How Data Goblins Wreck Copilot For Everyone cover art

How Data Goblins Wreck Copilot For Everyone

How Data Goblins Wreck Copilot For Everyone

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 Picture your data as a swarm of goblins: messy, multiplying in the dark, and definitely not helping you win over users. Drop Copilot into that chaos and you don’t get magic productivity—you get it spitting out outdated contract summaries and random nonsense your boss thinks came from 2017. Not exactly confidence-inspiring. Here’s the fix: tame those goblins with the right prep and rollout, and Copilot finally acts like the assistant people actually want. I’ll give you the Top 10 actions to make Copilot useful, not theory—stuff you can run this week. Quick plug: grab the free checklist at m365.show so you don’t miss a step. Because the real nightmare isn’t day two of Copilot. It’s when your rollout fails before anyone even touches it.Why Deployments Fail Before Day OneToo many Copilot rollouts sputter out before users ever give it a fair shot. And it’s rarely because Microsoft slipped some bad code into your tenant or you missed a magic license toggle. The real problem is expectation—people walk in thinking Copilot is a switch you flip and suddenly thirty versions of a budget file merge into one perfect answer. That’s the dream. Reality is more like trying to fuel an Olympic runner with cheeseburgers: instead of medals, you just get cramps and regret. The issue comes down to data. Copilot doesn’t invent knowledge; it chews on whatever records you feed it. If your tenant is a mess of untagged files, duplicate spreadsheets, and abandoned SharePoint folders, you’ve basically laid out a dumpster buffet. One company I worked with thought their contract library was “clean.” In practice, some contracts were expired, others mislabeled, and half were just old drafts stuck in “final” folders. The result? Copilot spat out a summary confidently claiming a partnership from 2019 was still active. Legal freaked out. Leadership panicked. And trust in Copilot nosedived almost instantly. That kind of fiasco isn’t on the AI—it’s on the inputs. Copilot did exactly what it was told: turn garbage into polished garbage. The dangerous part is how convincing the output looks. Users hear the fluent summary and trust it, right up until they find a glaring contradiction. By then, the tool carries a new label: unreliable. And once that sticker’s applied, it’s hard to peel off. Experience and practitioner chatter all point to the same root problem: poor data governance kills AI projects before they even start. You can pay for licenses, bring in consultants, and run glossy kickoff meetings. None of it matters if the system underneath is mud. And here’s the kicker—users don’t care about roadmap PowerPoints or governance frameworks. If their very first Copilot query comes back wrong, they close the window and move on. From their perspective, the pitch is simple: “Here’s this fancy new assistant. Ask it anything.” So they try something basic like, “Show me open contracts with supplier X.” Copilot obliges—with outdated deals, missing clauses, and expired terms all mixed in. Ask yourself—would they click a second time after that? Probably not. As soon as the office rumor mill brands it “just another gimmick,” adoption flatlines. So what’s the fix? Start small. Take that first anecdote: the messy contract library. If it sounds familiar, don’t set out to clean your entire estate. Instead, triage. Pick one folder you can fix in two days. Get labels consistent, dates current, drafts removed. Then connect Copilot to that small slice and run the same test. The difference is immediate—and more importantly, it rebuilds user confidence. Think of it like pest control. Every missing metadata field, every duplicate spreadsheet, every “Final_V7_REALLY.xlsx” is another goblin running loose in the basement. Leadership may be upstairs celebrating their shiny AI pilot, but downstairs those goblins are chewing wires and rearranging folders. Let Copilot loose down there, and you’ve just handed them megaphones. The takeaway is simple: bad data doesn’t blow up your deployment in one dramatic crash. It just sandpapers every interaction until user trust wears down completely. One bad answer becomes two. Then the whispers start: “It’s not accurate.” Soon nobody bothers to try it at all. So the hidden first step isn’t licensing or training—it’s hunting the goblins. Scrub a small set of records. Enforce some structure. Prove the tool works with clean inputs before scaling out. Skip that, and yes—your rollout fails before Day One. But there’s another side to this problem worth calling out. Even if the data is ready, users won’t lean in unless they actually *want* to. Which raises the harder question: why would someone ask for Copilot at all, instead of just ignoring it?How Organizations Got People to *Want* CopilotWhat flipped the script for some organizations was simple: they got people to *want* Copilot, not just tolerate it. And that’s rare in IT land. Normally,...
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