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Our new AI tools and co-pilots have made some huge mistakes. They've given bad advice with overconfidence, they've let themselves be talked into shady deals, they've done things strangely, and they've been incredibly rude. Slip-ups are rare, of course, but when they happen, the internet goes mad. We're happy to give a stubborn AI a break.
But that is a big mistake.
This impulse is partly a result of the threat posed by AI. But I think it also reveals a profound misunderstanding: We still think of AI agents as machines, incapable of real growth and improvement like human employees are. So we mock their mistakes—and point out their shortcomings as if they were cornered Roombas.
But in fact, we have reached an important turning point. Today's AI agents are not static. They can grow and learn if we take the time to train them. Moreover, every company already has the ability to train AI agents themselves.
You don't need a PhD in machine learning. In fact, I've met hundreds of AI agent managers who have never written a line of code. What they do know is how people work and how best to manage them. And they understand that these principles now apply to AI agents as well.
Related: Entrepreneurs are using AI first. Here are 8 questions you should ask first.
The golden rule of human resource management (and AI)
The best managers know that human error is a constant and necessary part of human learning. For an employee to truly reach their potential, they must be given the freedom to push boundaries, experiment, and even fail. Expecting a new employee to never falter is not only unrealistic, but also unproductive. Good managers know that mistakes and growth go hand in hand.
However, exceptional managers know that it is not always the employee who needs to be corrected. Often, it is the manager's method of onboarding, training, or giving feedback to employees that needs to be adjusted. Large companies lose millions of dollars because employees misunderstand policies or processes. However, high-performing managers do not automatically point the finger at others; instead, they use these mistakes as a starting point for self-reflection and improvement.
The same principles now apply when working with AI agents. They don't arrive as finished products. Rather, they (just like humans) need onboarding and the opportunity to learn their new tasks. They need feedback. They need mentoring. In short, managers are discovering that AI agents need the same kind of care that human employees already receive.
Taking advantage of AI’s “teachable moments”
Let's say you work at a bank and are in the process of hiring an AI customer service agent. You uploaded to the agent all the documents that your human employees use to learn about company policies and procedures (these were read and processed in a matter of moments). Company blogs and changing product details can also be incorporated into the AI by simply providing relevant URLs.
Then, when the AI agent is ready to work with customers, it finally has the chance to make its first mistake. And you have the chance to improve it.
For example, an explanation of how to open a new checking account might be too long-winded for customers who expect a quick response. This is not a serious mistake. It is a teaching moment. Direct feedback – “Please provide shorter answers” – leads to immediate, visible improvement.
Each agent response can be designed and customized, which quickly pays off over time. I've seen managers invest time in training their AI employees and turn an eager “intern” into a seasoned professional in just a few months.
The real shift in perspective comes down to seeing these agents for what they are: fallible but eager collaborators, eager to learn if we give them a chance.
What is the benefit of helping AI overcome its mistakes?
The benefits of this shift in thinking are many. In customer service, the enormous time and expense of training human employees usually results in little return. Industry-wide, we lose almost half of these employees every year. It's like a sieve, with company resources going down the drain.
AI agents, on the other hand, aren't going away. Any effort put into training an AI agent will bring profits forever. And those profits are growing fast—a vice president of Wealthsimple, a leading online investment platform, recently estimated that her AI agent delivers the productivity of ten full-time agents. That, by the way, allows those humans to focus on concierge experiences, which are more complex and still require the human touch.
We already know that quality management by humans directly correlates with higher market capitalization. Quality management by AI agents promises an even more positive effect. AI agents never forget and never leave the world, allowing management efforts to be scaled and shared.
But the benefits go beyond skilled AI agents. Because AI needs human management and feedback to be successful, it doesn't just take away jobs, it creates new and often better ones. I've seen customer service employees take on roles in managing AI, giving them a new sense of ownership in the company.
In fact, managers who learn to coach AI agents make themselves indispensable. They have learned to use a tool that can help them increase productivity in every other department in their company.
Related: You can fear it and still use it – Why do so many American workers shy away from AI?
A future where we are all managers
And this shift is not limited to a few roles. From now on, pretty much everyone will be an AI manager. We will all have AI agents working for us and increasing our productivity. And that means that this shift I'm describing here – thinking of AI agents as learning, constantly evolving employees – will be discussed far beyond the boardroom.
With the introduction of a new paradigm, agents will become as intelligent as we make them together.
First, we extend the same courtesy to AI agents that we extend to humans – understanding that everyone (and every bot) makes mistakes. Then we do what great managers have always done: coach, train, and remove obstacles. After all, they are learning machines, just waiting for the next lesson that will allow them to leap forward again. This is where we come in.
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