Most AI frustration comes from the same place: expecting an executive and getting an intern. A very fast intern, dropped into a system that was never ready for one.-
Earlier I wrote about the current state of AI adoption in 2025 and shared a case study on what it looks like when a company gets it right. This piece is the story version of the same lesson. It follows a composite company that did what a lot of teams are doing right now, treating AI like a magic bullet, and it shows what happens when you bring in the fastest intern you have ever hired without giving them a healthy system to live in.
AI Is Not Your New Boss. It's The Fastest Intern You Have Ever Hired
By the end of the demo, Mark was fired up.
The vendor had just shown his team this slick AI platform that could summarize everything, automate anything, and basically fix all the annoying parts of their operations.
He watched it pull data from different systems, write clean summaries, answer questions on the fly, and he started thinking, this is it. This is what finally takes the pressure off.
Fewer late nights. Fewer bottlenecks. Fewer long email chains where nobody is really sure what is going on.
In his head, it already felt like he had made a dream hire.
Fast forward six months and the story looks very different.
The AI system is technically running, but it is scattered across a bunch of half finished pilots. Some teams use it for certain things. Some avoid it completely. A few people tried it once, got a strange result, and never opened it again.
Nobody is sure which parts are official or when they are supposed to use it. There are no clear rules. There is no real owner.
It is not that the AI is terrible. It is that it seems to take every bit of confusion they already had and crank the volume up.
It does not feel like a magic bullet. It feels like they hired the fastest intern in the world and then dropped them into a broken system.
The Fastest Intern In The Building
The way a lot of leaders talk about AI right now, you might think they were bringing in a brand new executive.
You hear people say things like we want AI across everything we do. Or AI will help us make better decisions. Or AI will own this whole process.
The truth is a bit less glamorous.
AI does not join your company as a seasoned chief operating officer. It shows up more like a junior hire with super powers.
This new intern can read more in a day than your whole team can read in a month. It can draft decent versions of documents, emails, plans and project notes. It can follow structured instructions without getting bored or distracted. It never sleeps and never calls in sick.
But it still does what you tell it to do.
If your instructions are fuzzy, if your data is messy, if your process is not clear, that is exactly what it will follow.
That is what this moment in AI really feels like. The technology is powerful. The expectations are huge. And the gap between what people think they hired and what they actually brought in is where most of the frustration sits.
Dropping A Super Intern Into A Messy System
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Picture this for a second.
You hire a brand new junior person. On day one you drop them into a place where nobody writes anything down, everyone does the same task a different way, half the real decisions happen in side conversations, and nobody really knows who owns what.
Then you tell them, you are here to transform our operations.
That is what a lot of companies are doing with AI.
They plug advanced tools into workflows that have never been properly defined. They skip the boring work. They skip the part where you sit down and actually say this is the process, this is the handoff, this is what good looks like, this is where the data lives.
Instead they hope the AI will magically bring order from chaos.
What actually happens is simple.
Bad inputs turn into bad outputs at high speed. Inconsistent processes turn into inconsistent automation that nobody fully trusts. Hidden bottlenecks turn into high speed pile ups that are suddenly visible to everyone.
The old operational mess does not disappear. It becomes automated mess.
The problem is not that the AI is too smart or too dumb. The problem is that the environment it is dropped into was never healthy to begin with.
Promoting The Intern On Day One
There is another pattern that shows up all the time.
Someone in leadership gets excited about AI. Maybe they saw a great keynote, or a competitor made a big announcement, or a vendor put on a very convincing demo.
A committee forms. Consultants get involved. Pretty soon there is a multi year transformation plan on the table. It comes with roadmaps, work streams, pretty diagrams and big numbers.
On the surface it sounds impressive. Underneath, it is a little like promoting that new intern straight into an executive role before you have ever seen how they handle one real task.
By the time the big project is ready, the business has already shifted. Customer needs change. Internal systems change. Teams get reorganized. The huge plan no longer fits the way it did in the slide deck.
Front line staff look at the AI thing and think, this came from corporate, not from us. It does not feel like theirs.
There is pressure from the top to prove the investment was worth it, even if the use cases are not quite right. Because it takes so long to get anything live, no one can answer the simple question of what value did we actually get out of this.
Meanwhile, small real opportunities to win with AI sit untouched. Helping a team write better scopes faster. Turning rough notes into clean status reports. Summarizing client conversations and flagging follow ups. None of that sounds big enough for the big transformation story, so it gets ignored.
Most of the time, real success with AI does not start with a giant launch. It starts with one modest, well chosen assignment that the intern can actually win.
Giving The Intern A Job They Can Actually Succeed At
The organizations that are quietly doing well with AI do not always look flashy from outside. There are no fireworks. They just keep making progress.
They treat AI like a very capable junior teammate and they design the work with that in mind.
They pick jobs with clear edges. Help our team turn site notes into structured project scopes. Help summarize long email threads and pull out action items. Help draft the first version of a proposal so a human can refine it.
These are tasks where a good first draft saves real time, and people still bring their judgment at the end. The work has a clear start and a clear finish.
They also clean up the process before they invite AI into it. They decide what good looks like. They agree on the steps. They decide what they are going to track and where that information lives.
Then they plug the AI into that flow. Now the intern has a playbook, not just a pile of random requests.
They keep the first projects small enough that they can learn without causing major damage. One team. One workflow. One clear goal. Short feedback loops so they can say this part worked, this part did not, and here is what we are going to change.
It does not sound dramatic. It does not give you a big press release. But it creates steady, real improvements that people feel in the work.
The People Side You Cannot Skip
Even with solid tools and well defined tasks, there is still the human part.
If you brought in a new hire and never explained why they were there, what success looks like, or how their work helps the team, you would not expect them to be fully engaged.
Yet that is basically how a lot of staff experience AI.
They hear there is a new system and they should start using it. They hear it will save time. They hear the company cares a lot about this.
What they feel is something different.
Is this going to replace part of my job.
If it messes up, am I the one who will take the hit.
Is this just another tool I have to learn on top of everything else.
Nobody sits down and says here is how this will actually make your day better. Here is how it supports the parts of your role that matter. Here is what we will not use it for, here is how you can shape what it becomes.
So of course the adoption is shallow, people try the system, then drift back to what they know. The AI intern is technically on payroll, but often ignored.
Teams that move past this do something simple and human. They invite people into the process.
They ask front line staff where the real friction is. They involve them in choosing where the AI should help. They are honest about what the tech cannot do and what it will not be used for. They tie the tools to a better version of the job, not a smaller version of the job.
When people see that AI can help them handle more volume, make fewer mistakes, or serve customers better without stripping away their value, the mood shifts. Instead of bracing against the change, they start to shape it.
Governance Is Just Being A Good Manager
There is one last piece that decides whether the intern becomes an asset or a problem.. management.
If you had a very eager, very fast new team member and you let them work with no guidance, no standards, and no check ins, you would not get magic. You would get chaos and AI is no different.
Governance sounds heavy, but it is really just good management for AI systems.
Someone needs to decide which tools are approved and for what. Someone needs to care about data quality and documentation. Someone needs to own each AI assisted workflow and keep an eye on how it is performing. There needs to be a simple way to say this is working, this is not, and here is what we are changing.
In many companies this layer barely exists. Little AI experiments pop up all over the place, but there is no shared playbook. Some are helpful, some risky, and most of them are invisible outside the team that built them.
When you put real governance in place, AI does not get slower. It gets more trustworthy.
People know which tools they are actually supposed to use. Leaders know where the systems live and who owns them. Small wins do not stay trapped in one team, they can spread.
You would not bring a powerful new person into your team and refuse to assign them a manager. AI should not be treated any differently.
What This Really Says About Your Leadership
Underneath all the talk about models and tools and roadmaps, AI is asking leaders a quieter question. What kind of organization are you really running?
Are you willing to build the kind of environment where a capable junior teammate can thrive. Clear processes. Honest communication. Real ownership. Space for people to learn and improve. Accountability that is firm but fair.
Or are you hoping that a powerful tool will cover for years of messy habits and fuzzy decisions.
AI does not change your culture on its own. It simply reveals it.
If your default is to skip the boring work, the cracks will show quickly. If your instinct is to announce big things without doing the groundwork, the intern will magnify that. If you treat people like obstacles instead of partners, your AI project will feel exactly the same.
The flip side is also true.
If you are willing to slow down enough to give this intern a real job, a real manager, and a real place in the system, the payoff can be huge. You get more capacity without burning people out. You get better decisions without drowning in information. You get tools that actually feel like help, not surveillance.
That is the deeper point here. AI is not your new boss, and it will not become one. It is the fastest intern you will ever hire.
If you build the right environment around it, it will help your team do the best work of their careers. If you do not, it will just help you make the same mistakes faster and louder.
The technology does not decide which way that goes. Your leadership does.
Dan Stuebe is the Founder and CEO of Founder's Frame, where he leads as Chief AI Implementation Specialist. With a proven track record of scaling his own contracting firm from a one-man operation into a thriving general contracting company, Dan understands firsthand the challenges of running a business while staying competitive in evolving markets.