One thing I’ve seen a lot is how we love to debate whether Tool A is better than Tool B — especially when we already have Tool B in place. Or we ask if Tool A, which might be 5% better, is worth the switch. Yet, the problem we often like to ignore? We forget the most important but inconvenient part: the users and whether they’ll actually use it.
Even if a new tool is slightly better or cheaper, I always point to the adoption rate. Sure, replacing a tool with one that’s 5% better sounds great. But have you checked how many people are actually using the current tool? If only 10% of your team is on board, a 5% improvement for that 10% won’t improve your situation lot. On the other hand, increasing adoption from 10% to 50% means 5x the people are leveraging more efficiency — just by getting more people to use what’s already there.
Of course, if you can improve the tool and the adoption rate, that’s ideal! But in reality, you usually have to choose — time and budget don’t stretch forever. And even if you switch tools, don’t expect miracles if you haven’t thought about adoption as well.
But even if you do get users on board, there’s another problem we rarely talk about: the learning curve. Introducing a new tool —especially something like AI — comes with a cost.
The Cost of Change
What I mean is: Introducing a new tool — especially something like AI (which comes with token cost) — comes with a learning curve. You can’t just roll it out and expect everyone to be more efficient overnight. You have to train people. Show them how to use it properly, not just superficially. And that takes time — for the project team and for the users.
While people are learning, their efficiency drops. They’re figuring out the new tool, make mistakes, and maybe they are even reverting to old habits when things get tough. This is the inevitable dip — the price you pay for change! It’s not a minor hiccup; it’s a temporary slowdown that can last weeks or even months, depending on the tool.
Yet, this part is almost never mentioned when tools are sold. The pitch is always the same: “Buy this, and your team will be more efficient immediately.” But that’s not how it works. Some users will adopt quickly, sure, but most won’t. Not without practice. Not without time. Some even with reluctance! But certainly not without a temporary drop in productivity.
This is especially true for AI. You can burn through tokens like there’s no tomorrow, but if you don’t know how to use the tool efficiently, you’re just wasting resources. Efficiency with AI doesn’t come for free — it comes from practice. And practice takes time (and training). Time that users aren’t spending on their regular work.
Why We Resist Change
This is where we hit a (completely untechnical) wall: people.
Change is hard because it means rethinking how we work. For example, I’ve heard complaints like, “We’ve always written documentation this way, but AI needs it structured a bit differently?!” Yes, that means cleaning up, adapting, and — omg — changing habits, too.
People resist this. I’ve written before that I’m not a fan of Excel, but even small workflow adjustments can feel like a threat. Why? Because people don’t like change. And until recently, I thought this was just an IT problem — until I saw “Die unbequeme Wahrheit über Transformation” (The inconvenient truth about transformation) by Maja Göpel, a transformation scientist in Germany, where she’s talking about the climate crisis. Her point was that people don’t want to change, even when the stakes are high. And that struck me: The resistance we may see in IT projects isn’t unique to IT — it’s a human problem.
We see it proven in the news every day: Politicians propose changes (hopefully for the better), and the backlash is immediate. People need incentives, motivation, or even financial rewards to embrace / accept change. Otherwise, they object.
And if that’s true for society, why would it be any different in companies? Some employees are eager to adopt and optimize processes, but I dare say: they are the exception. Most people have their routines, their tools, and their way of doing things. Their job isn’t even to constantly reinvent themselves — it’s to get work done! And maybe even: If they’ve never been motivated (or given the time) to improve, why would they start now?
The Real Challenge
Once I realized this wasn’t just an enterprise problem but a human problem, it became clear why so many change initiatives struggle: The challenge isn’t the technology — it’s the people. And yes, you can force change, but that takes energy, resources, and often creates resistance. If you want real adoption, you need to convince, train, and support users.
That’s not a technical problem—it’s a people problem.
So, is Tool A better than Tool B? Maybe. But unless it’s significantly better, the real hurdle isn’t the tool — it’s the users. And if you’re not prepared to train them, support them, and accept the temporary dip in productivity, you’re not ready for the change. Because the tool isn’t the bottleneck.
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