Where AI actually creates leverage
A practical framework for deciding which workflows should use AI — and which should not.
AI is genuinely useful, but not everywhere. The difference between a system that creates leverage and one that creates cleanup work usually comes down to whether the workflow was a good fit in the first place. A short test up front saves a lot of regret later.
/Good candidates share three traits
First, the task is repetitive and high-volume — something a person does the same way many times a day. Second, it’s tolerant of a quick human check, so a rare miss is caught rather than catastrophic. Third, it involves language, summarizing, drafting, classifying, or research — exactly the shape of work modern models are good at.
Lead qualification, first-draft follow-ups, research summaries, routing and tagging, and turning messy notes into structured records all fit that profile. In each case AI removes the slow, boring middle of the task while a person keeps judgment over the outcome.
/Poor candidates share one trait: no room for error
When a mistake is expensive, irreversible, or legally sensitive — and no one is checking the output — full automation is the wrong call. The same is true for decisions that depend on context the model can’t see, or judgment the business is ultimately accountable for. There, AI belongs in an assistive role: it drafts, a person decides.
/Keep a human where judgment lives
The strongest setups aren’t “AI” or “no AI.” They’re AI doing the volume and a person owning the judgment, with a clear handoff between the two. That division is where real leverage shows up: the team spends its attention on the decisions that matter and stops spending it on the work that never did.