By Dominick Miserandino, CEO and Founder, RTM Nexus
There’s a tendency in business to take a promising technology and immediately ask how it can transform the entire company.
AI has become the latest example.
Executives want systems that can manage customer relationships, handle communications, produce reports, research competitors, and somehow tie everything together into one intelligent operation.
I understand the appeal. If you’re spending money on technology, you want meaningful results.
But I’ve found that a much more practical approach is to give AI a small job and make sure it can actually do that job correctly.
Then give another agent another job.
I’ve been working with multiple AI agents that perform separate functions. One might examine a database for missing information. Another researches those records. A third might update the database after the information has been checked.
Each has a defined responsibility.
It sounds almost ridiculously simple, but that’s precisely why it works.
The butter robot approach
During a recent conversation on the Proteus Leader Show, I brought up an episode of Rick and Morty featuring a robot created for the sole purpose of passing butter.
The robot asks what its purpose is, and the answer is simply to pass the butter.
There’s a useful lesson for businesses in that example.
Not every piece of technology needs to be sophisticated enough to run the organization.
Sometimes you need a system that passes the butter.
Think about a retailer managing thousands of products across multiple locations.
One AI process could check whether inventory information is accurate. Another could identify products with unusual sales patterns. A third could flag items where supplier costs have changed but retail prices haven’t been reviewed.
Those are three distinct jobs with measurable results.
You can check whether each process is working. You can determine how much time it saves. You can calculate whether the effort is worth the cost.
Now imagine asking one enormous system to manage all those functions, make purchasing decisions, change prices, and communicate with suppliers.
You’ve added operational complexity, financial exposure, and multiple opportunities for errors to spread.
That may eventually be appropriate for certain businesses. But there’s little reason to start there.
Small jobs make financial sense
One of the problems with large technology projects is that companies often struggle to calculate what they’re actually getting for the money.
A vendor promises efficiency improvements, and management translates that into projected labor savings.
But the technology itself has costs.
There’s implementation, maintenance, software usage, quality control, and the time employees spend correcting mistakes.
If a system saves an employee five hours a week but creates three hours of additional review work, the financial benefit is considerably smaller than the original presentation suggested.
And if the errors involve customer orders, pricing, or financial records, the consequences can be substantially greater.
Small, specialized AI processes make it easier to measure the actual benefit.
Did the system identify 200 inaccurate records? How many were legitimate? How long would the same job have taken an employee? How much did it cost to run and review?
Those are numbers a business can work with.
AI still needs management
Even a small agent is willing to do an enormous amount of work. It also needs clear instructions and someone reviewing the results.
An agent checking a database needs to know which records it can change, what constitutes an error, and when something requires human approval.
It should maintain a record of what it did.
If I return later in the day, I want to know what changed, what succeeded, and what needs attention.
Without that accountability, automation can create problems that are difficult to trace.
Build on what works
Businesses shouldn’t confuse the size of an AI initiative with its value.
A small process that reliably saves employees several hours each week may be considerably more useful than an elaborate platform nobody fully understands.
Once the smaller process works, expand it. Connect it to another process where appropriate. Learn where the boundaries should be.
You may eventually build something much larger.
But by then you’ll understand the individual components, their costs, and their limitations.
I’ve seen enough technology cycles to know that companies have a habit of buying into the grand vision before understanding the operational details.
AI deserves investment, but it also deserves the same financial discipline applied to any other business decision.
If the technology can’t reliably pass the butter, I’m not sure why we’d ask it to run the kitchen.
I discussed the butter robot example and my experience operating multiple AI agents on the Proteus Leader Show.