Dora ROI: how to calculate the effect of AI-assisted development without magic (Category AI4SDLC)
Deal with the new report DORA and Google Cloud[The ROI of AI-assisted Software Development\](https://dora.dev/ai/roi/report/). He's good at continuing the last Dora. 2025 "State of AI-assisted Software Development" handlerThe main thing was that AI works as an amplifier of an engineering system, and the effect depends on seven possibilities. (I'm sorry. handler separately). The main point of the previous report was that strong teams from AI get more speed, weak teams get more chaos. And the new report proposes to evaluate changes in the engineering system not only by engineering metrics, but also in the language of business case, budget and CFO.
The main idea of the report is simple: return on investment (ROI) From AI-assisted development can not be considered simplified in the form of: “bought licenses, received savings”. The authors suggest looking at the entire software supply system. The formula is standard: ROI = (Value - Investment) / Investment. But what's more interesting is how they say value and investment (By the way, the authors even give calculatorWhere you can try all these calculations on yourself.).
Value. (value) They are evaluated by three components.
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1Released engineering capacity**
Important: DORA does not explicitly advise turning this into a strategy to cut people. The logic is different: if AI saves a developer part of the time, it’s not a “minus developer” but a “minus developer.” headcount reinvestment capacity Capacity that can be rolled into new features, product improvement, debt reduction and innovation.
2Additional revenue from faster delivery of successful features (Those that improve product metrics)
Here the authors are careful: not every feature brings money, so the calculator has an idea success rate and a conservative estimate of revenue impact.
3Stability Effect: Downtime, Change Failure Rate and Recovery Time after Failed Deployment
Here comes the unpleasant part of the model. AI can increase bandwidth, but if it increases the frequency of failures (change fail rate, CFR)Part of the economic effect is being eaten. In the DORA demo calculator, this block even gives a negative contribution.
The authors also divide the costs into separate components. Authors consider direct costs: subscriptions, API/token costs, infrastructure, training and change management
- And the top is added.
J-curve cost- the cost of temporary drawdown at the implementation stage.
J-curve This is a useful concept in this report that managers often forget. First, the team spends time learning. (learning curve)then pays verification tax A tax on the verification of AI-output, then rebuilds the pipeline, review, testing and delivery rules. The system’s first response might not be “we’ve accelerated,” but “we’ve got more code, more reviews, and more noise.” According to DORA, this is not necessarily a tool failure – it is the cost of transformation if it is pre-budgeted and metrics.
Next, the authors draw some plan for how to implement AI.
1First you need a context layer. Quality Internal Developer Platform, Normal Documentation, Healthy Data Ecosystems, Machine Readable Standards, and Clear APIs 2Next Next post: Human-in-the-loop Trust in AI, context engineering, training people to check and direct agents 3️ And only after that, it makes sense to look at leading indicators. Authors recommend standard DORA metrics (right here told framework 2026 the fifth metric year). And in addition, the experiment frequency metric, which they treat almost as financial: AI reduces the cost of small product experiments. (What they compare to financial options). You can quickly collect a few options, test them on users and not invest early in a large but untested idea.
The new DORA ROI report is useful in switching from engineering to business. Here we do not just “implement AI in development”, but change the engineering system, consider tax verification, monitor instability tax and understand where to reinvest the released capacity.
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