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Estimate the payback, anchored to real cases.

Every input range is anchored to a published case in the source database — click a chip to see the study behind it. Toggle between labor-savings, revenue-uplift, and combined modes; every output is cited.

Organization Profile

$
$

Microsoft 365 Copilot ≈ $30/user/mo · ChatGPT Enterprise ≈ $60/user/mo

18%
1%25%50%
Set from evidence

Projected Results

Projected Annual Savings
$1,440,000
Annual AI Tool Cost
$36,000
Net Annual Benefit
$1,404,000
ROI
3900%
Payback Period
<1 month

Evidence-bounded savings range

14%–30% per published cases
Conservative
$1,120,000
@ 14%
Expected
$1,440,000
@ 18%
Optimistic
$2,400,000
@ 30%

Industry Benchmark: Technology / Software

ROI Range
18% - 24%
Time to ROI
12-18 months
Cases Studied
6
Confidence
Low-Moderate

Moderate sample size. Higher data maturity and talent availability drive faster ROI.

Sources informing this estimate

Cases from the source database that anchor the efficiency band for Technology / Software.

  • Fortune 500 Customer Service ProviderCASE-014NBER · 2023

    14% avg productivity (NBER, n=5,179 agents)

    14% productivity increase; 34% improvement for novice workers

    Source
  • ZoomInfoCASE-032ZoomInfo Engineering · 2024

    20% median time reduction (ZoomInfo Copilot)

    33% suggestion acceptance rate; 20% line acceptance rate; 90% of developers report time savings (median 20%); 72% developer satisfaction

    Source
  • AirbnbCASE-031Airbnb Engineering · 2024

    97% automation rate, ~1.5 yrs of eng work in 6 wks (Airbnb)

    3500 test files migrated in 6 weeks; 97% automation rate

    Source
Browse the full source database
How these numbers are calculated

Annual savings = employees × avg salary × efficiency gain

Annual cost = employees × monthly cost × 12

Net benefit = annual savings − annual cost

ROI = net benefit ÷ annual cost

Payback (months) = annual cost ÷ net benefit × 12

Efficiency-gain bands are derived from the cases listed above, not modelled or extrapolated. Savings translate a time-saving to dollars at the user's loaded salary — this is an upper bound; only a fraction of saved time typically converts to redeployable capacity.

Figures are projections from published case studies. Actual results vary materially with implementation quality, workforce adoption, and use-case fit — Gartner forecasts 30% of GenAI projects abandoned after POC by end of 2025. Use the conservative figure for board planning.

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