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: industrial software team at a manufacturer
Fictional scenario: invented company, all inputs illustrative assumptions. Not a reported outcome. An in-house team at a large manufacturer: engineering barely moves company revenue, so the capacity value is the main view.
Report “Fictional example: industrial software team at a manufacturer”
Incremental AI cost: US$91,104.00 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$150,120.00 per year · ROI 64.78%
Revenue-linked value
A hypothetical contribution scenario. It is not a sales forecast.
US$26,626.60 per year · ROI -70.77%
Inputs - company
Item | Value | Source |
|---|---|---|
| Revenue, last completed financial year | US$900,000,000.00 | Illustrative, not validated |
| Business-as-usual revenue growth | 3.00% | Illustrative, not validated |
| Incremental contribution margin | 25.00% | Illustrative, not validated |
Inputs - workforce
Item | Value | Source |
|---|---|---|
| Engineering FTE (annual average) | 80 | Illustrative, not validated |
| Licensed seats (annual average) | 48 | — |
Inputs - AI tool costs
Item | Value | Source |
|---|---|---|
| Engineers with a paid seat | 60.00% | — |
| Seat price per month | US$39.00 | Illustrative, not validated |
| Extra usage per active engineer per month | US$20.00 | Illustrative, not validated |
| Implementation, training and governance (year one) | US$60,000.00 | Illustrative, not validated |
Inputs - productivity and adoption
Item | Value | Source |
|---|---|---|
| Engineering work eligible for AI | 40.00% | Illustrative, not validated |
| Net time saved on eligible work | 15.00% | Illustrative, not validated |
| Active engineers (adoption) | 45.00% | Illustrative, not validated |
| Released time used for extra output (redeployment) | 50.00% | Illustrative, not validated |
Inputs - commercial response
Item | Value | Source |
|---|---|---|
| Engineering commercial response | 2.00% | Illustrative, not validated |
| Commercial diffusion loss | 60.00% | 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 |
|---|---|---|---|---|---|---|---|---|
| DE | junior | 12 | US$90,000.00 | 1 | — | reporting currency (no conversion) | US$90,000.00 | US$14,580.00 |
| DE | mid | 44 | US$130,000.00 | 1 | — | reporting currency (no conversion) | US$130,000.00 | US$77,220.00 |
| DE | senior | 24 | US$180,000.00 | 1 | — | reporting currency (no conversion) | US$180,000.00 | US$58,320.00 |
Incremental annual AI cost
Item | Value |
|---|---|
| Licences | US$22,464.00 |
| Additional usage | US$8,640.00 |
| Implementation, training and governance | US$60,000.00 |
| Total incremental AI cost | US$91,104.00 |
Productivity chain
Item | Value |
|---|---|
| Active engineers (annual average) | 36 |
| Released time per active engineer | 6.0000% |
| Theoretical output uplift per active engineer | 6.3830% |
| Released baseline time, whole workforce | 2.7000% |
| Realised workforce output uplift | 1.4362% |
| 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$150,120.00 |
| Net value after AI cost | US$59,016.00 |
| Capacity economic ROI | 64.78% |
| Benefit / cost | 1.65× |
Revenue-linked contribution value (scenario)
Item | Value |
|---|---|
| Forecast BAU revenue (no AI) | US$927,000,000.00 |
| Incremental revenue scenarioScenario, not a sales forecast | US$106,506.38 |
| Incremental contribution benefit | US$26,626.60 |
| Net value after AI cost | -US$64,477.40 |
| Commercial scenario ROI | -70.77% |
| Benefit / cost | 0.29× |
| Effective commercial response e × (1 − d) | 0.8000% |
Diagnostics (calculated by the model, not audited)
Item | Value |
|---|---|
| Engineering-addressable contribution per FTE | US$57,937.50 |
| BAU revenue at which the two valuations are equalNeither valuation is a fixed minimum or maximum | US$5,226,400,000.00 |
Sensitivity - net time saved on eligible work ±10 points
Scenario | Shift | Capacity benefit | Capacity ROI | Commercial benefit | Commercial ROI |
|---|---|---|---|---|---|
| Conservative | -10% | US$50,040.00 | -45.07% | US$8,513.27 | -90.66% |
| Base | 0% | US$150,120.00 | 64.78% | US$26,626.60 | -70.77% |
| Optimistic | 10% | US$250,200.00 | 174.63% | US$46,350.00 | -49.12% |
Reproducibility
Reproduced exactly by the current engine (0.1.3). Input hash 9870523300b309dc…, output hash e1a86980aa79478b….
- 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.