They tried to sell me 'analytics automation' for $12k. I opened Excel and did the maths
Ten minutes, three numbers, one sheet. A formula after which half of the proposed rollouts fall off on their own because they'll never pay back.
An acquaintance called and asked me to look at a proposal. "We're thinking of buying it," he said. "Price isn't low, but they promise 10× efficiency. Could you look?"
I looked. It was about automating a quarterly analytics task. A financial report that an analyst compiles every three months, eight hours of work, pulling data from four sources. The automation would do it in ten minutes.
Price — $12,000 setup, plus $500/month maintenance.
I opened Excel. In ten minutes it was clear this was a failing rollout. Not because of bad company or tool. Because the analyst does this task once a quarter. Do the maths:
Eight hours × four times a year = 32 hours per year. Analyst costs about $4,000 a month = $25 an hour. So the task's annual cost is $800.
Setup — $12,000. Maintenance — $6,000 a year. For the automation to break even in two years, you'd have to save $9,000 a year. You save a maximum of $800.
No, this won't pay back. Not in two years, not in five, never. I called my acquaintance and said: "Let the analyst keep doing it. Honestly — it's cheap."
I have conversations like this two or three times a month. Someone sells automation, someone buys, someone complains "we rolled it out, it didn't pay off." In fact, in the overwhelming majority of cases whether a rollout will pay back or not is a question you can answer in ten minutes in Excel. With precision sufficient for a decision.
I've been doing this for a few years, and I've arrived at a simple formula with three variables. It doesn't give pennywise precision. It gives precision that separates yes from no.
ROI = (T × C × N) - (S + O)
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S + O
Where:
T — minutes of human time per one operation before automation.
C — cost of one employee-minute (monthly salary ÷ 168 ÷ 60).
N — number of operations per month.
S — setup cost (dev or implementation), amortised over 12 months.
O — operating cost per month (LLM tokens, SaaS subscription, maintenance).
If ROI > 1 — pays back in under a month. If ROI > 0 — under two. If ROI < 0 — don't do it.
Two examples from real projects.
Example one. Email triage.
T = 3 minutes per email (I sat next to the operator and timed).
Operator: $1,000/month ≈ $0.10/minute.
N = 2,000 emails/month.
Setup: two weeks of development × $2,500 = $5,000. Over 12 months → $420/month.
LLM tokens: 2,000 × $0.02 ≈ $40/month.
Benefit = 3 × $0.10 × 2,000 = $600/month.
Cost = $420 + $40 = $460/month.
ROI = ($600 − $460) / $460 ≈ 0.30.
Pays back in ~3 months. Do it.
Example two. Quarterly financial report (the one I started with).
T = 480 minutes per report.
Analyst: $4,000/month ≈ $0.41/minute.
N = 0.33 operations per month (4 times a year).
Setup: $12,000 = $1,000/month.
Operating: $500/month.
Benefit = 480 × $0.41 × 0.33 ≈ $65/month.
Cost = $1,000 + $500 = $1,500/month.
ROI = ($65 − $1,500) / $1,500 ≈ −0.96.
Loss. Don't do it. Let the analyst keep doing it — 32 hours a year is cheaper.
Three mistakes I most often see when this formula is applied.
First — not counting verification time.
Automation rarely gives 100% accuracy. If the system errs in 5% of cases and a human reviews those errors, actual T_after = 5% × T_before + some time for review. Keep those 5% in mind.
Second — forgot about maintenance.
In six months the input format will shift, and the prompt will stop working. Or the LLM API will change pricing. Or a new model drops and is worth trying. That's 5–20% of operating cost every year.
Rule: if there's no team that can maintain the automation, it will break sooner or later and ROI flips negative. I now check this at the start. If the client says "the developer leaves after launch" — we need another developer, someone internal, who'll maintain it.
Third — counting only direct savings.
Automation often brings indirect benefits that don't land in the formula:
— Customer response time from 24 hours to 5 minutes = conversion × 1.3.
— Load taken off a key expert = they don't burn out = they don't quit.
— Ability to scale without hiring = growth without headcount.
These aren't in the formula, but sometimes they're bigger than direct savings. Especially the third one — with one client we once calculated that automating their qualification process let them take three times more leads without growing the sales team. That's not "savings per lead." That's new revenue that otherwise didn't exist.
So I count direct savings first, indirect benefits last — but I count them. And if the direct formula gives ROI near zero, indirect benefits can tip it.
That conversation about the analytics report I ended with the line: "Automation ROI isn't a matter of faith. It's three numbers multiplied and divided. If someone sells you an implementation and can't fill in this formula — they're not solving your business case. They're selling themselves work."
That's, I think, the most honest rule I can give on this topic. The formula is free. Excel is free. Ten minutes you have.