I met a technology professional at an airport not long ago. He runs an information technology (IT) services business. He manages infrastructure, handles support, and keeps his clients' systems running. He has been doing it for years. He has systems. He has a workflow. When something breaks, he knows how to fix it. When a client needs something, he knows how to deliver it.
When artificial intelligence (AI) came up in our conversation, he was direct about where he stood.
He said he already handles everything manually and it works fine.
He is the kind of AI skeptic small business owner rarely talked about publicly but privately representing more than any other category in the room. Not someone who is afraid of technology. Someone who has already mastered it.
I did not argue with him. He was right. It does work fine.
We exchanged contact information before we went our separate ways. The conversation is still open. Nothing has shifted yet.
But here is what I noticed in that conversation. His confidence in his existing systems was precisely the thing making AI invisible to him. Not ignorance. Not fear. Competence.
The business owners most likely to miss what AI can do are not the ones who do not understand technology. They are the ones who already do.
That sounds counterintuitive. It is worth spending some time on.
Technology familiarity creates a blind spot. When your current systems work, you stop looking for improvements. The question is not whether your system works. It is whether it works as well as it could.
Running your business on solid systems is an advantage. It just creates a blind spot most people never notice until someone points it out. Image created in Midjourney
Why Competence Creates a Blind Spot
Most conversations about AI skepticism frame it as a knowledge gap. The skeptic does not understand AI yet. Once they learn more, they will come around.
That framing is wrong for the kind of skeptic I am describing.
The technology professional I met at the airport does not have a knowledge gap. He has a working system. Client infrastructure managed manually. Tickets handled. Problems solved. Revenue coming in. He is not skeptical of AI because he does not understand technology. He is skeptical because the technology he already uses is doing the job.
This is what psychologists call a competence trap. When a system works well enough, the brain stops generating reasons to change it. The cost of the status quo becomes invisible because the status quo is not obviously broken.
The problem is that 'works fine' and 'works as well as it could' are not the same thing.
Managing client infrastructure manually works. It also means every ticket, every renewal, every routine check, and every configuration change is a task that lands in one person's queue. The business is limited not by skill or demand but by hours. That is the hidden cost of a manual system that works fine. It is not visible until you ask the right question.
The blind spot is not about AI. It is about the accepted cost of the current system. Every manual workflow has a cost built into it that the person running it has stopped noticing.
The Question That Changes the Conversation
When I work with a skeptical business owner, I do not make a case for AI. I ask one question.
What would have to change about AI for you to think it was worth your time?
That question does something that a broad argument for AI cannot do. It puts the skeptic in the position of expert rather than student. They are not being told why they are wrong. They are being asked to define their own conditions.
The answers are almost always specific.
It would have to write client emails that actually sound like me. It would have to handle the repetitive parts of my ticket queue without me having to supervise every step. It would have to produce something I could actually use, not something I have to spend an hour editing.
Those answers are not objections to AI. They are specifications. They describe exactly what the right application would need to do to earn a place in that person's workflow.
For the IT services provider I met, the answer to that question would almost certainly point to one of two things. Either client communication, the emails, proposals, and follow-ups that come with managing ongoing service relationships, or the administrative overhead of managing tickets, renewals, and routine checks that each require attention but none of which require his specific technical expertise to handle.
The question is not whether AI can help your business. The question is what would have to be true about AI for it to fit the way your business actually works. That answer is almost always more specific than 'it needs to work better.
What the Research Says About Technology-Confident Skeptics
The data on AI skepticism is more nuanced than most coverage suggests.
A 2026 Gallup poll found that Americans have grown more skeptical of AI even as they have become more familiar with the technology. That is a counterintuitive finding. More familiarity is producing more skepticism, not less.
The Thomson Reuters Future of Professionals 2026 report found that 91% of professionals say they have felt some degree of frustration with the gap between what AI promises and what it actually delivers. The frustration is not random. It is concentrated among professionals who had specific expectations and found that AI delivered something different or something generic.
Research from Workera published in 2026 makes the point plainly: AI skeptics are not anti-technology. They are reacting to predictable failure modes. Inconsistent outputs. Unclear accountability. Vague promises about productivity gains that do not translate to their specific context.
That description fits the IT services provider I met at the airport almost exactly. He is not opposed to technology. He has built a career on it. He is opposed to a technology that has not yet made a compelling case for his specific workflow.
That is a reasonable position. It is also a solvable one.
Skeptics convert when they see role-specific proof of AI impact in a workflow they recognize. Not a general productivity argument. A specific application in a context they actually work in.
Knowing your systems well enough to run a tight operation is exactly the preparation AI rewards most. Image created in Midjourney
What AI Actually Looks Like in an IT Services Business
The managed service provider (MSP) and IT services industry is one of the fastest-moving sectors when it comes to AI tool adoption in 2026. That is worth noting because it is the field where our technology professional works and the field where he feels AI has nothing to offer him.
According to Omdia's 2026 MSP Trends and Predictions report, leading managed service providers using AI are seeing 15 to 25% improvement in technician productivity and 40 to 70% reduction in ticket resolution times. The 2026 State of AI in the MSP Industry report from CloudRadial found that writing tasks like drafting client emails and summarizing client meetings have crossed into mainstream adoption at over 74% of MSPs.
Forester's 2026 tech trends report predicts that 40 to 60% of IT triage and repetitive fixes will be automated. Generative AI adoption among MSPs is expected to reach 78% by 2026.
None of that means he should adopt any of those tools. Adoption pressure is not the point.
The point is that his industry is changing around him. The question is not whether AI will eventually reach his workflow. It will. The question is whether he gets to choose how it enters on his own terms, or whether he finds out after the fact that his competitors have already made that choice.
That is not a threat. It is just the pattern that every technology cycle follows. The professionals who evaluate new tools on their own schedule, with their own criteria, consistently end up with better implementations than the ones who adopt late under pressure.
The most effective AI implementations among technology-savvy professionals are not the ones driven by enthusiasm. They are the ones driven by a specific question about a specific workflow, evaluated on the professional's own terms.
What Holding the Question Open Actually Means
The technology professional I met at the airport has my contact information. The conversation we started is still open.
I am not waiting for him to change his mind. I am waiting for the moment when his own answer to the question arrives.
That is how most skeptics actually move. Not through an argument they could not counter. Through a moment in their own work when the question they had already asked themselves, what would AI have to do to be worth my time, suddenly has a visible answer.
It might be the first month he loses a client because a competing IT services provider is resolving tickets 40% faster. It might be the week he spends three hours on routine client checks that should have taken thirty minutes. It might be a conversation with another IT professional who quietly mentions that one specific tool changed how they handle their ticket queue.
When that moment comes, having already had a real conversation about AI, rather than a sales pitch, is what determines whether the next step is productive or just another frustrating experience with a tool that was not matched to the work.
That is the only thing the 20-minute call is designed to do. Not convince a skeptic. Not pitch a product. Ask the question, listen to the answer, and identify whether there is a specific application worth exploring on terms that make sense for that business.
Sometimes the answer is not yet. That is a real answer. It is not a failure.
The goal is not to convert every skeptic. The goal is to make sure that when the moment arrives, the conversation that happens next is the right one.
Frequently Asked Questions
Is AI Actually Worth It for a Skeptical Small Business Owner Who Already Has a Working System?
It depends entirely on what the working system costs in time. Most manual systems that work fine have a hidden cost built into them: the time it takes to run them. If managing client tickets and infrastructure checks manually takes ten hours a week that could be reduced to three with the right tool, the system works fine but it is also limiting growth. AI does not replace technical skill. It handles the parts of the workflow that do not require it.
How is AI being used in IT services businesses right now?
The applications are specific and growing. On the administrative side, AI tools are drafting client communications, summarizing support tickets, and generating documentation from resolved issues. On the operational side, tools are automating ticket triage, routing, and routine configuration checks across multiple client accounts. The CloudRadial 2026 report found that documentation automation, specifically generating knowledge base articles from resolved tickets and creating runbooks, is one of the highest-opportunity areas with only 15 to 24% adoption today, meaning most IT services providers have not yet captured this time savings. The professionals seeing the most benefit are not adopting AI broadly. They are identifying one specific task where the manual time cost is highest and starting there.
What if AI tools produce outputs that do not meet my quality standards?
That is the most common frustration among technology-confident professionals who try AI. The first output is rarely production-ready. The gap between a generic AI output and something usable for a professional audience is real. The professionals who get past this point are the ones who invest enough time in a single tool to learn how to give it the context it needs to produce something specific. That learning curve is real. It is also much shorter than most skeptics expect once the right tool is matched to the right task.
Will AI eventually replace IT services providers?
The evidence from 2026 suggests a more nuanced picture than replacement. Managed service providers using AI are not eliminating technicians. According to the 2026 MSP industry research, top-performing providers see AI as a productivity enhancer rather than a headcount reduction tool. The IT professional who understands both client infrastructure and how AI tools work in that context becomes more valuable, not less. Forester predicts AI will automate 40 to 60% of IT triage and repetitive fixes, freeing technicians to move toward higher-value consulting services. The risk is not replacement. The risk is being outpaced by a competitor who resolves tickets faster, documents better, and onboards clients more efficiently because they found one specific AI application that fits their workflow.
What is the right first step for a skeptic who is open to one conversation?
Answer one question honestly before the conversation starts. What would have to change about AI for you to think it was worth your time? Write that answer down. Bring it to the conversation. That answer tells both of us more about where the right starting point is than any tool demonstration or productivity statistic could.
The Conversation Is Still Open
The technology professional I met at the airport is not wrong to be skeptical. He has a working system. He has built a career on technical competence. He has seen technology trends come and go, and he has outlasted most of them by doing his work well.
What would have to change about AI for it to be worth his time is still his question to answer. I did not answer it for him at the airport. I will not answer it in this post.
But the question is worth asking. Because the answer is almost always more specific than the general skepticism that surrounds it. And specific answers have specific solutions.
If the manual workflow he has built is genuinely as efficient as it could be, that is worth knowing. It means he can stop reading posts like this one and get back to work.
If it is not, that is also worth knowing. It means there is one hour somewhere in his week that does not have to cost what it currently costs.
That is the only thing worth finding out.
If you have been skeptical about AI and you have a specific answer to the question of what would have to change for it to be worth your time, that answer is worth one 20-minute conversation. Not a pitch. A real look at whether the application exists and whether it fits your workflow.
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What Would Have to Change for AI to Be Worth Your Time?
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Michael Carmine, Ed.D., MBA is the Founder and Chief Executive Officer of AI Educational Solutions (AIES), a boutique AI consulting and training firm based in Chapin, South Carolina, serving small and medium businesses across the Midlands and nationally. He holds a doctorate in Curriculum and Instruction with a specialty in Instructional Design and Technology, and has trained over seven hundred professionals across the United States, South Korea, and Qatar. Most AI consultants come from technology. Michael comes from education, and that difference shapes every tool he builds.
Meet Michael, Founder & CEO of AI Educational Solutions, LLC
Passionate About Making AI Simple and Useful
Michael started AI Educational Solutions, LLC after noticing a major gap: many small businesses and educators were investing in AI training that was too technical, too generic, or failed to deliver real-world results. He believes organizations should never waste time, money, or momentum on solutions that look impressive in theory but fail in practice. When companies invest in AI, they should walk away with operational clarity, improved efficiency, and a competitive advantage. That standard is non-negotiable.
AI Educational Solutions helps by assessing your needs, suggesting the right tools, and providing hands-on training so you leave feeling confident in using AI effectively.
