Calculating ROI Before Adopting a Per-User AI Tool

"How much is the monthly subscription" is not the question that decides adoption. The real question is "is the time you get back for that money worth more than the fee". This post takes a per-user AI tool as an example and lays out how to do the arithmetic before you adopt.

Convert the ROI into time

The first step is putting money and time in the same unit.

Monthly labor cost per person ≈ monthly pay ÷ monthly working hours
Value of time saved = time saved × hourly labor cost
Monthly net effect = (users × per-person value saved) − (users × per-person fee)

The key is not overestimating the time saved. The speed you feel in a demo is largely offset by verification and correction in real work.

Step 1: pick the tasks that might get faster

Before adopting, make a list of "what will get faster". For example:

Task	Times a day	Time each	Expected saving
Email drafts	5	4 min	1 min × 5
Meeting minutes	2	20 min	8 min × 2
Summarizing material	1	30 min	10 min
Document search	6	3 min	1 min × 6

This table is only a list of hopes. The next step cuts it down.

Step 2: subtract verification and correction time

AI output is not used as is. The time to polish a draft, check numbers and fix what is wrong always comes on top.

Real saving = expected saving − (verification time + correction time + prompt writing time)

As experience accumulates the saving grows, but in the first month it may be close to zero is the safer assumption. The standard for verification itself is in a verification procedure so you do not use AI output as is.

Step 3: add the hidden costs

Looking only at the subscription is not enough.

• Initial setup and admin training time
• Loss of productivity while the team gets used to it
• Work to manage accounts and permissions
• The cost of rolling back if it fails
• The time spent on data boundary checks and security review

These costs are front-loaded and fall afterwards. So calculate on a 12-month basis.

Step 4: narrow down the number of users

Rolling out to everyone is almost always a loss. Start with the 10–20% who do the most of the tasks that could get faster.

• People who write many documents a day
• People with many meetings and records
• People who repeat searches a lot

Do not include people whose work is simple, or who has nobody to verify AI results.

Step 5: put it on one sheet

Collect the numbers on one sheet. Example (figures are placeholders):

Item	Value
Number of users	10
Monthly fee per user	(check the official page)
Monthly hours saved per user	2
Hourly labor cost	(internal figure)
Monthly subscription cost	users × fee
Monthly value of saving	users × hours saved × labor cost
Net effect	value saved − subscription cost − hidden costs

Prices change often, so check each service's official pricing page directly.

A three-month trial and the decision rule

Run a three-month trial before rolling out to everyone.

1. Choose the users (10–20%).
2. Choose the measures: hours saved, reversions, error count, satisfaction.
3. Record the same items in months 1, 2 and 3.
4. Decide to continue, expand or stop.

Set the decision rule in advance — "if the saving is under one hour per person per month, we do not expand". If you set the standard later, the judgment blurs.

Common mistakes

• Copying the demo's felt speed straight into hours saved
• Not subtracting verification and correction time
• Starting with everyone
• Signing an annual contract without a three-month trial
• Not setting a cost ceiling or an exit plan

Checklist

• Made a list of tasks that could get faster
• Subtracted verification and correction time
• Calculated hidden costs over 12 months
• Narrowed down the users
• Set the decision rule and the period
• Wrote an exit plan

The same calculation applies to any other per-user AI or SaaS tool. Which tool to choose is covered in 7 criteria for choosing AI and SaaS tools, and overlapping bills in the AI and SaaS subscription audit.