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Tal Elor

AI won’t magically make better decisions for us.

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But it will raise the bar on what “good decision-making” even means.

Faster insights will stop being impressive - they’ll become expected.
Broader analysis won’t be a differentiator - it’ll be table stakes.
Deeper visibility won’t be optional - it’ll be assumed.

And once that happens, a lot of traditional discovery work will start to feel outdated. Not because it was wrong, but because it was designed for a world where you could afford to be slow, partial, and reactive. That world is gone.

The real shift isn’t that AI improves discovery -it’s that it exposes how much of discovery today is still fragmented, episodic, and disconnected from actual decisions.

That’s why we built Athena -not to “add AI to discovery,” but to move toward a model where discovery is continuous, adaptive, and directly tied to what teams actually do next.

Let’s chat in the comments - how do you make sure you're building value, not just noise?


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Fotso Tobou Achille
This makes a lot of sense from a builder's point of view. My problem was never finding ideas — it was that I studied my users and my market once, at the start, and then kept building for months without checking if things had changed. So I was making choices based on old information. The way I understand Athena: it doesn't decide for you. It just keeps your knowledge about your users fresh and connected to your decisions, all the time — so you stop building on outdated facts. That's the real value: not more information, but information that's still true at the moment you actually decide something.
Hossein Yazdi

I think that's the key point. AI can give faster insights and more data, but it still can't tell us what actually matters for our users.

For me, the biggest challenge is filtering signal from noise. It's very easy today to collect feedback, analytics, and AI-generated suggestions, but turning all that into the right product decision is still the hard part 😅

Tina Chhabra

if you stripped the word AI off it, would people still want it is basically the same test I apply to every AI tool I evaluate. the products that actually stick are the ones solving the decision problem not just the data problem. we already have more data than we know what to do with. what's missing is the part where it turns into something you can act on before the moment passes