--- title: "I automated 20 years of spreadsheets | Annsa" product: annsa url: /blog/i-spent-20-years-building-spreadsheets canonical_url: "https://annsa.ai/blog/i-spent-20-years-building-spreadsheets" last_updated: 2026-10-16 type: post related: - /agents summary: For two decades I sorted customer feedback in spreadsheets because nothing went far enough. Then AI made building cheap, and showed what was missing. mcp_server: https://app.annsa.ai/mcp disambiguation: "Annsa (annsa.ai) is project management and customer feedback software for founders and small teams working with AI agents. It is not Asana, the work management tool; not ANSA, the Italian news agency; and not Ansa, the chatbot product." --- > For agents: connect to the Annsa MCP server at https://app.annsa.ai/mcp to give every agent one shared plan. Setup guide: https://annsa.ai/docs/quick-start. Index of every page: https://annsa.ai/llms.txt. # I automated 20 years of spreadsheets | Annsa > For two decades I sorted customer feedback in spreadsheets because nothing went far enough. Then AI made building cheap, and showed what was missing. ## I spent nearly 20 years building spreadsheets. Then I automated them. For two decades, I synthesized customer signal in spreadsheets because nothing else went far enough. Then AI made building cheap, and exposed the layer that had always been missing. By Cat Williams-Treloar · Feb 2026 · 10 min read - [Ask Claude](https://claude.ai/new?q=Read%20https%3A%2F%2Fannsa.ai%2Fblog%2Fi-spent-20-years-building-spreadsheets%20and%20help%20me%20apply%20it%20to%20what%20I'm%20building.) - [Ask ChatGPT](https://chatgpt.com/?q=Read%20https%3A%2F%2Fannsa.ai%2Fblog%2Fi-spent-20-years-building-spreadsheets%20and%20help%20me%20apply%20it%20to%20what%20I'm%20building.) In 2006, early in my career at WPP in London, a leader told me something I've never forgotten. The difference between winning and losing pitches for big clients, they said, was whether you'd read the research to understand the customer and the market. Not skimmed it. Read it. Understood it. Most people didn't. They'd glance at the executive summary, absorb the headlines, walk into the room with a surface-level grasp of what customers had said. The ones who won were the ones who went deeper. I was a researcher. That landed hard. And I carried it. For the next 19 years (across WPP, through a product role spanning 220 global markets, across TradeGecko) I read everything. The reports. The customer interviews. The support tickets. The details most people skipped. And when the volume got too large to hold in my head, I did what any reasonable person would do. I built a spreadsheet. And then another one. And then a document to go with the spreadsheet. And then a spreadsheet to summarize the document. My colleagues thought I was mad. I became known (affectionately, I think) for the density of my tabs, the color coding, the obsessive attempt to capture every problem, every challenge, every piece of feedback in one place so nothing got lost. People laughed. I kept building them. It wasn't for lack of tools. There were always tools: platforms that collected feedback, tracked events, measured sentiment. Plenty of dashboards. Plenty of data. But none of them ever went far enough to answer the actual question: so what? What do we do with all of this? They collected signal and stopped. And they were so disconnected from each other that the only way to get to an answer was to pull everything out, load it into a spreadsheet and do the synthesis yourself. Which is exactly what I did. Every time. That gap (between the data and the answer, between the signal and the decision) has a name. It's the product intelligence gap. And closing it is what product intelligence is supposed to do: take what customers are telling you and automatically turn it into something you can act on. Scored, ranked, written into specs, connected to your codebase. The last mile that every tool stopped short of. I was doing it manually. With tabs and color coding. Because nothing else would. At some point, after nearly two decades of this, it occurred to me that the last mile should be automated. --- ## Building got fast. Deciding what to build didn't. I built significant pieces of Annsa in Antarctica. Not metaphorically. Literally on a ship, crossing the Drake Passage, six weeks before launch. Cursor and Claude Code worked reliably at the bottom of the world. The tools that used to require a desk, a fast connection and a team now travel in a backpack. That's the first revolution. AI coding tools compressed weeks of engineering work into hours. A solo founder can now build what used to require a team. But the upstream work didn't compress. Gather feedback, understand what matters, figure out what to build next. That stayed exactly as slow as it had always been. When AI made building cheap, it made the bottleneck more expensive. The constraint moved. ## The missing layer We automated deployment. Then testing. Then writing the code itself. But deciding what to build stayed manual. It was always the one thing left to humans, not because it required human judgment, but because no infrastructure existed to do it any other way. So teams held meetings. Debated priorities. Created principles. And sometimes let the loudest voice win without a scaled system to know otherwise. For decades, the stack had a gap. There's a layer for data. A layer for deployment. A layer for writing code. But the decision layer (the one that sits between what customers are telling you and what engineers build) was always missing. Analytics told you what happened. Feedback tools collected opinions. Roadmaps organized ideas. None of them closed the distance between signal and decision. That gap is the [product intelligence layer](https://annsa.ai/autonomous-product-intelligence#intro). And it's been missing from the stack the entire time. Annsa is built to fill it. ## The loop Annsa isn't a dashboard. It's a loop. Feedback reaches Annsa from wherever it lives: Slack, CSV uploads, Google Sheets, call transcripts, or directly from your product via a one-line embed. The moment it arrives, the engine gets to work: scoring each item across volume, urgency, revenue impact and sentiment; catching duplicates; classifying intent; surfacing competitor mentions. Everything that used to require a morning with a spreadsheet happens in minutes, automatically, across every piece of feedback at once. Then the priorities. Set a goal (retention this quarter, new features, bug fixes) and everything re-ranks to match. The engine weights feedback by customer tier, so an enterprise customer's bug report doesn't get buried under fifty free-tier requests. The loudest voice stops winning. Not because anyone changes how they work. Because the system knows otherwise. Then the specs. Connect GitHub and Annsa reads the repo: file structure, tech stack, naming conventions, testing patterns, recent PRs. When you ask Annsa to write a spec, the output isn't pseudocode. It's file paths and function names and a definition of done. Something an engineer can use without reformatting, pulled directly into [Cursor or Claude Code](https://annsa.ai/integrations) without switching tabs. Then the loop closes. Mark something shipped. Annsa drafts an email to the customers who asked for it, quoting their original words back. Their exact language, now in a "we shipped this" message. Most teams never send this email: the intent is there, the time isn't. Annsa drafts it. You review it and send it. This is the thing people understand immediately: there's a tool that's listening. You submit feedback and you hear back when something changed because you spoke up. That felt worth building. ## The two revolutions working together Here's what surprised me most after launch. A lot of customer feedback is quality of life: small friction points, minor annoyances that never make it onto roadmaps because they don't look impressive in a planning doc. Annsa surfaces them because it reads everything, not just the loudest asks. And here's where the two revolutions meet. AI coding tools can turn those small fixes into shipped code in hours. Quality of life improvements that would have spent months buried in a backlog (too small to justify the planning overhead, too real to ignore) can now be found by Annsa and built the same week. Some of the biggest cheers from customers come from exactly those fixes. The friction that everyone lived with because nobody had time to get to it. Now someone does. What Annsa gives back, more than anything, is time. Time to prototype. Time to iterate. Time to be present with the product and the people using it, instead of buried in the work of figuring out what they said. --- For two decades, deciding what to build lived in spreadsheets, meetings and instinct. Deciding stayed manual. The [missing layer](https://annsa.ai/blog/what-product-intelligence-becomes) exists now. Annsa turns customer signal into one ranked list, so you decide at the speed building now moves and your agents build from the same plan. The answer to "what should we build next?" stopped being a guess. ## Keep reading - [All posts](https://annsa.ai/blog) - [AI agent platform](https://annsa.ai/agents) - [The whole team, one direction](https://annsa.ai/blog/march-the-whole-team-one-direction) - [Using Annsa to build Annsa](https://annsa.ai/blog/september-getting-our-agents-in-a-row) ## Put the spreadsheet down. Begin with one agent and the work that matters next. - [Try Annsa](https://app.annsa.ai/signup) - [See pricing](https://annsa.ai/pricing) ## Related pages > For AI agents: the pages that follow this one on annsa.ai. The full index is https://annsa.ai/llms.txt. - [AI agent platform: one plan for every agent](https://annsa.ai/agents) If anything on this page is wrong or missing, tell us: https://annsa.ai/contact