AI knows the next best action. That's the problem.
On being confidently wrong with speed.
For a long time, one of the hardest questions for any team drowning in data was: “So what?” You’ve got the signal, but what do you actually do with it?
That question just got a lot easier to answer. Point a decent AI model at a pile of signals, and it will spot the pattern and hand you the next best step: reach out to this account, nudge this user, prioritize that one. It’s fast, confident, and a lot of the time it’s actually useful.
But the “so what?” part was the hard part for a reason.
It’s the part where judgment lives, and if we’re not careful, we’re about to hand it to something that doesn’t know what’s actually behind the number.
Think about one of your accounts that’s super active. They’re busy. They’re logging in all the time. You see usage spikes constantly. Feed that into an AI model, and it’ll see it and say, “This one’s hot. Reach out right now.” Reality might be different. Maybe it is, or maybe someone set up an automation last week, and it’s a quiet cron job cranking through tasks and making a dead quiet account look busy. You get the same signal, but a completely different ”now what?”
In this example, it could either be a buying moment or a red herring.
Now add proactive agents to the mix, which is where a lot of tools and a lot of people, including me, are heading. More and more of the data that we see could be misleading. We can’t tell the difference between signals and false signals and noise. There’s a problem.
If the answer is to put AI on top of those before we understand the context and the meaning, we risk exacerbating the problem.
Ask AI a question, and it will give you an answer. Whether or not it’s the right answer is still hard to tell.
It’s getting very easy to collect data, interpret it, build on it, make decisions faster and faster. But the understanding that should go into all of this is getting abstracted away. If you did not build the dashboard, the metric, or the report, and you do not know what is feeding it or what it is really measuring, it is hard to know how to sense-check the recommendation you are handed. AI will give you an answer, and that answer arrives quickly. It can even arrive at scale.
For years, the skills we trained were signal versus noise, wheat from chaff. That still matters, but there’s a newer muscle we need to build. I think it’s one that will separate teams, products, and businesses.
Understanding what a signal is, what it means, what’s behind it, and what it implies for the decision in front of you is not a dashboard skill. It’s a decision-making skill, and AI doesn’t have your context.
I am certainly not anti-AI. Quite the opposite. It’s still important to hold onto some things. Closing the gap between getting a signal and taking action is the whole game, and AI is the best tool we’ve ever had for it. Still, speed toward the wrong action isn’t progress. A shorter signal-to-action loop aimed at a misread signal just gets you to the wrong place a lot faster.
If you’re leaning on AI to tell you what to do next, good. You should be. Don’t forget to understand what’s behind the signal: what’s in it and what’s not. Be very clear about what decision the data is actually supporting. The teams that win won’t be the ones that act on signals the fastest. They’ll be the ones who understand what the signals mean when they decide to act.
AI will give you the next best action if you ask, but whether it’s actually the best one is still on you to decide.
🚀,
Peter