June 12, 2025

Why 'AI Writes It in Seconds' Is the Wrong Metric

Vendors like to quote how fast a draft appears. That number tells you almost nothing about whether the draft was worth producing.

Generation speed versus useful speed

A draft that appears in ten seconds and needs forty minutes of correction is slower, end to end, than a template that took two minutes to adapt correctly the first time. The metric that matters is total time to a sendable document, not time to first output.

The variable that actually predicts the outcome

The single biggest predictor of whether an AI-assisted draft saves time is how much specific, verified information it started with. A draft built from the target's actual 10-K, actual leadership names, and actual recent events needs light editing. A draft built from a generic prompt needs a rewrite, because the model fills gaps with plausible-sounding fiction rather than admitting it does not know.

What good input discipline looks like

Firms that see consistent gains treat the input stage as the real work: gather the filing, confirm the names, note the specific event that makes this prospect timely. The drafting step, done well, is almost mechanical once the input is solid. Done poorly, drafting becomes a second research project disguised as writing.

This is also where hallucination risk concentrates. A model asked to write about a company it has thin information on will not usually say so, it will write confidently and incorrectly. The fix is not a better prompt. It is not asking the question until the facts are already in hand.

A short checklist for the input stage

Before asking for any draft, confirm four things are already in hand: the correct, current name of the company and the specific contact, the source document the draft should be grounded in, the one recent event that makes the outreach timely, and the specific ask or purpose of the document being drafted. Missing any one of these is the most common reason a draft comes back generic or wrong.

This is a two-minute checklist, not a formal process, but treating it as a required step rather than an optional habit is what separates firms that get consistently good drafts from firms that get an inconsistent mix.

Why this discipline pays off beyond the current engagement

A well-documented input trail on one proposal becomes a reusable reference the next time a similar prospect appears, a comparable company in the same sector, facing a similar disclosed event. Firms that keep this trail organized find that later proposals in the same practice area move faster, not because of any change in tools, but because the research discipline compounds into an internal reference library over time.

Where this leaves a firm

None of this is complicated in principle, which is exactly why it gets skipped under deadline pressure. The question worth returning to before treating where AI genuinely saves time on client work as settled is what a careful reader would actually notice if the firm got it right. On the point raised above under “generation speed versus useful speed,” the answer is usually specific rather than clever: time to a sendable document matters more than time to first draft. Firms that build this expectation into how they train new associates find it easier to sustain once experienced staff move on, because the standard lives in a documented habit rather than in one person's memory. The gap between a firm that talks about where AI genuinely saves time on client work and a firm that actually practices it shows up over several quarters, not in any single engagement, and it tends to show up most clearly in the small, unglamorous checks that a client never sees directly but benefits from anyway.

It also helps to name, plainly, who is responsible for keeping this working once the novelty of a new tool wears off. Someone should own the point raised under “the variable that actually predicts the outcome,” check it periodically rather than assume it stays true on its own, and be the person a colleague asks when a new situation does not fit the pattern described here. Put simply: thin input is where hallucination risk concentrates, fix it upstream. That kind of ownership, named and specific, is a small addition to a firm's process, and it is usually the difference between a good idea that is followed for a month and a standard that actually holds up over a year of real client work.

None of this needs to be elaborate to be effective. A short, dated note in a shared file, reviewed at the next quarterly check-in, is usually enough to keep the responsibility from quietly disappearing when the person who first cared about it moves on to something else.

VIPMarketing Editorial