In AI meeting tools, experimentation matters more than automation

DECODED

AI-powered meeting tools are everywhere these days. They promise to take notes, highlight action items, and make meetings more efficient, without requiring you to scribble down details or stay fully engaged. At first glance, these tools seem like a dream come true for teams trying to stay organised and save time. But if you’re planning to use them or have already started, it’s worth pausing for a moment before handing over control to automation.

The reality is that these tools are far from perfect. They can be helpful, but they’re only as good as the structure you provide and the effort you invest in learning how to use them. You can’t just “set and forget”. Doing so will likely lead to frustration and wasted effort. The problem isn’t that AI is broken; it’s that teams often treat these tools like plug-and-play solutions instead of something that needs to be tested, adapted, and refined.

Clear processes

The ideal way to put these tools to use is by setting clear processes in place. Without that, you end up with incomplete summaries, missing context, or notes that no one trusts. That’s not a flaw in technology, but a flaw in how it’s being used. Just like in any system where you’re trying something new, the first few attempts may be messy. But rather than expecting perfection from the start, it’s far more effective to treat the tool as something that improves with feedback and practice.

One key mistake teams make is assuming that automating a task means it’s solved. Meeting notes aren’t just about capturing words, but they’re about interpreting meaning, assigning responsibility, and reinforcing decisions. GenAI tools struggle with nuance. If your team hasn’t built clear guidelines on what’s important, how decisions are made, or how follow-ups are tracked, AI will simply mirror the confusion.

You need to experiment with how you use these tools. Try them out in small groups first, see where they succeed, and where they fall short. Pay attention to the kinds of meetings where they’re most helpful. Is it in status update meetings where facts are clear, or brainstorming sessions where context and emotion matter more? Use this feedback to adjust your workflows. Treat errors not as failures, but as signals that the system needs more tuning.

Over-reliance

Another pitfall is over-reliance. Some teams assume that by simply recording everything and summarising it afterward, they’ve solved the problem of follow-through. But without clarity in communication, these summaries can become noise. Important points get buried, assumptions go unchallenged, and the real work stalls. Automation doesn’t replace judgment; it only supports it when used thoughtfully.

What’s more, tools that work well for one team may not suit another. Different teams have different styles of communication, decision-making processes, and levels of formality. This is why it’s essential to treat tools as adaptable, and not as one-size-fits-all solutions. Explore how your team functions, and tailor the tool to support those patterns rather than trying to force the team to conform to the tool’s limitations.

Approach these tools with a mindset that embraces trial and error. Test them in varied scenarios, ask for honest feedback, and be prepared to iterate. Over time, you’ll discover where the AI genuinely adds value and where it simply complicates the process.