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AI ROI / Calculator

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AI ROI Calculator

Sensible-Defaults Edition

Most AI ROI calculators inflate the result by ignoring adoption friction, productivity capture, and the cost of running inference at production scale. This one applies field-tested haircuts by default. Edit any input to model your scenario. Use the result to compare options, kill bad business cases early, and surface the assumptions that drive the answer.

Your inputs

Foundation model API or SaaS license, per user per year
Pilot-measured or industry benchmark, before adoption haircut
Salary + benefits + overhead, per user
Integration, training, change management, first-year governance
Advanced: override the field-tested defaults
Default 60%. Developer tools often hit 75\u201385%; general knowledge workers 35\u201360%.
Default 50%. Saved hours rarely convert to recovered value 1:1.
Default 1.0 if cost-per-seat already reflects production load. Set higher if pilot economics.

Result

Annual benefit $0
Annual cost $0
Net annual value $0
3-year ROI 0%
Payback period \u2014
Adjust the inputs to see your AI ROI scenario.

REFERENCE SCENARIOS

Three pre-computed scenarios for context

These run the same math the calculator above runs. Use them to sense-check whether your scenario\u2019s output is in a plausible range. They also work as the SEO-indexable fallback when JavaScript is disabled.

Small team, developer tools

GitHub Copilot or similar for a small engineering team. High adoption likely; capture rate moderate.

Team size20
Cost per seat / yr$480
Hours saved / wk5
Hourly rate$120
Implementation$15,000
Annual benefit$172,800
Annual cost$14,600
Net annual$158,200
3-year ROI1084%
Payback1 months

Mid-size knowledge worker rollout

M365 Copilot for general knowledge workers. Adoption typically below pilot; capture rate often the binding constraint.

Team size200
Cost per seat / yr$360
Hours saved / wk3
Hourly rate$90
Implementation$80,000
Annual benefit$777,600
Annual cost$98,667
Net annual$678,933
3-year ROI688%
Payback1 months

Customer service AI augmentation

AI agent assist for support team. Highest capture rate (decisions to recover headcount or grow throughput are binary and tracked).

Team size500
Cost per seat / yr$240
Hours saved / wk6
Hourly rate$45
Implementation$200,000
Annual benefit$1,944,000
Annual cost$186,667
Net annual$1,757,333
3-year ROI941%
Payback1 months

How the math works

Annual benefit equals: team size, multiplied by adoption rate, multiplied by hours saved per week, multiplied by 48 working weeks, multiplied by hourly rate, multiplied by productivity capture rate. The two haircuts (adoption and capture) account for the two places where pilot economics consistently fail to translate to production economics.

Annual cost equals: team size multiplied by cost per seat (with optional inference scale-up), plus one-third of the implementation cost (3-year amortization). The implementation amortization assumes a meaningful chunk of the build cost has reusable value beyond year one.

Payback is calculated against the implementation cost, divided by the net annual value plus the amortization (since the amortized chunk would be available cash if you stopped the program). If the net annual value is negative, payback is reported as "never".

The defaults (60% adoption, 50% capture, 1.0x inference scale-up) are conservative for general enterprise rollouts. Override them if your context warrants. The point of the calculator is to surface the assumptions, not hide them.

AI ROI Calculator: Frequently Asked Questions

What does the AI ROI calculator account for?
Five inputs and four hidden defaults. The five inputs you control: team size, annual cost per seat for the AI tool, hours saved per user per week, fully-loaded hourly rate, and one-time implementation cost. The four hidden defaults applied automatically: an adoption haircut (only some users actually use the tool), a productivity capture rate (saved hours don’t become recovered hours 1:1), an inference scale-up factor (production usage costs more than pilot), and a 3-year amortization on the one-time implementation. You can override any of these in the advanced settings.
Why does the calculator default to a 60% adoption rate?
Field data from Microsoft 365 Copilot deployments and similar enterprise rollouts consistently shows 50–70% sustained adoption after the initial enthusiasm fades, with 60% being the median across non-developer knowledge worker rollouts. Developer-tool adoption can be higher (75–85%); general-purpose AI assistants for non-technical users can be lower (35–50%). If your pilot shows 90% adoption, that’s pilot novelty, not steady state.
Why does the calculator apply a 50% productivity capture rate?
A developer who writes code 40% faster does not ship 40% more product. The bottleneck moves to review, testing, deployment, planning. A support agent who handles 30% more tickets does not necessarily reduce headcount unless the organization decides to. Saved hours convert to recovered value at roughly half the rate the time-savings number suggests, on average. Industries vary, and you can adjust this. Setting it to 100% means assuming every saved hour becomes captured value, which is rare.
What’s a good payback period for an AI program?
Under 12 months is strong. 12–18 months is reasonable for non-trivial deployments. Over 24 months means the business case depends on assumptions that probably won’t hold; the foundation model market changes too fast. If your model says 36 months, kill the project and find a use case where the math is more forgiving.
How accurate is this calculator?
It’s a first-pass screen, not a board-grade business case. It’s good for comparing scenarios, killing obviously-bad business cases, and surfacing the assumptions that drive the result. For a board-grade case you need use-case-specific accuracy data, real adoption data from a comparable deployment, and a finance-modeled cost line for inference at production scale. See the AI business case template for the full structure.
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Thomas Prommer
Thomas Prommer Technology Executive — CTO/CIO/CTAIO

These salary reports are built on firsthand hiring experience across 20+ years of engineering leadership (adidas, $9B platform, 500+ engineers) and a proprietary network of 200+ executive recruiters and headhunters who share placement data with us directly. As a top-1% expert on institutional investor networks, I've conducted 200+ technical due diligence consultations for PE/VC firms including Blackstone, Bain Capital, and Berenberg — work that requires current, accurate compensation benchmarks across every seniority level. Our team cross-references recruiter data with BLS statistics, job board salary disclosures, and executive compensation surveys to produce ranges you can actually negotiate with.

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