How the bands are calculated

Adoption (50% / 80% / 95%). Not every task in the intended volume actually flows through the automation. Edge cases get routed back to humans. Exception paths get added to the manual queue. Adoption captures this reality.

Accuracy (85% / 95% / 98%). Not every automated run is good enough to skip human review. Quality control re-checks drag time savings down. Pessimistic accounts for material rework; best-case assumes mature eval + monitoring.

Redeployment (30% / 55% / 80%). Saved human time doesn't convert 1:1 into reallocated value. Parkinson's law absorbs a meaningful fraction. The realistic 55% captures typical post-automation redeployment patterns; pessimistic 30% accounts for the productivity that just disappears into longer breaks.

Multiplying the three: P50 captures ~42% of theoretical maximum savings, P90 ~75%, P10 ~13%. These bands are honest representations of outcome variance — they are not pessimism for its own sake.

Per-automation-type implementation cost ranges are sourced from cross-referenced 2026 case studies + system integrator rate cards, with explicit low/midpoint/high anchors:

• RPA: $30,000-$150,000 per process
• iPaaS workflows: $2,000-$25,000 per flow
• Self-host (n8n): $5,000-$30,000 platform + initial flows
• AI agent automation: $5,000-$80,000 — wide band captures eval depth + prompt engineering effort

P10 uses the high end (pessimistic build cost), P90 uses the low end (clean build), P50 uses the midpoint.

Each automation type carries an annual maintenance burden expressed as % of original implementation cost:

• RPA: 40% — bots break when target UIs change
• iPaaS: 15% — vendor maintains the integration layer
• Self-host: 25% — version upgrades + infra ops
• AI agent: 35% — prompt drift, model deprecation, eval failures, observability

All LLM rates are fetched live from each provider's pricing page on the verifiedOn date shown next to each model. Cost-per-task uses a representative 3,000-input + 1,000-output token shape with no prompt caching or batch discounts — apples-to-apples comparison across providers.

Two caveats matter for AI agent budgeting:

• Claude Opus 4.7 uses a new tokenizer that can consume up to ~35% more tokens for the same text than Sonnet/Haiku. The cost-per-task headline assumes equivalent token counts; real Opus cost on the same source text may be materially higher.
• OpenAI's model lineup migrated to GPT-5.x in 2026. GPT-4, GPT-4o, o1, o3 are no longer the primary listed models — if your project doc references the old lineup, update it.

Per-task economics where verifiable. Zapier, Make, n8n, Power Automate, and UiPath Basic publish entry-tier pricing publicly — verified directly. Automation Anywhere, Blue Prism, and Workato are quote-only by design — flagged in the tool matrix and excluded from per-task calculations.

Loaded hourly rates default to UK admin (£15/hr) or US admin ($22/hr) — sourced from ONS / BLS occupational wage statistics with industry-standard 1.3× burden multiplier for benefits, employer taxes, overheads.

We do not model the soft benefits (employee satisfaction, capacity for high-value work, faster customer response) that vendor calculators routinely include at 30-50% of total ROI. They are real, but not measurable in £/$ terms without large assumptions. Including them is what gets vendor calculators to 5×-10× headline ROI; excluding them is what keeps this site honest.

LLM API rates re-checked monthly. Tool pricing quarterly. Implementation cost ranges re-baselined annually against fresh case studies. Pre-deploy CI gate refuses to publish data >90 days stale.

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