
 
My initial prompts was something like "Create an SOP for the Logistics team" or "What caused Line 2's downtime yesterday" but has now evolved into "on yesterday’s downtime on Line 2, using available sensor logs, operator notes, and maintenance history, identify the most probable root causes and suggest low-cost, high-impact fixes relevant to skincare batch making process" or "Create a logistics SOP for a skincare manufacturing plant in Nigeria, including job grade responsibilities, escalation paths, tool access levels, and cross-functional dependencies.” 
 
I think the prompts given to an AI model is critical because it determines whether the output is surface-level or genuinely actionable and within the context of the problem you are trying to solve. The improvement didn’t just help me get better results; it helped the AI model understand my context more deeply.
 
In other words, better prompts led to smarter AI support which in turn leads to better decision making.