---
title: "Comment by BridgeXAPI on \"AI Can Solve Every Problem in My Company. Right?\" | Hashnode"
description: "This is a really good take, especially the “solution-first vs problem-first” part. Something I’ve been running into though is that even when you start with a re"
url: "https://hashnode.com/posts/ai-can-solve-every-problem-in-my-company-right/69dd7cbf217f5dfcbd609f19/comment/69dfc2e746ad31000be8be28"
---

[Discussion](https://hashnode.com/posts/ai-can-solve-every-problem-in-my-company-right/69dd7cbf217f5dfcbd609f19)

Comment [AI Can Solve Every Problem in My Company. Right?](https://hashnode.com/posts/ai-can-solve-every-problem-in-my-company-right/69dd7cbf217f5dfcbd609f19)

[BridgeXAPI](https://hashnode.com/@bridgexapi)

[Apr 15](https://hashnode.com/posts/ai-can-solve-every-problem-in-my-company-right/69dd7cbf217f5dfcbd609f19/comment/69dfc2e746ad31000be8be28)

This is a really good take, especially the “solution-first vs problem-first” part.

Something I’ve been running into though is that even when you start with a real problem, there’s still another layer people underestimate: what actually happens after you “solve” it.

We hit this with messaging infrastructure. On paper the problem was clear, the implementation was correct, everything looked fine from the application side. But behavior still varied in ways we couldn’t explain at first.

That’s when it clicked that solving the problem at the interface level isn’t the same as understanding the execution behind it.

Feels like a lot of AI use cases will run into the same thing. You can build something that works in a controlled setup, but once it’s part of a real system, you’re dealing with hidden constraints, data quality issues, timing, external dependencies…

So even “boring AI” can get unpredictable if that layer isn’t visible.

Curious if you’ve seen that as well when moving from prototype to something closer to production.

[Frandi Tech](https://hashnode.com/@frandi-tech)

[Apr 15](https://hashnode.com/posts/ai-can-solve-every-problem-in-my-company-right/69dd7cbf217f5dfcbd609f19/comment/69dfc2e746ad31000be8be28#reply-69e00d18f95c47e87f5c62e8)

Great point, and yes — I've seen this play out more than once. What you're describing is actually an extension of the same root cause: we assume the solution is done once it's deployed.

But I think production is really where the learning starts. Moving something to production (infrastructure-wise) doesn't mean it's the final version — it just means it's ready to be observed.

That's why I try not to over-invest in the first attempt. Delivering a solution (regardless of the results) to a real problem is already the first win. AI solutions are no different, and honestly, they might require even more of this cycle because their behavior can be less deterministic than traditional code.

So the "layer that isn't visible" you mentioned — I'd argue it should actually be the goal when you go live. And when you finally realize that behavior varied once users start using your proposed solution, isn't that a layer starting to reveal itself? Tweak it, and be ready for the next one.