Public Models / Fictional example
Fictional scenario. The company is invented and every input is an illustrative assumption, not a reported figure or outcome.
These are hypothetical scenarios, not verified AI deployments or observed financial returns. Any resemblance to a real company is unintended.
In the inputs below, every input has the mark “Illustrative, not validated”.
Fictional example: health-tech SaaS
Fictional scenario: invented company, all inputs illustrative assumptions. Not a reported outcome. Regulated work: only a quarter of engineering work is eligible, and implementation includes compliance review.
Report “Fictional example: health-tech SaaS”
Incremental AI cost: US$150,848.00 per year. These are two different valuations of the same output gain. Do not add them together. Neither value is a guaranteed minimum or maximum.
Capacity-based value
The cost of the engineer time used for more work. It is not a cash saving.
US$301,050.00 per year · ROI 99.57%
Revenue-linked value
A hypothetical contribution scenario. It is not a sales forecast.
US$137,454.55 per year · ROI -8.88%
Inputs - company
Item | Value | Source |
|---|---|---|
| Revenue, last completed financial year | US$70,000,000.00 | Illustrative, not validated |
| Business-as-usual revenue growth | 20.00% | Illustrative, not validated |
| Incremental contribution margin | 70.00% | Illustrative, not validated |
Inputs - workforce
Item | Value | Source |
|---|---|---|
| Engineering FTE (annual average) | 120 | Illustrative, not validated |
| Licensed seats (annual average) | 96 | — |
Inputs - AI tool costs
Item | Value | Source |
|---|---|---|
| Engineers with a paid seat | 80.00% | — |
| Seat price per month | US$39.00 | Illustrative, not validated |
| Extra usage per active engineer per month | US$30.00 | Illustrative, not validated |
| Implementation, training and governance (year one) | US$80,000.00 | Illustrative, not validated |
Inputs - productivity and adoption
Item | Value | Source |
|---|---|---|
| Engineering work eligible for AI | 25.00% | Illustrative, not validated |
| Net time saved on eligible work | 15.00% | Illustrative, not validated |
| Active engineers (adoption) | 60.00% | Illustrative, not validated |
| Released time used for extra output (redeployment) | 50.00% | Illustrative, not validated |
Inputs - commercial response
Item | Value | Source |
|---|---|---|
| Engineering commercial response | 40.00% | Illustrative, not validated |
| Commercial diffusion loss | 50.00% | Illustrative, not validated |
| Downside commercial transmission | 100.00% | Illustrative, not validated |
Workforce cohorts
Location | Seniority | FTE | Fully loaded cost (original currency) | FX rate | Rate date | FX source | Loaded cost (USD) | Capacity benefit |
|---|---|---|---|---|---|---|---|---|
| US | junior | 24 | US$140,000.00 | 1 | — | reporting currency (no conversion) | US$140,000.00 | US$37,800.00 |
| US | mid | 60 | US$210,000.00 | 1 | — | reporting currency (no conversion) | US$210,000.00 | US$141,750.00 |
| US | senior | 36 | US$300,000.00 | 1 | — | reporting currency (no conversion) | US$300,000.00 | US$121,500.00 |
Incremental annual AI cost
Item | Value |
|---|---|
| Licences | US$44,928.00 |
| Additional usage | US$25,920.00 |
| Implementation, training and governance | US$80,000.00 |
| Total incremental AI cost | US$150,848.00 |
Productivity chain
Item | Value |
|---|---|
| Active engineers (annual average) | 72 |
| Released time per active engineer | 3.7500% |
| Theoretical output uplift per active engineer | 3.8961% |
| Released baseline time, whole workforce | 2.2500% |
| Realised workforce output uplift | 1.1688% |
| Adverse output change (stress channel) | 0.0000% |
Capacity-based value
Item | Value |
|---|---|
| Capacity benefit (cost-equivalent of redeployed time)Not a cash or payroll saving | US$301,050.00 |
| Net value after AI cost | US$150,202.00 |
| Capacity economic ROI | 99.57% |
| Benefit / cost | 2.00× |
Revenue-linked contribution value (scenario)
Item | Value |
|---|---|
| Forecast BAU revenue (no AI) | US$84,000,000.00 |
| Incremental revenue scenarioScenario, not a sales forecast | US$196,363.64 |
| Incremental contribution benefit | US$137,454.55 |
| Net value after AI cost | -US$13,393.45 |
| Commercial scenario ROI | -8.88% |
| Benefit / cost | 0.91× |
| Effective commercial response e × (1 − d) | 20.0000% |
Diagnostics (calculated by the model, not audited)
Item | Value |
|---|---|
| Engineering-addressable contribution per FTE | US$196,000.00 |
| BAU revenue at which the two valuations are equalNeither valuation is a fixed minimum or maximum | US$183,975,000.00 |
Sensitivity - net time saved on eligible work ±10 points
Scenario | Shift | Capacity benefit | Capacity ROI | Commercial benefit | Commercial ROI |
|---|---|---|---|---|---|
| Conservative | -10% | US$100,350.00 | -33.48% | US$44,658.23 | -70.40% |
| Base | 0% | US$301,050.00 | 99.57% | US$137,454.55 | -8.88% |
| Optimistic | 10% | US$501,750.00 | 232.62% | US$235,200.00 | 55.92% |
Reproducibility
Reproduced exactly by the current engine (0.1.3). Input hash 04c16fc9e66e3188…, output hash 00a7950b9c1efaaf….
- 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.