Your AI ROI in 60 seconds.

The total hours per year your team spends on manual work that AI can automate, calculated as: Employees × Weekly Manual Hours × 52 weeks × 70% automation rate. The 70% factor is conservative — it accounts for tasks within a manual process that remain human-handled even after AI deployment.

• Automation Runway is the pre-investment signal for whether AI automation is worth pursuing — organisations with under 5,000 hours/year typically find the ROI case difficult to close
• The difference between estimated and actual manual task hours is typically 30–40% — over-estimating is the most common error in AI business cases
• Runway also reveals where to look first: the highest-runway processes are your best automation candidates

1. Time-log actual manual task hours for one week across a sample of 5–10 employees before accepting slider defaults
2. Separate structured repetitive tasks (high automatable) from unstructured judgement tasks (low automatable)
3. Use the validated hours as the basis for your business case — estimated hours are challenged in board reviews

The net financial return calculated as: (AI Time Savings Hours × Hourly Wage) − (AI Savings Hours × $2.50 overhead). The $2.50 represents the fully-loaded cost of AI platform access, maintenance, and governance per hour saved — reflecting real enterprise AI platform costs at scale.

• Most AI ROI calculations only count gross savings (hours × wage). The net ROI deducts ongoing AI overhead, producing a defensible number for CFO review
• The $2.50 overhead rate is conservative for enterprise deployments but may underestimate for small-scale or highly customised implementations
• Net ROI is the floor, not the ceiling — it excludes AI-influenced revenue (pipeline acceleration, error reduction) which is often 3–5× larger than direct savings

1. Use all-in staff costs (not base salary) — add 25–35% for benefits, overhead, and management time
2. Verify actual AI platform costs with vendor quotes before finalising the overhead rate
3. Present net ROI, not gross savings, to leadership — gross savings without overhead deduction will be challenged

A composite score derived from your tech stack maturity (1 = fully manual, 3 = basic cloud apps, 5 = modern data platform) reflecting how quickly and reliably AI automation can be deployed in your environment. The score predicts implementation timeline and risk, not just whether AI is viable.

• A company at 60% readiness with a $500K projected ROI should expect to realise it over 18–24 months, not 6 — the score is a deployment timeline predictor
• Below 40% readiness, the ROI model's assumptions about data accessibility and integration break down — projected ROI will be lower than calculated
• The readiness score is the risk multiplier for the ROI figure — never present ROI without the corresponding readiness context

1. If your score is below 40%, invest in data infrastructure before committing AI tooling budget
2. Take the full AI Readiness Self-Assessment for a five-dimension breakdown of exactly which readiness gaps to address
3. Re-run the calculator after addressing the primary readiness gap — the timeline and risk profile change materially