The spreadsheet was the right answer at five people. At twenty-five, it is the bottleneck. Four signs to watch for, and a proportionate migration sequence that does not retire the spreadsheet on day one.
Owners who measure data and knowledge readiness in a structured way ship better AI outcomes than owners who treat it as a feeling. Four questions, every quarter, no dashboard required.
Three situations where external data help is worth the spend, the more common ones where in-house is faster, and how to brief a consultant so the engagement actually lands.
A focused ninety-day sprint, structured in three phases of about a month each, gets a 5 to 50 person business from scattered information to data and knowledge that does not block the next AI use case.
Proposal_v4_dh_FINAL_final2.docx was tolerable when only humans read it. The moment AI is reading your documents the cost shows up sharply. Here are the three rules that do the work.
One page, twenty terms, ninety minutes. The cheapest and highest-impact AI readiness investment a small business can make, and the one almost nobody does.
Every SME has a metadata problem and almost none has named it as one. A proportionate fix is ten to fifteen tags, agreed once, reviewed quarterly.
An owner's new AI tool surfaced a 2019 PDF as the answer to a current client question. Three more recent and relevant documents were sitting two folders away. The fix is not a clean-out, it is a categorisation pass.
Your AI assistant just cited Client_Proposal_FINAL_v3 (2).docx as the latest version of a document that was superseded three months ago. The fix is four rules, applied going forward, with a forgiving attitude to history.
Two PDFs that look identical on screen can behave very differently when an AI tool tries to read them. Here is the difference, why it matters, and how to triage a mixed document estate without OCR-ing the lot.
Your team has already answered the question dozens of times. The answers are sitting in Slack, in old email threads, and in the call recordings nobody re-listens to. AI has finally made that archive useful, but only if you scope it proportionately.
An owner asks the AI to onboard a new client using the standard process. It invents half the steps. The fix is not a new tool, it is three small changes to how the SOP is written.
The most valuable knowledge in an owner-operated business is almost never written down. When a long-serving person leaves, the cost is measured in months of pattern-matching the replacement cannot do. Here is the proportionate capture discipline that actually works.
Enterprise master data management is a seven-figure programme. The version a five-to-fifty-person firm actually needs is two pages of decisions and thirty minutes a quarter.
Point a new AI tool at SME customer records and three problems appear within a week. They are predictable, they are well understood, and the fixes are proportionate.
Five short sessions, five hours total, one page at the end. A proportionate way for an owner to see what data and knowledge she actually has, and what state it is in, without commissioning a consultant.
When an AI tool stops working, the named failure is the model. The actual failure, four times in five, is the data it was fed. Here is how to tell which one you are looking at.
Most data readiness content was written for organisations with chief data officers and seven-figure clean-up budgets. Here is the owner-operated version, sized for a 5 to 50 person business with none of those things.
The next step is a conversation. No pitch, no pressure. Just an honest discussion about where you are and whether I can help.
© 2026 Larocca Consulting Ltd