“Use AI” is not a strategy

There are two finance books on my desk. I bought them six weeks ago. Neither one is open.

We had to assess whether to replace one of our finance tools with an AI-native version. To judge that, I had to understand what we were replacing. That's where I keep getting stuck.

The pressure moves faster than the thinking

A colleague told me a few weeks ago how investors had put it at another company:

Use AI. Find use cases. Personal agents in production by September. Or…

That exact phrasing is not everywhere yet, but the pressure underneath it is. Boards want movement. Leadership wants proof the company is not falling behind. The urgency is real. It is not crazy to look at what AI can already do and conclude your company should be moving faster.

But "use AI" is not a strategy. It is urgency without direction. A strategy says what should change and what should stay human. "Use AI" doesn't answer that. It moves the question to someone else. Every team is asked to find use cases and show progress.

I knew the numbers, not the work

I've sat close to Finance since I joined Aiven, mostly through the data around it. I knew enough gotchas in the revenue data to feel like I understood the work. That made me overconfident.

Looking back, I think I knew it wasn't enough. I was hoping it wouldn't matter.

The numbers are the surface. Underneath them is the work itself: what Finance needs to produce, and where the manual work still hides. Almost none of it sits in a dashboard. Most of it lives in handoffs and tribal knowledge.

Nobody has the whole picture. Every person you ask gives you a different version depending on which part of the process they touch. You assemble it person by person, exception by exception, and the full picture only exists in the overlap between conversations.

That's why the books are still on my desk. I bought them thinking I could catch up. Then I realized a finance textbook isn't going to teach me what our team actually needs to produce, or which parts of the current process should disappear.

The vendor came in for a day and asked how things work on our end. They showed us what the tool does. What none of us could answer was whether it actually fits the work we run, not in a few hours.

The gap didn't show up during the demo. It showed up when we started scoping the POC and had to think about requirements and success criteria. That's when we realized how much of the process we couldn't clearly describe.

The one that works

We already have an agent triaging data issues from our observability tool. I wrote the skills for it myself. Issue comes up, triage runs, it queries BigQuery, reads our dbt codebase, pulls PRs from GitHub, and figures out whether it's a platform issue or a data quality issue. Then it routes the issue to a data engineering agent or an analytics engineering agent. Before that, every issue sat in a queue until someone on my team had time to look.

I could describe that process in thirty seconds. I knew the routing logic because I have been doing it myself for years. Putting it in a skill was straightforward. The agent works because the process was already clear enough to teach.

Same company, different team. One process I could automate because my team owns it. The other I can't even scope properly because the knowledge isn't mine.

The hard part comes before the tool

When you understand what the work has to produce, you can stop treating the current process as the thing to automate. You know the output and where judgment still matters. You can see where an agent might fit and where a human still needs to stay.

The danger is that you can move forward without that understanding. Buy the tool, push the agent live, and you have automated a half-understood version of the work. That is worse than the old tool, because the old tool did not pretend.

"Use AI" sounds like a tools problem. Tools problems become vendor problems. By the time the vendor is in the room, the harder question has been skipped.

The pressure to use AI isn't going away. It shouldn't. But I'm starting to think the real work isn't picking the tool. It's becoming the person who understands the work well enough to know what should change.

For the triage agent, I was already that person.

For finance, I'm not there yet. The books on my desk aren't going to change that.

The next conversation is not with the vendor. It is with the people who know where the clean process breaks. If I cannot describe the work without the tool, I'm not ready to decide what it should replace.