AI ROI calculator.

The core formula is simple: (hours of manual work automated per year × loaded hourly cost of the people doing it) minus what the system costs to build and run. Hours saved = people × hours per week × 52 × the share of that work automation realistically takes over. Hourly cost = monthly salary ÷ 173 working hours. Payback = build cost ÷ monthly savings. The honest complications are adoption (a system nobody uses saves nothing) and the fact that saved hours convert to value only when they are redirected to real work, which is why we default the automation share to 60%, not the 90% a pitch deck would use.

For SMBs and mid-market teams, one well-built production automation or assistant typically lands between $10,000 and $60,000 depending on how many systems it touches, with simple single-workflow builds below that and multi-department platforms above. We price fixed after a scoped audit, so the number you approve is the number you pay. Running costs (model usage, hosting) are usually tens to a few hundred dollars a month at SMB volume when the architecture is done with cost in mind; our own product Compumeal generates an AI meal plan for under two cents in model costs.

Sometimes, and it matters to be honest about when. Automation savings become cash when they let you avoid a hire, drop overtime, or serve more customers with the same team. They become capacity (still valuable, not cash) when the freed hours get redirected into selling, service, or work that was not happening. If the freed hours evaporate into slack, the ROI was theoretical, which is why adoption tracking is part of every engagement we run.

For repetitive, rule-shaped work (copying data between systems, drafting the same emails, assembling recurring reports), 50-80% is realistic in production. Demos hit higher; month three tells the truth. Judgment-heavy work automates less and should keep a human in the loop. If a vendor quotes 95% across the board, they are quoting a demo, not an operation.