The short answer
Stop treating AI as all-or-nothing. Use it to generate emissions estimates and flag patterns, then check its assumptions yourself before anything ships. Your job shifts from data-gatherer to reviewer. That split — AI drafts, you verify — is where the real value lives.
You're stuck in a binary: either you don't use the tool at all, or you let it run and hope for the best. Research on AI in professional settings points to a middle path — people who get the most out of AI use it to handle the heavy lifting on first drafts and pattern-spotting, then bring their own judgment in to check the logic. That's not blind trust and it's not avoidance. It's a workflow.
Marcus Aurelius says to strip a thing down to its bare cause — look at what it actually is, not what you fear or assume it to be. An AI model producing emissions data is just a tool running calculations on the inputs you give it. It has no judgment about whether those inputs are right. It can't know if your activity data is stale, or if a source category got mislabeled. That's not a flaw to fear — it's just the boundary of what it can do. You supply what it can't.
This week, pick one data set you'd normally build manually. Run it through the AI tool first, then spend 30 minutes auditing the output: check the key assumptions it used, flag anything that doesn't match what you know about the source, and note where it saved you time versus where it needed correction. That single review loop will teach you more about where to trust it — and where not to — than any amount of reading about AI.