By Dominick Miserandino, CEO and Founder, RTM Nexus
One of the things I’ve learned from using AI extensively is that it’s remarkably good at doing exactly what it thinks you asked it to do.
Unfortunately, that isn’t always what you wanted.
I’ve compared AI to an overly enthusiastic intern who’s had too much coffee. It’s eager to help, willing to take on almost anything, and sometimes needs a little more direction than you initially expected.
The comparison isn’t about interns being incompetent. Quite the opposite. Anyone learning a new job needs clear expectations, feedback, and someone willing to explain how things work.
AI needs much of the same management.
And that’s where I think a lot of businesses are going to run into trouble.
Just because AI finished the job doesn’t mean the job is finished
I’ve been experimenting with AI agents that handle everything from database research to checking information and updating records.
Some of these systems are genuinely impressive. Work that previously required hours of repetitive effort can be completed much more quickly.
But speed has a way of making people less cautious.
Ask AI to research 100 companies, and it may return a beautiful spreadsheet with names, titles, websites, and background information.
Everything looks finished.
Then you discover that a few executives changed jobs, some information is outdated, and several records don’t belong in the database.
Now someone has to check the work.
That doesn’t mean AI failed. It means the company needs a process for determining whether the results are accurate.
And that process has a cost.
If AI saves four hours of research but requires three hours of verification, the savings are considerably less impressive than the original demonstration suggested.
Companies need to look at the entire process, including the time spent checking and correcting the results.
Give AI a job it can actually do
One approach I’ve found particularly effective is assigning AI very specific responsibilities.
I have agents that examine databases, others that research information, and others that handle updates.
Each has a defined purpose.
Picture an agent whose only job is to find customer records that are missing a phone number. That’s a clear assignment. You can see what it flagged, spot-check a sample, and know within an hour whether it’s working.
Narrow jobs are easy to check. Broad ones aren’t.
You don’t need one enormous AI system attempting to handle every aspect of your business.
Start with something small. Make sure it works. Then expand.
If an agent’s job is to find missing information, let it find missing information.
That doesn’t mean it should automatically rewrite existing records, delete contacts, or send communications without someone authorizing those decisions.
Those are separate responsibilities with different consequences.
Accountability doesn’t disappear with automation
Managing people requires more than assigning tasks. You need to understand what they’re doing.
Think about a store manager who has never worked a register, stocked a shelf, or processed a return. They can read every report, but they’ll miss why the line backs up every Saturday at noon or why returns spike the week after a promotion.
The people on the floor know. The manager is guessing.
The same principle applies to AI.
If you’re responsible for introducing AI into your organization, you should understand how it operates, where it makes mistakes, and how employees are supposed to handle those mistakes.
Otherwise, you’re simply handing your employees another system they have to manage while telling them it should make their jobs easier.
And if something goes wrong, the responsibility still belongs to management.
The economics have to make sense
I’m particularly interested in what AI costs relative to the work it actually accomplishes.
Businesses have a habit of focusing on the exciting part of new technology while overlooking the operating expenses.
AI has subscription fees, usage costs, integration requirements, and oversight needs.
There’s also the potential cost of mistakes.
An inaccurate research report might be inconvenient. An incorrect customer communication could damage a relationship. A pricing error across thousands of products could become expensive very quickly.
The more authority you give an automated system, the more important those distinctions become.
That’s why I prefer keeping records of what AI has done.
If I check a system later in the day, I want to know what it accomplished, what it changed, and what it couldn’t resolve.
That gives me something useful to manage.
Good technology still needs good management
I’ve been through enough technology transitions to recognize a recurring pattern.
Companies get excited about what a new tool promises, invest heavily, and sometimes forget to establish how they’re going to measure whether it’s working.
AI deserves the enthusiasm. I’m using it for things I wouldn’t have imagined automating just a few years ago.
But I also think executives need to spend more time understanding the technology themselves.
Give it a clear assignment. Establish boundaries. Review the results. Learn from the mistakes.
When something works, build on it.
And when it doesn’t, figure out why before expanding it across the organization.
AI may be able to do the work faster than anyone expected.
That doesn’t eliminate the need for someone to make sure the work was done correctly.
And ultimately, that’s still management’s job.
Dominick Miserandino is the CEO and Founder of RTM Nexus. He discussed AI, leadership, and managing technological change during his appearance on the Proteus Leader Show.