October 12, 2024

What AI Actually Saves a Professional Services Firm

Every partner asks the same question in a different order: does this actually save time, or does it just move the work around? The honest answer depends on the task.

Where the hours really disappear

First-draft research is the clearest win. Pulling a target company's basic profile, recent filings, and public statements used to eat an associate's afternoon. A well-built research process now does the first pass in minutes, leaving a person to check it rather than build it from scratch.

Drafting follows the same pattern. A first draft of a proposal outline, a client memo skeleton, or a set of talking points is faster to edit than to originate. The saved time is real, but it shows up in the editing phase, not in the moment the draft appears.

Where it quietly fails

Anything requiring judgment about a specific relationship, what this particular CFO cares about, what happened in last year's engagement, whether a competitor already burned this contact, does not come from a general model. That knowledge lives in a partner's memory or a firm's own records, and no amount of drafting speed substitutes for it.

Numbers are the second failure point. A model can describe a trend correctly and still misstate the underlying figure. Every number that reaches a client needs to be traced back to its source before it goes out the door.

The honest math

A reasonable estimate, drawn from how firms actually use these tools today, is a 30 to 50 percent reduction in the time spent on first drafts and initial research, and close to zero reduction in the time spent on judgment calls, relationship context, and final review. Firms that expect the second number to move are the ones who end up disappointed.

The firms getting real value are not the ones asking AI to do more. They are the ones who identified the narrow band of repetitive, low-judgment work and automated exactly that band, while leaving everything upstream and downstream untouched.

A useful test before trusting a draft

Before accepting any AI-assisted draft as close to final, ask a simple question: could a first-year associate have produced this without access to the specific facts of this prospect? If the answer is yes, the draft is still generic and needs more input, not more editing. If the answer is no, if it clearly reflects something specific and true about this one company, the drafting stage has done its job and the review can focus on accuracy rather than substance.

This test is quick enough to apply to every draft without slowing the process down, and it catches the most common failure mode: a document that reads smoothly but could have been sent to almost anyone.

Watching the trend over a full quarter

Track the ratio of hours spent producing versus hours spent reviewing across a full quarter, not a single week. Firms that measure this consistently usually see the ratio shift steadily toward review over the first few months, as the input discipline described above becomes routine rather than a new habit people have to remember. That shift, tracked over time, is a more convincing internal case for the value of the process than any single fast draft could be.

Where firms most often overestimate the gain

The most common overestimate happens when a firm measures the time saved on a single draft and multiplies it by the number of drafts produced in a year, without accounting for the review time that draft still requires. A more honest measure tracks total time from request to sendable document, across a representative sample of real engagements, not a best-case demonstration draft chosen because it happened to need very little correction.

A final word on expectations

Set the internal expectation at the start of any rollout as a meaningful, not dramatic, improvement in first-draft speed, with no expected change in the time senior staff spend on judgment and relationship work. Firms that set this expectation up front report fewer disappointed conversations six months in than firms that let enthusiasm about a demo set expectations nobody intended to promise.

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 “where the hours really disappear,” the answer is usually specific rather than clever: first-draft research and drafting see the largest, most reliable time savings. 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 “where it quietly fails,” 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: automate the narrow band of repetitive work; leave the rest to partners. 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