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: fintech start-up
Fictional scenario: invented company, all inputs illustrative assumptions. Not a reported outcome. A small, fast-growing team where almost everyone uses the tool and new features reach customers quickly.
Report “Fictional example: fintech start-up”
Incremental AI cost: US$25,088.00 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$191,787.75 per year · ROI 664.46%
Revenue-linked value
A hypothetical contribution scenario. It is not a sales forecast.
US$372,050.61 per year · ROI 1,382.98%
Inputs - company
Item | Value | Source |
|---|---|---|
| Revenue, last completed financial year | US$6,000,000.00 | Illustrative, not validated |
| Business-as-usual revenue growth | 80.00% | Illustrative, not validated |
| Incremental contribution margin | 70.00% | Illustrative, not validated |
Inputs - workforce
Item | Value | Source |
|---|---|---|
| Engineering FTE (annual average) | 18 | Illustrative, not validated |
| Licensed seats (annual average) | 18 | — |
Inputs - AI tool costs
Item | Value | Source |
|---|---|---|
| Engineers with a paid seat | 100.00% | — |
| Seat price per month | US$39.00 | Illustrative, not validated |
| Extra usage per active engineer per month | US$60.00 | Illustrative, not validated |
| Implementation, training and governance (year one) | US$5,000.00 | Illustrative, not validated |
Inputs - productivity and adoption
Item | Value | Source |
|---|---|---|
| Engineering work eligible for AI | 55.00% | Illustrative, not validated |
| Net time saved on eligible work | 25.00% | Illustrative, not validated |
| Active engineers (adoption) | 90.00% | Illustrative, not validated |
| Released time used for extra output (redeployment) | 70.00% | Illustrative, not validated |
Inputs - commercial response
Item | Value | Source |
|---|---|---|
| Engineering commercial response | 70.00% | Illustrative, not validated |
| Commercial diffusion loss | 30.00% | 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 |
|---|---|---|---|---|---|---|---|---|
| UK | junior | 5.4 | US$85,000.00 | 1 | — | reporting currency (no conversion) | US$85,000.00 | US$39,760.88 |
| UK | mid | 9 | US$125,000.00 | 1 | — | reporting currency (no conversion) | US$125,000.00 | US$97,453.13 |
| UK | senior | 3.6 | US$175,000.00 | 1 | — | reporting currency (no conversion) | US$175,000.00 | US$54,573.75 |
Incremental annual AI cost
Item | Value |
|---|---|
| Licences | US$8,424.00 |
| Additional usage | US$11,664.00 |
| Implementation, training and governance | US$5,000.00 |
| Total incremental AI cost | US$25,088.00 |
Productivity chain
Item | Value |
|---|---|
| Active engineers (annual average) | 16.2 |
| Released time per active engineer | 13.7500% |
| Theoretical output uplift per active engineer | 15.9420% |
| Released baseline time, whole workforce | 12.3750% |
| Realised workforce output uplift | 10.0435% |
| 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$191,787.75 |
| Net value after AI cost | US$166,699.75 |
| Capacity economic ROI | 664.46% |
| Benefit / cost | 7.64× |
Revenue-linked contribution value (scenario)
Item | Value |
|---|---|
| Forecast BAU revenue (no AI) | US$10,800,000.00 |
| Incremental revenue scenarioScenario, not a sales forecast | US$531,500.87 |
| Incremental contribution benefit | US$372,050.61 |
| Net value after AI cost | US$346,962.61 |
| Commercial scenario ROI | 1,382.98% |
| Benefit / cost | 14.83× |
| Effective commercial response e × (1 − d) | 49.0000% |
Diagnostics (calculated by the model, not audited)
Item | Value |
|---|---|
| Engineering-addressable contribution per FTE | US$294,000.00 |
| BAU revenue at which the two valuations are equalNeither valuation is a fixed minimum or maximum | US$5,567,274.05 |
Sensitivity - net time saved on eligible work ±10 points
Scenario | Shift | Capacity benefit | Capacity ROI | Commercial benefit | Commercial ROI |
|---|---|---|---|---|---|
| Conservative | -10% | US$115,072.65 | 358.68% | US$209,848.71 | 736.45% |
| Base | 0% | US$191,787.75 | 664.46% | US$372,050.61 | 1,382.98% |
| Optimistic | 10% | US$268,502.85 | 970.24% | US$556,348.12 | 2,117.59% |
Reproducibility
Reproduced exactly by the current engine (0.1.3). Input hash 47d3b6972e6ad520…, output hash 72625e5f01436c4b….
- 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.