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.