AI Business Automation
How do you calculate back-office automation ROI?
Measure cost per case before anything is built: fully loaded handling time, rework, and the cost of errors caught downstream. Model value from three levers — straight-through processing rate, reduced review time on the remainder, and error reduction — then subtract platform, integration, evaluation and run cost. Claim only savings the operating plan can realise, because capacity released is not cash saved until headcount, backlog or growth absorbs it.
Last reviewed 2026-08-31 · pronix.ai
Key takeaways
- No baseline, no business caseA case built on vendor averages fails review. The baseline must come from the enterprise's own volumes, handling times and error costs.
- Run cost is part of the caseInference, platform, monitoring and the engineering time to maintain evaluation sets belong in the denominator from day one.
- Benefit realisation needs an ownerFinance signs off on how released capacity converts — attrition backfill avoided, backlog cleared, or volume growth absorbed without hiring.
What the numbers show
First-party figures from Pronix research. Each links to the report or playbook that publishes it.
- 60–85%
- A contained AI interaction is typically 60% to 85% cheaper than the equivalent human-handled contact.Source: Contact Center AI Benchmarks by Industry 2026 →
- 25–35%
- Foundation-model tokens account for 25% to 35% of total cost of ownership for a mature enterprise LLM workload.Source: Enterprise LLM Cost & TCO Benchmarks 2026 →
- 15–25%
- Enterprises with strong shift patterns recover 15% to 25% of CCaaS licence cost by moving from named to concurrent licensing.Source: FinOps for LLM and CCaaS →
External references
- NIST — AI Risk Management Framework (AI RMF 1.0) (2023)The govern / map / measure / manage structure Pronix uses to organise AI controls.
- ISO/IEC — ISO/IEC 42001 — AI management systems (2023)The certifiable management-system standard enterprise procurement increasingly asks about.