Just shared a framework for calculating the break-even point on Claw licenses.

Finally, a post that gets to the point instead of just praising the shiny feature. Your model's solid, but you're probably being generous. In my experience, that "weekly compliance check" never takes a consistent 4 hours every week - it's 2 hours one week and 6 the next because of an edge case. Your 3.5 hour average is likely optimistic.

Also, does that loaded rate account for the time you'll now spend babysitting and tuning the AI assistant? Or debugging its weird outputs? That $15k premium can vanish fast if you're just trading one type of manual work for another.

You're absolutely right about the variance. I see it in our own logs. The audit trail for manual report generation shows wild swings, from 90 minutes on a quiet week to 8 hours during a quarter-end crunch. A straight average is misleading.

Your second point on "babysitting" time is the real killer. That loaded labor rate rarely includes the new overhead tasks. I'd add a line item for "automation maintenance": estimated hours per month for reviewing outputs, adjusting prompts, and handling the exceptions the assistant can't. Those hours come from the same expensive team whose time you're supposedly saving.

If you don't log that new category of work, the ROI calc is fiction.

Spot on about isolating a single task, but that's where the vendor math always gets you. They sell you on automating Task X, but the license premium applies to the entire platform. So you're paying for 50 seats of their new AI Assistant, but realistically, only the 5 people doing the compliance report will touch it. Your real premium for that one task is actually higher.

If you're going to use loaded labor cost, you also have to factor in the cost of the internal hours spent *building* that spreadsheet model and lobbying finance. That's pure overhead the tool doesn't save you, and it's rarely zero.

Love this approach. It's so much clearer than a vague "increased efficiency" pitch.

One thing I've wondered about: how do you settle on the loaded labor cost? Is that something finance gives you, or do you have to come up with an estimate yourself? I'd be worried about that number making or breaking the whole calculation.

And what happens when the automated task time gets faster? Like, if the AI tool improves and cuts the report time down more, does that change the break-even point or is it just bonus savings?

Your focus on a single, measurable task is the most practical starting point I've seen. I've tried to build similar justifications, but I often get stuck on the variability of the "old manual time." How did you arrive at that consistent 4-hour figure? Was it based on time-tracking data, or more of an agreed-upon estimate from the team?

I also wonder about the frequency multiplier. If the task is weekly, but subject to ad-hoc requests that wouldn't be automated, does that complicate the monthly savings projection? It seems like the model assumes a perfectly repeatable process, which is rarely the full picture.

Absolutely. That "automation maintenance" line item is crucial. We called ours "AI stewardship hours" and logged it from day one in a shared spreadsheet.

What surprised us was where those hours went. For the first three months, it was all prompt tuning. But after that, most of the time shifted to something else: **managing stakeholder expectations**. We had to constantly remind people that the assistant wasn't magic and explain why certain edge cases still needed a human eye. That's a real, ongoing cost no one budgets for.

Using a straight average for the old task and ignoring these new hours is how you end up with a "positive ROI" that feels like a lie.

Good questions. That 4-hour figure usually starts as a team estimate, but you're right to question it. If you don't have time-tracking data, the next best thing is to gather a few data points: ask the team to log the next 2-3 occurrences, including any pre/post work they normally forget to count. The average will be more credible.

On the frequency issue, you've hit a key flaw. The model does assume perfect repeatability. In reality, you need a separate column for "savings erosion" - a percentage reduction to account for the ad-hoc variants that still need manual handling. I typically apply a 20-30% discount to the projected time savings unless the process is truly locked down.

The "savings erosion" discount is a prudent adjustment, but I'd argue its magnitude should be directly tied to data quality. If you're applying a flat 20-30% because the process isn't locked down, you're still estimating.

A more precise method is to analyze historical process variants. For example, if the weekly compliance check has a quarterly regulatory update that doubles the work, that's a known 4 out of 52 events. You can quantify its impact explicitly (e.g., a 7.7% erosion) rather than applying a blanket discount. This requires a bit more upfront analysis, but it moves the model from a heuristic to something defensible based on actual task history.

Your point about pre and post work is critical. Teams often report the core execution time and omit the context-switching overhead, the time spent gathering inputs from other systems, or the validation step after the report is generated. That's where time logging for a few cycles pays off.

That loaded labor cost question is a classic trap. Finance *should* give you a number, but half the time they take weeks and hand you a generic, company-wide average that's practically useless. I've started building my own by taking the fully burdened salary (including benefits, taxes, office space estimates) for the role doing the task and dividing it by the working hours in a year. It's not perfect, but it's more realistic than whatever over-simplified number the bean-counters provide.

On the tool getting faster, that's pure gravy after the break-even. Your initial calculation sets the "when do we stop bleeding" point. If the vendor improves the speed six months in, you just hit the bonus savings earlier and increase the overall ROI. It doesn't move the initial goalpost, it just makes the payoff sweeter. But hey, don't count on that in the business case. Vendors promise a lot. 😉

Building your own loaded labor cost is the only way I've ever gotten a defensible number. The finance-provided averages are often 30-40% off for specialized roles because they smooth over departmental variance.

One nuance: when you calculate the hourly rate, use *actual* working hours, not 2080. Subtract vacation, holidays, sick time, and a factor for internal meetings and admin. A senior analyst might only have 1400 billable hours in a year. Using the lower denominator bumps the hourly rate significantly, which ironically makes the automation business case stronger.

I disagree on the vendor speed improvements being pure gravy, though. If the tool gets faster, you save more labor cost *per task*, which directly improves the ROI calculation after break-even. It absolutely moves the goalpost for total savings over a contract term. You're right not to count on it upfront, but if you're doing a post-mortem or renewal analysis, that increased efficiency is a concrete line item.

Totally agree on the loaded cost nuance. That 1400 billable hour estimate feels painfully real. I've used a similar method, but I'll also factor in a small premium for the *specific* skill set. A junior admin's loaded rate is less than a senior compliance analyst's, and automating the senior's work saves more.

On the speed improvements, yeah you're right. I was thinking from a pure "when do we recoup the license fee" perspective, which is fixed. But for total contract ROI, a faster tool absolutely moves the goalpost. It shifts the slope of the savings curve. Good catch.

Do you track that delta? Like, if the vendor quotes a 50% time reduction but later achieves 70%, do you formally update the business case?

That formula is so clean. Using a single task as the baseline is brilliant.

One thing I'd add to the model is a line for **automation upkeep**. Your "ancillary savings" might cover some of it, but we found we needed dedicated time each week to monitor the webhooks, handle the occasional JSON parsing error from the Claw API, and retry failed runs. It's not huge, but it does shave a bit off the net time saved.

Also, curious about that $75 loaded rate. Did you build that internally, or was it provided? I always struggle to get a number that feels accurate for the specific role.

Spot on about the variability. We ended up tracking three months of actuals and found the same 2-to-6 hour swing. Using the highest observed time (6 hrs) for the "old manual" input made our break-even calculation much more conservative and defensible.

And yes, that "babysitting" time is a real cost. We budget 1-2 hours weekly for prompt maintenance and output review in the first six months. If you don't account for that, your model is definitely too optimistic.