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Our new AI tools and co-pilots have made epic blunders. They’ve been overconfident and given the wrong advice, been recommended dodgy deals, made things weird, or been downright rude. Mistakes are rare, of course, but when they do happen, the Internet goes wild. And we love to put wayward AIs down.
But that’s a big mistake.
This impulse stems in part from feeling threatened by AI, but I also think it exposes a deep-seated misunderstanding: We still think of AI agents as machines that can’t truly grow and improve the way human employees can. So we mock their mistakes and point out their shortcomings as if they were a Roomba backed into a corner.
But in reality, we are reaching a major tipping point. Today’s AI agents are not static. They can grow and learn if you teach them over time. What’s more, every company already has the power to teach their AI agents themselves.
You don’t need a PhD in Machine Learning. In fact, I’ve seen hundreds of AI agent managers who have never written a single line of code. do They know how humans work and how to manage them most effectively, and they understand that those principles apply to AI agents as well.
Related: Entrepreneurs rush to embrace AI. Here are eight questions to ask first.
The Golden Rules for Managing People (and AI)
Great managers know that human error will always be an essential part of human learning. For employees to truly realize their potential, they need to be given the freedom to push boundaries, experiment, and even fail. Expecting a new hire to never fail is not just unrealistic, it’s counterproductive. Great managers know that failure and growth go hand in hand.
On the other hand, great managers also know that it’s not always the employee that needs fixing. Often it’s the manager’s onboarding, training, and how they provide feedback that needs adjusting. Large companies lose tens of millions of dollars because employees misunderstand policies and processes. But instead of automatically assigning blame, great managers use those mistakes as a springboard for self-reflection and improvement.
The same principles apply when dealing with AI agents: AI agents don’t come finished. Rather (just like humans), they need onboarding and opportunities to learn new tasks. They need feedback. They need mentoring. In short, managers are finding that their AI agents need the same kind of leniency they already give to their human employees.
Seizing AI’s “learning opportunities”
For example, say you work in a bank and you have AI customer service agents. You have uploaded all the documents that your human employees use to learn about your company policies and procedures to your agents (they are read and understood instantly). Your company blog and product details that change can all be pulled into the AI ​​as well, simply by providing the relevant URLs.
And once your AI agent is ready to begin serving customers, it finally has a chance to make its first mistake — and then some to improve upon.
For example, instructions on how to open a new checking account may be too long for a customer looking for a quick answer. This isn’t a fatal flaw; it’s a teaching moment. Providing direct feedback (“Please give us a shorter answer”) leads to immediate, measurable improvement.
Because every response from an agent is shaped and crafted, the benefits accumulate rapidly over time. I’ve seen managers who take the time to coach their AI employees turn enthusiastic “interns” into seasoned professionals in a matter of months.
The real perspective shift here is to recognize these agents for what they are: fallible but engaged employees who are eager to learn if you give them the opportunity.
What you get from AI coaching Overcoming mistakes
The benefits of this mindset shift are manifold. In customer service, the sheer amount of time and money spent training human agents typically limits the results. Across the industry, nearly half of all hires are lost each year, creating a sieve through which company resources are wasted.
In contrast, AI agents aren’t going anywhere. All the effort put into training them will continue to generate benefits in perpetuity. What’s more, those benefits will grow rapidly. A VP at Wealthsimple, a leading online investment platform, recently estimated that the company’s AI agents are now as productive as 10 full-time human agents. This, incidentally, frees up humans to focus on the more complex concierge experiences that still require the human touch.
We already know that quality human management is directly related to rising market capitalization. Quality management by AI agents promises an even more positive effect: AI agents never forget and never leave, allowing management work to be scaled and shared.
But the benefits go beyond just capable AI agents. needs While it will require human oversight and feedback to be successful, it won’t just take away jobs — it will create new, and often better, jobs. I’ve seen front-line customer service reps take on the role of managing AI and gain a new sense of ownership over their companies.
In fact, managers who learn how to train AI agents make themselves indispensable: they learn to use tools that can increase the productivity of every other department in the company.
Related: Fearful yet useful — why do so many American workers shy away from AI?
A future where we are all managers
And this change isn’t just limited to certain job titles. From now on, almost everyone will be an AI manager. We will all have AI agents doing our jobs and helping us be more productive. This means that this mind shift I’m describing — thinking of AI agents as teachable, constantly evolving colleagues — will be discussed beyond the C-suite level.
As the new paradigm takes hold, agents will become just as intelligent as we collectively strive to make them.
It starts with showing your AI agents the same courtesy you would show to humans: understanding that everyone (and all bots) makes mistakes. Then, do what good managers have always done: coach, train, and remove obstacles. After all, your AI agents are learning machines, just waiting for the next lesson to soar again. And that’s where we come in.
