Public Models / Curated example
Illustrative ROI scenario. Financial scale is rounded from public-company reports; all AI adoption, workforce configuration, contribution margin, tool spending, productivity and realised benefits are modelled assumptions, not reported outcomes.
These are hypothetical scenarios, not verified AI deployments or observed financial returns. Not endorsed by, or describing, any company. Pseudonymous names do not guarantee anonymity.
In the inputs below, revenue is marked “Rounded from reported figures”; everything else is marked “Illustrative, not validated”.
Software Company A
Illustrative scenario: a software company with revenue rounded from public reports and modelled AI-adoption assumptions. Not a reported outcome.
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Report “Software Company A”
Incremental AI cost: US$1,762,000.00 per year. Two alternative valuations of the same output gain — never add them; neither is a guaranteed minimum or maximum.
Capacity-based value
Cost-equivalent of redeployed engineer time — not a cash saving.
US$19,551,375.00 per year · ROI 1,009.61%
Revenue-linked value
Hypothetical contribution scenario — not a sales forecast.
US$31,020,000.00 per year · ROI 1,660.50%
Inputs — company
Item | Value | Source |
|---|---|---|
| Revenue, last completed financial year | US$4,700,000,000.00 | Rounded from reported figures |
| Business-as-usual revenue growth | 20.00% | Illustrative, not validated |
| Incremental contribution margin | 60.00% | Illustrative, not validated |
Inputs — workforce
Item | Value | Source |
|---|---|---|
| Engineering FTE (annual average) | 1,800 | Illustrative, not validated |
| Licensed seats (annual average) | 1,260 | — |
Inputs — AI tool costs
Item | Value | Source |
|---|---|---|
| Engineers with a paid seat | 70.00% | — |
| Seat price per month | US$40.00 | Illustrative, not validated |
| Extra usage per active engineer per month | US$70.00 | Illustrative, not validated |
| Implementation, training and governance (year one) | US$250,000.00 | Illustrative, not validated |
Inputs — productivity and adoption
Item | Value | Source |
|---|---|---|
| Engineering work eligible for AI | 50.00% | Illustrative, not validated |
| Net time saved on eligible work | 20.00% | Illustrative, not validated |
| Engineers actively achieving the effect | 60.00% | Illustrative, not validated |
| Released time redeployed into extra output | 50.00% | Illustrative, not validated |
Inputs — commercial response
Item | Value | Source |
|---|---|---|
| Engineering commercial response | 55.00% | Illustrative, not validated |
| Downstream commercial loss | 50.00% | Illustrative, not validated |
| Downside commercial transmission | 100.00% | Illustrative, not validated |
Workforce cohorts
Location | Seniority | FTE | Original loaded cost | FX rate | Rate date | FX source | Loaded cost (USD) | Capacity benefit |
|---|---|---|---|---|---|---|---|---|
| US | junior | 162 | US$260,000.00 | 1 | — | reporting currency (no conversion) | US$260,000.00 | US$1,263,600.00 |
| US | mid | 540 | US$410,000.00 | 1 | — | reporting currency (no conversion) | US$410,000.00 | US$6,642,000.00 |
| US | senior | 378 | US$650,000.00 | 1 | — | reporting currency (no conversion) | US$650,000.00 | US$7,371,000.00 |
| CA | junior | 27 | US$150,000.00 | 1 | — | reporting currency (no conversion) | US$150,000.00 | US$121,500.00 |
| CA | mid | 90 | US$230,000.00 | 1 | — | reporting currency (no conversion) | US$230,000.00 | US$621,000.00 |
| CA | senior | 63 | US$350,000.00 | 1 | — | reporting currency (no conversion) | US$350,000.00 | US$661,500.00 |
| DE | junior | 27 | US$140,000.00 | 1 | — | reporting currency (no conversion) | US$140,000.00 | US$113,400.00 |
| DE | mid | 90 | US$220,000.00 | 1 | — | reporting currency (no conversion) | US$220,000.00 | US$594,000.00 |
| DE | senior | 63 | US$335,000.00 | 1 | — | reporting currency (no conversion) | US$335,000.00 | US$633,150.00 |
| PL | junior | 40.5 | US$80,000.00 | 1 | — | reporting currency (no conversion) | US$80,000.00 | US$97,200.00 |
| PL | mid | 135 | US$135,000.00 | 1 | — | reporting currency (no conversion) | US$135,000.00 | US$546,750.00 |
| PL | senior | 94.5 | US$200,000.00 | 1 | — | reporting currency (no conversion) | US$200,000.00 | US$567,000.00 |
| CR | junior | 13.5 | US$65,000.00 | 1 | — | reporting currency (no conversion) | US$65,000.00 | US$26,325.00 |
| CR | mid | 45 | US$105,000.00 | 1 | — | reporting currency (no conversion) | US$105,000.00 | US$141,750.00 |
| CR | senior | 31.5 | US$160,000.00 | 1 | — | reporting currency (no conversion) | US$160,000.00 | US$151,200.00 |
Incremental annual AI cost
Item | Value |
|---|---|
| Licences | US$604,800.00 |
| Additional usage | US$907,200.00 |
| Implementation, training and governance | US$250,000.00 |
| Total incremental AI cost | US$1,762,000.00 |
Productivity chain
Item | Value |
|---|---|
| Active engineers (annual average) | 1,080 |
| Released time per active engineer | 10.0000% |
| Theoretical output uplift per active engineer | 11.1111% |
| Released baseline time, whole workforce | 6.0000% |
| Realised workforce output uplift | 3.3333% |
| Adverse output change (stress channel) | 0.0000% |
Capacity-based economic value
Item | Value |
|---|---|
| Capacity benefit (cost-equivalent of redeployed time)Not a cash or payroll saving | US$19,551,375.00 |
| Net value after AI cost | US$17,789,375.00 |
| Capacity economic ROI | 1,009.61% |
| Benefit / cost | 11.10× |
Revenue-linked contribution value (scenario)
Item | Value |
|---|---|
| Forecast BAU revenue (no AI) | US$5,640,000,000.00 |
| Incremental revenue scenarioScenario, not a sales forecast | US$51,700,000.00 |
| Incremental contribution benefit | US$31,020,000.00 |
| Net value after AI cost | US$29,258,000.00 |
| Commercial scenario ROI | 1,660.50% |
| Benefit / cost | 17.60× |
| Effective commercial response e × (1 − d) | 27.5000% |
Diagnostics (model-derived, not audited figures)
Item | Value |
|---|---|
| Engineering-addressable contribution per FTE | US$1,034,000.00 |
| BAU revenue at which the two valuations are equalShows that neither method is a fixed minimum or maximum | US$3,554,795,454.55 |
Sensitivity — net time saved on eligible work ±10 points
Scenario | Shift | Capacity benefit | Capacity ROI | Commercial benefit | Commercial ROI |
|---|---|---|---|---|---|
| Conservative | -10% | US$9,775,687.50 | 454.81% | US$14,693,684.21 | 733.92% |
| Base | 0% | US$19,551,375.00 | 1,009.61% | US$31,020,000.00 | 1,660.50% |
| Optimistic | 10% | US$29,327,062.50 | 1,564.42% | US$49,267,058.82 | 2,696.09% |
Reproducibility
Reproduced exactly by the current engine (0.1.3). Input hash 4ab0800845b14c58…, output hash b9ffbbf7f64eb874….
- Capacity-based and revenue-linked values are two alternative valuations of the same output gain. Never add them.
- Neither valuation is a guaranteed minimum or maximum; which one is higher depends on the inputs.
- Capacity value is the cost-equivalent of redeployed engineer time, not a payroll or cash saving.
- Revenue-linked value is a hypothetical scenario based on stated assumptions, not a causal sales forecast.
- Illustrative estimate — not an observed financial result until supported by evidence.
- Dodone Model v1 is a controlled draft for independent methodological audit; it has not been audited.
- One 12-month period; a new AI tool compared with no tool. Not valid for replacing an existing AI tool.