If AI wants to hang out in the break room with the rest of us, it’s only fair that it faces the time-honored ritual of corporate life: the dreaded performance review.
I know, it’s a bit of a letdown. We dreamed up artificial intelligence to free us from the drudgery of repetitive work, make smarter decisions, and maybe even give us a shot at escaping endless status meetings. Yet, despite all that progress, we’ve managed to drag the annual performance review right along with us into the future. Some traditions are just too stubborn to die.
In earlier posts, I made the case that AI deserves the full employee treatment: give it a job title, a spot on the org chart, the right access, and yes, even a manager to keep it in line. Once you’ve done all that, you’re left with the classic question every manager eventually asks: Is this thing actually any good at its job, or is it just really good at looking busy?
That question is more important than it sounds. AI projects usually kick off with a lot of excitement and a few jaw-dropping demos. Someone shows off a model that can summarize a 50-page report in seconds, write an email that actually sounds human, or dig up answers from the company archives faster than you can say ‘search function.’ Everyone is impressed, pilots are launched, licenses are bought, a steering committee springs into existence, and before you know it, there’s a shiny new dashboard to admire.
But eventually, someone has to ask the awkward question: Is this AI actually delivering real, measurable value? Not just whether people enjoy playing with it, or if the demo wowed the crowd, or if leadership can brag about being ‘AI-enabled’ at the next conference. The real question is whether the AI is actually doing the job it was hired for, or if it’s just another shiny object in the tech toolbox.
That, my friends, is what we in the business call a performance-management problem.
For human employees, performance conversations usually begin with expectations. What is this person responsible for? What does good performance look like? What outcomes should they produce? If an employee is hired as a security analyst, we do not measure success by how many PowerPoint slides they create unless something has gone terribly wrong with the job description.
We should evaluate AI the same way. If an AI is assigned to triage security alerts, measure its performance against security-triage outcomes. Does it reduce the amount of time analysts spend reviewing low-value alerts? Does it identify pertinent context accurately? Does it escalate the right events? Does it miss important events? How often does a human analyst have to correct its conclusions?
If the AI is a customer-support assistant, the questions are different. Does it reduce response time? Are its answers accurate? Does it resolve routine requests without creating additional work? Does it know when to escalate to a person? Are customers more satisfied, less satisfied, or confused in a new and technologically advanced way?
The job itself should decide which yardstick we use to measure success.
That might sound obvious, but it’s easy to forget when you’re dealing with AI, especially the kind that spits out polished, professional-looking results. A well-written paragraph feels valuable, a slick summary seems smart, and a tidy answer with bullet items inspires confidence. But here’s the catch: just because something looks good doesn’t mean it’s actually doing the job right. Formatting is not the same as performance—no matter how many bullet points you use.
An AI can crank out a beautifully written answer that’s completely wrong. It might save you ten minutes drafting a report, only to cost you an hour double-checking every sentence. It can close support tickets at lightning speed, but if half of them have to be reopened, that’s not exactly a win. Sometimes, it just shifts the work from one team to another, like a digital game of hot potato.
If we focus on the wrong metrics, AI can look like a superstar while quietly making the organization less efficient. To be fair, humans have been gaming the numbers for years, so at least AI is joining a well-established tradition.
The first category I would measure is accuracy. How often is the AI correct when correctness can be objectively determined? That may mean comparing AI recommendations to analyst decisions, checking extracted contract language against the source, validating generated code, or reviewing whether customer answers match approved policy.
Accuracy alone is not enough, though. An AI that is 95 percent accurate sounds impressive until you learn that the remaining 5 percent involves payroll, safety systems, regulatory filings, or disabling customer accounts. Risk changes the permissible error rate.
The second measure should be correction rate. How often does a human have to modify what the AI produced before it can be used? This is one of the most useful measures because it exposes hidden work. Imagine an AI drafts 100 customer responses and employees have to rewrite 60 of them significantly. Technically, the AI generated 100 responses. A dashboard may celebrate that fact. Operationally, we may have built a very sophisticated first-draft machine that creates more review work than expected.
That might still be useful, but we should be clear about what value we’re actually getting for our investment.
Time saved is another obvious metric, but let’s be honest about it. If an AI chops a forty-minute task down to ten, that’s a real win. If it gets the task done in five minutes but you spend twenty minutes checking its work, the improvement is a little less impressive—though the marketing team might still try to spin it as a breakthrough.
It’s also worth remembering that there’s a big difference between making one person more productive and making the whole organization more productive. AI might save time for one employee, but if it creates extra work for someone else, we’re just moving the bottleneck around. For example, a developer might crank out code faster with AI’s help, but if the security team has to spend twice as long reviewing it for bugs, we haven’t really moved forward. Similarly, a support AI might shorten calls, but if it just means more escalations for another team, the net gain could be zero.
So, when we measure performance, we need to look at the whole process—not just the part where the AI swoops in and looks impressive.
Cost should be part of the review too. AI isn’t free, even if the chat window looks friendly and inviting. There are license fees, API charges, infrastructure bills, data processing costs, integration headaches, monitoring, support, security controls, and let’s not forget the time your team spends double-checking what the AI spits out.
Sometimes, the math is simple: the AI saves 1,000 hours of staff time and costs less than the people it replaces. Easy win. Other times, the AI saves 1,000 hours but eats up several hundred hours in review, racks up a hefty infrastructure bill, and brings in a parade of consultants who say ‘AI transformation journey’ so often you start to wonder if the buzzword is charging you.
That kind of math deserves a closer look before we start handing out trophies.
Security performance belongs in the evaluation too. If we are treating AI as an identity, then a good performance review should include whether that identity stayed within its assigned role. Did the AI attempt to access data it did not need? Did it retrieve sensitive information unnecessarily? Did it trigger actions outside its scope? Did users find ways to manipulate it into performing unauthorized tasks? Did it create new data exposures?
An AI that saves everyone twenty minutes a day but constantly breaks data-handling rules isn’t a star performer. It’s basically the digital version of that employee who crushes their sales targets but can’t stop clicking on phishing emails.
Results matter. How those results are achieved also matters.
Performance reviews should also look at how the AI handles escalation. I’ve said before that AI should be allowed to admit when it doesn’t know something and should know when to call in a human. If every request ends with ‘Please contact an administrator,’ congratulations, you’ve built a very pricey FAQ page. On the flip side, if the AI never escalates anything—even when it’s clearly out of its depth—that’s a different kind of headache.
The goal isn’t to eliminate escalation entirely. The goal is to make sure the AI knows when to ask for help—just like any good employee.
A good employee knows when to handle something solo and when to call for backup. AI should be able to do the same.
There’s another performance measure that can be a little awkward: adoption. If nobody is using the AI, that tells you something. Maybe the training was confusing, maybe people don’t know what the AI is supposed to do, maybe the interface is a pain, or maybe—just maybe—the AI isn’t actually solving a real problem.
Organizations should resist the urge to blame users right away. If people keep going back to the old way instead of using the shiny new AI, it’s worth asking why. Maybe the old process is just faster. Maybe people don’t trust the AI’s answers. Maybe checking the AI’s work takes too long. Or maybe the AI was built to solve the problem leadership thought existed, not the one employees actually deal with every day.
Every manager has met that employee who looks perfect on paper but just doesn’t quite deliver in real life. AI can have the same issue—though it usually comes with much better branding and a slicker logo.
Performance reviews also create a mechanism for changing authority. If an AI consistently performs well, it may be reasonable to expand its responsibilities. Perhaps an AI that initially only recommends actions can be allowed to prepare them. Later, it may be trusted to execute a narrow category of low-risk actions automatically.
That’s basically the AI version of getting a promotion—minus the awkward cake in the break room.
But promotions should be earned. Just because the technology can do more doesn’t mean it should be given more responsibility. ‘The new version supports autonomous workflows’ might sound impressive in a product demo, but it’s not a reason to hand over the keys to the kingdom.
The reverse is true as well: AI can be demoted. If its accuracy drops, you can dial back its permissions. If a model update makes it act strangely, you can take away its autonomy until things are sorted out. If it starts making questionable decisions, it can go from acting on its own to just making recommendations while you figure out what went wrong.
There’s no shame in that. We do it with technology all the time—we just dress it up with phrases like ’temporarily restricting functionality’ because ‘we demoted the chatbot’ doesn’t look great in a change-management ticket.
Performance reviews also force organizations to confront who is responsible for measuring results. The vendor should not be the only source of performance information. A product dashboard showing millions of AI interactions may be accurate and still tell you very little about whether those interactions created value.
The organization needs its own measures tied to its own goals. That means the business owner, technical owner, security team, and sometimes risk or compliance functions may all contribute to the evaluation. The business measures the outcome. Technology measures reliability. Security measures behavior and control effectiveness. Risk functions consider whether the remaining risk is acceptable.
Together, those measures answer a much more useful question than “How many prompts did we send last month?” They answer, “Is this AI doing the job we hired it to do?”
Eventually, organizations might even roll out official scorecards for their AI ’employees.’ I can already picture the spreadsheet: Accuracy, 94 percent. Time savings, 27 percent. Escalation quality, good. Security exceptions, two. Cost per transaction, trending down. Ability to complete mandatory cybersecurity training: still outperforming a few humans I know.
Who knows, maybe we’ll even ask AI to fill out a self-evaluation form one day.
“I believe I exceeded expectations this year by generating 4,200 reports, decreasing average response time by 37 percent, and only hallucinating an acquisition twice.”
At the very least, annual reviews might finally get a little more entertaining.
Jokes aside, performance management is a serious part of AI governance. Trust in AI should be earned, not handed out just because it’s shiny, the vendor is big, or the executives are excited. We should trust AI only as much as its actual performance deserves.
That means tracking outcomes, mistakes, costs, security behavior, escalation, and how often humans have to step in. It means checking if the AI is actually doing the job it was hired for. If it’s performing well, give it more responsibility. If not, scale it back—just like you would with any employee.
That’s exactly how mature organizations manage their people—and now, their digital coworkers too.
There’s one catch, though: performance reviews only work if you know which worker actually did the work. If ten AI agents are sharing the same login, or if everything just gets logged as ‘automation,’ it’s pretty tough to figure out which AI deserves a gold star—or a stern talking-to.
Before we can give an AI a fair review, we need to know exactly which one we’re dealing with.
It needs a badge number.
And that is where we go next.