I Stopped Building AI Tools. I'm Hiring Them

AI I Stopped Building AI Tools. I'm Hiring Them. Geoffrey Doempke 22 Apr 2026 — 4 min read This is what changed.Three months ago I was building AI tools for myself. A month ago I was building them for my coworkers. Today I’m building them for my bots.Each phase felt obvious at the time, and each one was wrong in a way I couldn’t see until I moved to the next one. This is what the progression looked like.The world I was coming out of is the one every finance and data team still operates in. A non-technical manager wants a number. A technical person writes the query, builds the chart, exports the CSV, fields the follow-up. Every question hits a gatekeeper. Every urgent ask queues behind the last urgent ask. The bottleneck isn’t the data. It’s the person holding the key to it, and for a lot of years that person was me.Phase 1: Building for meThe first version of my AI workflow was me at a keyboard, typing at Claude, getting responses, deciding what to do with them. Every interaction flowed through me. I wrote the prompts. I evaluated the output. I decided what to run next.When something was missing, my instinct was let me give it another skill. An MCP server for QuickBooks. A tool for reading PDFs. A prompt for reconciling bank feeds. Every addition made the model more capable for me, specifically, sitting at my specific desk, running my specific workflow.This is where most of the industry is. Copilots, chat windows, tool-use frameworks, “give your AI a thousand skills.” It works. It’s also a dead end, because the person doing the work is still me.Phase 2: Building for my coworkersA few weeks in, my non-technical boss wanted to run a recurring financial report himself, the kind of thing he’d normally wait on me for. I’d already wired him up with the MCP servers I’d custom-written for our financial systems, so his laptop could talk to the database. What he was missing was the choreography: which queries, in what order, producing what output.So I wrote it once as a skill, checked it into a repo called finance-skills, and sent him a message that said, basically, first time: git clone …/finance-skills. Every time after that: cd finance-skills && claude, and paste this. The prompt told Claude to read the repo’s CLAUDE.md, run the right skill for the last three months, collect per-customer detail, produce a summary table with a totals row, and export everything to CSV. If a customer tripped a manual-review flag, the output explained why so he could eyeball those rows himself.That’s the entire handoff. A guy who does not write code, pulling a multi-month financial analysis by typing claude in a terminal and pasting one prompt.Pulling the report was only the start. In the old world, the analysis ended at the query or the BI dashboard. If he wanted to know “what does this look like if sales jump 10% next quarter?”, that was another ticket for me. Now he asked Claude the follow-up directly, got a modeled answer, asked another, asked another. I wasn’t the gatekeeper for any of it.It isn’t the end state, though. He still has to run the analysis. I still have to maintain the data underneath it.The AI had been democratized. I had not.Phase 3: Building for my botsThis phase is still in progress. What follows is the direction, not the finished thing.The shift was small and it didn’t feel like much when it happened. I stopped asking what can the AI do? and started asking what does this agent need in order to own its job?The trigger was simple. I get bored…

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