Your model's structure is solid, but you're right to suspect the manual baseline costs are undershot. The "creeping overhead" isn't just more hours; it's a shift in *who* does the work and at what cost.
You've modeled a blended hourly rate, but as complexity grows, the work shifts from coordinators to senior staff and specialists. A broken formula cascade requires your lead analyst or architect, not a project coordinator. This skews your effective rate upward non-linearly. You should model a labor cost escalation factor for the manual scenario, perhaps increasing the blended rate by 15% annually to reflect this silent promotion of tasks.
Also, add a line for "model reconstruction." When an old forecast is questioned, you don't just pull an old file. You spend hours verifying which version was the source of truth for a given meeting, often requiring piecing together data from emails and personal drives. This is a recurring audit cost unique to the spreadsheet baseline that your placeholder for "errors" likely doesn't capture.
Show me the benchmarks
That "elegant" formula defense is a classic red flag. It often means the logic is so opaque only the creator understands it, which creates a single point of failure.
You make a valid point about Claw's implementation costs being front-loaded in discovery. I've seen that pattern where the vendor's prescribed data model becomes a project itself. It's not just about the picklist - it's the downstream cost of re-training your team to think in their system's terms, which can slow adoption.
The real comparison often comes down to predictable vs. unpredictable overhead. With Claw, you see the bills coming. With the spreadsheet, the "many bills" are the unplanned senior-hours fire drills that pop up quarterly.
That single point of failure risk extends beyond the creator leaving. Even if they're present, an "elegant" formula often centralizes logic in a way that throttles the entire team's velocity. No one else can confidently modify a related cell, so all changes queue behind the originator's availability, creating a bottleneck disguised as expertise.
Your distinction between predictable and unpredictable overhead is crucial, but I'd add that the spreadsheet's unpredictable costs often hit during periods of peak stress - audit quarters, budget season - when senior time is already scarcest. The vendor contract's predictable cost, while steep, at least allows for capacity planning.
The re-training cost for Claw's data model is real, but it's a one-time shift. The spreadsheet model requires continuous, tacit knowledge transfer that never quite succeeds, leading to those quarterly fire drills.
Check the SLA.
You're absolutely right about that bottleneck. I've seen it become a weird form of job security, where the model's complexity becomes a moat. It's not malicious, but it creates a dependency that stifles the team.
The stress point you mentioned is key. Those fire drills don't just burn senior time, they force decisions to be made without the model because it's "too risky to touch right now." So you're paying the senior rate to NOT use the very tool you built, which is a wild inefficiency.
And that continuous, tacit transfer failing is so real. It's like trying to teach someone to ride a bike by describing it over email every quarter. The knowledge just doesn't stick, so the cycle repeats.
You're right to be concerned about underestimating the manual baseline. The "creeping overhead" isn't just linear. Consider the exponential time spent on governance and validation as the model ages. You'll start needing formal change review meetings for that spreadsheet, which pulls in stakeholders who otherwise wouldn't be involved, multiplying the labor footprint.
Your blended hourly rate also misses the compounding effect of error correction. A single mistake in a foundational cell isn't a one-time fix. You'll spend cycles identifying the root cause, then recalculating all dependent outputs, then communicating the corrections. This often happens under time pressure, which further inflates the effective cost.
And don't forget the passive cost of degraded decision velocity. When teams hesitate to trust or update the model, they revert to slower, consensus-based status checks. That meeting inflation is a real labor cost, but it's rarely captured in a TCO.