The CEO sends an all-company email: “We need to do something about AI. Everyone should be thinking about how to use it in their work.”
A month later, nothing has changed. A few people signed up for ChatGPT. Maybe someone tried to build something and it didn’t work. But there’s no momentum, no strategy, no real progress.
This scenario plays out at companies everywhere. And there’s a specific reason why it fails—there’s no one to actually make it happen.
The Missing Link
When companies approach AI transformation, they usually make one of two moves. Either they create a central AI team that departments submit requests to, or they just tell everyone to figure it out themselves. Both approaches fail for the same reason: they’re disconnected from the actual work.
The central AI team builds things months later that don’t quite fit the workflow. The “figure it out yourself” approach creates chaos—35 different point solutions with no governance or shared learning.
What works is identifying people inside each department who can act as the bridge between AI capability and business need. These people have a specific role, and understanding that role changes everything about how AI gets implemented.
What an AI Operator Actually Does
An AI Operator isn’t a developer. They’re not a full-time AI specialist or an external consultant. They’re employees who already work in your departments—marketing, sales, operations, wherever—who become the point person for AI opportunities in their area.
In any department or company, about five to ten percent of people are already heading this direction. They’re curious about AI. They want to learn. They see opportunities. The key is identifying these people and giving them the framework, tools, and time to actually do something about it.
The approach is straightforward: ask who wants to be involved. “Who wants to work with me on this? We’re going to learn about AI, you’re going to become the point person inside your group, you’re going to help discover use cases.” You’re looking for raised hands, not résumés. Curiosity matters more than credentials.
This is fundamentally different from the traditional IT approach. AI Operators sit inside the business units. They understand the actual work because they do the actual work. They can spot opportunities that outsiders miss. And when solutions get built, they drive adoption because their colleagues trust them.
The Evolution of Operations Roles
This pattern has happened before. Marketing operations started as “the person who knows how to use the marketing tools.” Sales operations started as “the person who makes Salesforce actually useful.” Revenue operations started as “the person who connects marketing, sales, and customer data.”
Each role emerged when technology became critical to a function but too specialized for everyone to master, yet too important to outsource entirely to IT. AI Operations is following the same path.
Whether companies bring in external help to implement a framework or do it themselves, identifying employees who have a proclivity for this type of thinking and then empowering them is one of the most impactful things a company can do right now.
Who Makes a Good AI Operator
The best AI Operators share three traits: they’re curious, they have high agency, and they’re systems thinkers.
Curious means they ask “why?” and “what if?” They experiment with new tools without being asked. They actually read articles about AI. High agency means when they see a problem, they try to solve it rather than waiting for permission. They’re comfortable with ambiguity. Systems thinkers understand how pieces connect, can map workflows, and see second-order effects.
Here’s what surprises people: technical skills aren’t required. Great communicators with domain expertise who are willing to learn often make the best AI Operators.
That marketing manager who’s always figuring out how to connect different tools? That operations coordinator who everyone goes to with questions? That salesperson who built their own tracking system in Excel? Those are your AI Operators.
How AI Operators Actually Work
AI Operators need to be trained on a specific approach. They learn how to do discovery—asking the right questions to uncover AI opportunities. They learn to use tools like ChatGPT and Claude effectively. And critically, they learn to document what they find in a standardized way.
The documentation approach is key. When an AI Operator discovers a potential opportunity, they capture it systematically: What’s the problem? Who’s involved? What are the steps? What systems are we using? How long does it take now? What would success look like?
This structured approach solves a critical problem. When everybody is documenting opportunities the same way, leadership suddenly gets what they want—a transparent look into where innovation lies within the company. They can see which departments have the most opportunities, what types of solutions are needed, potential ROI, and what resources would be required. They can prioritize based on actual impact rather than whoever lobbies loudest.
How AI Operators Evaluate Solutions
AI Operators assess opportunities starting with the simplest approach—can existing AI tools solve this?—then move to connected automations, and only then to custom development. This prevents over-engineering and ensures the AI Operator makes judgment calls based on both business context and technical feasibility.
What Happens When It Works
When companies properly implement an AI Operator program, results come quickly. Initial discovery often surfaces 40+ use cases—some excellent, some not actually AI problems, some not worth the investment. AI Operators help evaluate and prioritize.
Within weeks, solutions start delivering tangible results. One 400-person company saw 28 implementations across six months, $180,000 in annualized time savings, and 92% adoption rates. More importantly, AI shifted from scary buzzword to practical toolkit.
The Goal: Democratizing AI Knowledge
The end goal isn’t to create dependency. It’s to democratize AI knowledge throughout the organization. If done correctly, everything known about AI implementation gets spread across the AI Operators. They’re only a few weeks ahead of everyone else in the market, but that’s enough when they’re concentrating on it full-time.
Teaching AI Operators how to solution for themselves is the key. How to use the three-level framework. How to evaluate whether something needs AI or just automation. How to capture use cases. How to measure impact.
When AI Operators can do this themselves, you’ve democratized all of that knowledge. The organization has a platform implemented and can innovate continuously without external dependency.
The Alternative: What Happens Without AI Operators
Without AI Operators, companies face either paralysis or chaos. The CEO’s AI mandate leads nowhere, or employees experiment without guidance—violating security, creating unmaintainable solutions, building 35 different point solutions with no governance.
Frontline workers see opportunities but can’t execute. Their best ideas—informed by deep subject matter expertise—either die or become technical debt.
How to Start
Look for who’s already experimenting—using ChatGPT to improve their work, showing initiative without being asked. Look for the unofficial tech support in each department who helps with Excel, integrates tools, solves problems.
Start with two to four hours per week. Make it official in goals and performance reviews, but don’t pull them entirely from their current role. Curiosity matters more than credentials.
The Bottom Line
AI Operators bridge the gap between capability and execution. They understand both business and technology, spot opportunities, implement solutions, and drive adoption among colleagues who trust them.
In two years, this won’t be an emerging role—it’ll be standard, like marketing ops and sales ops. Companies that move now have an advantage.
The winners aren’t those with the best technology. They’re those with the best people using technology to solve real problems.
Want help identifying and empowering AI Operators in your organization? Our AI Audit includes training your potential AI Operators on the three-level framework and giving them their first wins. Learn more about our AI Audit.
