A new AI coding tool comes out. It looks better than the one your team has. Someone on the team is already asking if you can switch.
Before signing anything, it is worth asking a harder question: is the current tool actually the problem, or is adoption?
The tempting shortcut
Switching tools feels like progress. It is a concrete action, it comes with a launch moment, and it lets everyone believe the next rollout will go better than the last one.
It is also, often, a way to avoid diagnosing what actually went wrong the first time. If adoption was concentrated in a few engineers with your current tool, a new tool will not automatically fix that. The same barriers, no shared workflow, no defined use cases, no follow-through after the announcement, will likely show up again.
What to check before you buy
How deep is current adoption, really? Not license activation. Actual workflow usage. If most of the team never developed a real habit with the current tool, that is worth understanding before assuming a different tool would fare better under the same conditions.
What specifically is the complaint? Get concrete. "It's slower" or "the other one seems better" are not enough. Ask what tasks people tried, what happened, and what they expected instead. Specific, repeated complaints about a particular capability gap are meaningful. Vague dissatisfaction from people who never built a real workflow is a different problem entirely.
Is this a capability gap or a workflow gap? Some tools genuinely are better at specific things: certain codebases, certain languages, certain agent behaviors. If your team hit a real capability wall on tasks that matter to your stack, that is a legitimate reason to look elsewhere. If the team just never got a habit going, a new tool inherits the same risk.
What would need to be true for the new tool to actually get adopted? If you cannot answer this clearly, that is a sign the switch is being driven by tool appeal rather than a plan for making it stick.
What is the real cost of restarting? Switching tools resets the adoption clock. Whatever partial habits and knowledge exist around the current tool, even imperfect ones, get discarded. The team starts over, and if the underlying rollout problems were never fixed, you are likely to land in the same place a few months later with a different tool name.
When switching is actually the right call
None of this means never switch tools. Sometimes the capability gap is real and worth the cost of restarting adoption. The point is to make that decision based on a real assessment, not on tool fatigue or a demo that looked more impressive than your team's day-to-day experience with the current one.
A good signal that switching is the right call: your team has real, specific workflows built around the current tool, has hit a genuine capability ceiling doing those specific things, and a concrete case exists for why the new tool removes that ceiling.
A bad signal: adoption never got past a couple of engineers, and the hope is that a shinier tool will somehow solve a problem that was never really about the tool.
What good looks like
A team that assesses before buying can explain, specifically, what is not working with the current setup and why a new tool would fix that particular gap rather than just feeling newer. If your honest answer is "we're not sure, it just seems like it might work better," that is worth sitting with before the next purchase.
If you want a clear-eyed assessment of what's actually happening with your current AI adoption before you buy anything else, book a call. If I'm not the right person, I'll say so.