Your AI ROI in 60 seconds.
Four inputs. Real-time results. See exactly how much operational time your team is leaving on the table, and what AI implementation could reclaim.
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Win the Boardroom with AI Math
Your Business Profile
Model assumes 70% automation rate. AI overhead costed at $2.50/hr. This is an upper-bound planning scenario for a well-integrated deployment, not a typical outcome: MIT's 2025 NANDA research found 95% of enterprise GenAI pilots show no measurable P&L return, and McKinsey's 2026 data puts significant EBIT impact at roughly 6% of firms. Treat this figure as the ceiling to plan toward, not the number to expect by default.
Estimated Annual Impact
Your Estimated AI Impact: $685K/yr Saved
Company Size (Number of Employees)
Opens a 6-page report with your inputs, an automation roadmap, and calculated ROI. Print or save it as a PDF from there.
This calculator models a planning estimate from the inputs above, using a fixed 70% automation assumption and a $2.50/hour platform-cost assumption. It is not a financial projection, an audit, or a guarantee of return. For a fuller 3-year TCO and adoption model, see the Enterprise AI Value & Adoption Dashboard, which uses a different methodology and will not produce the same number as this calculator on purpose. Your inputs stay in your browser and are not sent to Terence Kok or reviewed by anyone.
What each metric measures, why it matters, and how to validate it.
Automation Runway
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
- As a rule of thumb from AI business cases in practice, expect estimated manual task hours to diverge from actual measured hours by roughly 30–40%, not a measured statistic, but a common enough gap that over-estimating is the single most frequent error in these cases
- Runway also reveals where to look first: the highest-runway processes are your best automation candidates
- Time-log actual manual task hours for one week across a sample of 5–10 employees before accepting slider defaults
- Separate structured repetitive tasks (high automatable) from unstructured judgement tasks (low automatable)
- Use the validated hours as the basis for your business case. Estimated hours are challenged in board reviews
Net Annual ROI
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
- Treat the figure this model produces as a best-case planning ceiling, not a forecast. Most published 2025 to 2026 research on enterprise AI, including MIT's NANDA study and McKinsey's State of AI work, finds the large majority of programmes fall well short of it
- Use all-in staff costs (not base salary). Add 25–35% for benefits, overhead, and management time
- Verify actual AI platform costs with vendor quotes before finalising the overhead rate
- Present net ROI, not gross savings, to leadership. Gross savings without overhead deduction will be challenged
AI Readiness Score
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.
- As a directional guide, not a modelled output of this calculator, a company at 60% readiness with a $500K projected ROI should plan for realisation closer to 18–24 months than 6. Readiness is a timeline signal as much as a viability one
- 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
- If your score is below 40%, invest in data infrastructure before committing AI tooling budget
- Take the full AI Readiness Self-Assessment for a five-dimension breakdown of exactly which readiness gaps to address
- Re-run the calculator after addressing the primary readiness gap. The timeline and risk profile change materially
I built this after watching too many AI business cases get killed by vague ROI numbers nobody could defend to a CFO. The math here nets out the AI platform overhead, not just gross hours saved, because that's the number that actually survives a budget review. It won't tell you exactly what you'll save. It will tell you whether the case is even worth building. If the number surprises you, that's usually the most useful part.
