How to Measure the ROI of AI Tools in an Advisory Practice

Why start with a baseline, and how do you set one?

Vendors describe time savings in their marketing, and those claims may be true for someone. They tell you nothing about your practice, your clients or how you work today. The only number that matters is the difference between how long a task takes you now and how long it takes with the tool.

So measure before you buy, or at least before you start the pilot. A week of honest tracking on one task is enough to give you a starting point. Without it, you'll be comparing the pilot to a guess, and guesses tend to drift toward whatever you hoped to find.

This guide gives you a method, not a promise. It doesn't include industry averages for time saved because we don't have reliable, sourced ones for advisory firms. Your own baseline is better anyway.

For one to two weeks before the pilot, everyone in the test group logs the chosen task each time they do it: date, start and end time (or minutes), and anything unusual. A shared spreadsheet is fine.

Count the whole task, not just the typing. For meeting notes, that includes finding your rough notes, writing the summary, entering it in the CRM and creating tasks.

Note quality issues too: how often a note is incomplete, how often a client email needs a second version, how often compliance sends a marketing draft back.

Write down the volume: how many times the task happens per week per person. That number turns minutes saved per task into hours saved per month.

Which workflow should you measure?

Pick one workflow that is frequent, time-consuming and easy to define, and one main measure for it. Good candidates:

Meeting notes and CRM updates: minutes from the end of a meeting to an approved note in the CRM. Notetakers in our meeting notes & crm notes category, such as Jump, Zocks and Fathom, target this directly; our client review meeting workflow shows each step you'd be timing.

Follow-up emails: minutes from the end of a meeting to a reviewed follow-up sent.

Tax return review: minutes per return from upload to a planning-topics summary. Tools in tax return & document analysis, such as Holistiplan and FP Alpha, focus here.

Marketing drafts: minutes from topic to a draft ready for compliance review, plus how many rounds of compliance edits it needs.

Avoid measuring "productivity" in general. Broad goals produce fuzzy results that are easy to argue with in either direction.

How do you run a 30-day pilot?

Keep it small. One or two advisors, one workflow, 30 days. A small pilot is easier to supervise and cheaper to reverse.

Get approval first. Run the pilot only in a tool your compliance team has approved for the data involved, through firm-managed accounts. Your firm's written AI policy should say what that approval includes. Many tools offer free plans or trials; check each vendor's current terms on our pricing pages.

Measure the same way as the baseline. Same log, same definition of start and finish. Include the time spent reviewing and correcting AI output; that review is part of the task, not overhead.

Allow a learning curve. The first week is often slower while people learn the tool. Track it, but judge mostly on weeks three and four.

Keep notes on what went wrong: errors in AI output, integration problems, client reactions, anything compliance flagged.

Which metrics should you track during the pilot?

Keep the list short enough that people actually fill it in. For most pilots, five numbers are enough:

Minutes per task, measured the same way as the baseline, including review time.

Volume: how many times the task happened, so you can convert minutes per task into hours per month.

Corrections: how many outputs needed a factual fix before approval.

Compliance returns: how many outputs compliance sent back, if the task goes through compliance.

Adoption: how many of the eligible tasks people actually used the tool for. Low adoption usually means the tool doesn't fit the workflow, which is worth knowing before you buy more seats.

A simple weekly check-in, ten minutes with the pilot group, catches problems early and keeps the log honest.

Which costs should you count?

The subscription is only part of the cost. Count all of these:

Seats or licenses for everyone who'll use the tool, at the plan you'd actually buy. List prices vary by plan, billing term and seat count; see each tool's page on our pricing hub, where prices are shown as of October 21, 2026 with a link to the vendor's source.

Add-ons you'd need, such as an AI notetaker add-on to a CRM.

Setup and onboarding fees, and the hours your staff spend on setup and integration.

Training time for each user.

Review time: the minutes spent checking AI output, including any extra compliance review. FINRA's 2026 Annual Regulatory Oversight Report lists validation and human review of AI outputs among the practices firms may use; budget for it rather than assuming it's free.

Vendor diligence and compliance work to approve the tool in the first place (see our AI vendor due diligence guide), and the annual review after.

Billing terms. Many vendors list a lower monthly price for annual billing than for month-to-month, and some plans have a minimum number of users. Price the pilot on the terms you'd actually sign, and remember that an annual commitment makes canceling after a disappointing pilot more expensive. Ask whether a monthly plan or a trial is available for the pilot period.

Overlap with tools you already pay for. Check whether your CRM, email platform or office suite already includes, or offers as an add-on, a feature that does the same job. The cheapest option is sometimes a feature you already have.

How do you measure quality, not just time?

A tool that saves ten minutes per note but introduces errors isn't saving time; it's moving the work to whoever finds the mistake later. Track at least two quality signals alongside time:

Error rate: how many AI outputs needed a factual correction (a wrong name, figure, date or action item) before approval.

Rework: how many outputs needed a substantial rewrite, and how many compliance sent back.

Our check an AI meeting summary prompt gives reviewers a consistent checklist, which makes the error count more reliable. If quality during the pilot is worse than your baseline, fix the process or the tool before scaling up.

A worked example (hypothetical numbers)

Example only. Every number in this section is invented to show the arithmetic. None is a statistic, a benchmark or a typical result.

Suppose a two-advisor team pilots an AI notetaker for meeting notes. In the baseline, each advisor holds 8 client meetings a week and spends 30 minutes per meeting writing the note, entering it in the CRM and creating tasks. In weeks three and four of the pilot, the same work takes 12 minutes per meeting, including 5 minutes reviewing and correcting the AI summary.

Time saved: 18 minutes per meeting × 8 meetings × 2 advisors = 288 minutes, or 4.8 hours, a week. Over 48 working weeks that's about 230 hours a year, or about 19 hours a month.

Value of that time: the team decides an hour of advisor time is worth $150 to them, but only if the hours are reused for client work. They plan to add two prospect meetings a month with the time. At $150 an hour, 19 hours a month is worth about $2,850.

Cost: suppose the tool costs $100 per advisor per month, so $200 a month for two, plus 6 hours of setup and training time valued at $900 once.

Result: about $2,850 of reused time a month against $200 a month, plus a one-time $900 that's covered in the first month. On these made-up numbers the tool clearly pays off. Change any input (fewer meetings, more review time, time that isn't reused) and the answer can flip. Run your own numbers in our AI ROI calculator.

A second hypothetical, where it doesn't pay. Same team, but in the pilot the advisors spend 15 minutes correcting each AI summary because the tool struggles with their meeting style, so the task takes 25 minutes instead of 30. That saves 5 minutes per meeting, or 80 minutes a week across both advisors, about 5.3 hours a month. If the team doesn't change their schedule, that time isn't reused at all, and the realistic value is close to zero against $200 a month. Even if they did reuse it, 5.3 hours at $150 is about $800. The lesson isn't that the tool is bad; it's that review time and reuse decide the answer.

What are the common measurement mistakes?

No baseline. Comparing the pilot to how long you think the task used to take.

Counting only the subscription. Leaving out setup, training and review time makes almost any tool look profitable.

Ignoring review time. Minutes spent checking AI output are part of the task. If they're excluded, the savings are overstated.

Judging on week one. The learning curve makes early weeks look worse than steady state; judge on the later weeks.

Assuming saved time is reused. Time only has value if it goes somewhere. Decide in advance what the freed hours are for, and check at the end whether they went there.

Measuring only the enthusiasts. If the pilot group is the two people most excited about AI, results may not carry over to the rest of the team. Include at least one skeptic if you can.

Changing several things at once. If you add a new tool, a new CRM template and a new assistant in the same month, you won't know what caused the change.

How do you decide whether to keep the tool?

At the end of the pilot, compare the results against the baseline with the whole team in the room, including your compliance lead.

Keep it if time saved is meaningfully more than the full cost and quality held up or improved.

Change how you use it if the savings are there but quality slipped: tighten the review step, adjust settings, or narrow the tool to the parts of the workflow where it worked.

Write the decision down. Record the baseline, the pilot results, the full cost, the quality findings and the decision, with the date and who made it. If you revisit the tool next year, or an examiner asks how you evaluated it, you'll have the reasoning, not just the outcome.

Cancel if the savings are small, quality got worse, or the time saved isn't being reused for anything valuable. A canceled pilot is a good result; it cost you a month instead of a year. Before canceling, export anything your firm is required to keep.

Re-measure at renewal. Prices, features and your own workflows change. Before each renewal, rerun a short version of the measurement: a week of logging, an updated cost and a quick quality check.

If you keep it, roll out gradually, write the workflow into your procedures, and re-measure after a few months. If you're comparing several tools, our tool finder can help build a short list to pilot next.

Can you use your ROI results in marketing?

Be careful. If you plan to tell clients or prospects that AI makes your service faster or better, that statement may be an advertisement under the SEC Marketing Rule for SEC-registered advisers, and statements of material fact must be ones you can substantiate. The SEC has also brought "AI washing" cases against advisers that overstated their use of AI (press release 2024-36).

Keep internal pilot numbers internal unless your compliance team approves a specific, accurate public statement. Our guide to the SEC Marketing Rule and AI-written content explains the issues.

Sources

FINRA: 2026 Annual Regulatory Oversight Report, GenAI.

SEC: Rule 206(4)-1, Marketing Rule (eCFR); Press release 2024-36 (AI washing charges). This guide is general information, not legal or compliance advice.