The Tool Desk
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Set the measurement boundary first
Choose a team, project, or portfolio and a fixed observation period. Define which tools and workflows count as AI-assisted, which costs are in scope, and how you will attribute shared costs. Apply those same rules to the baseline and assisted periods.
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Record the adoption period separately from any later steady-state period, if you have enough data to report both. Keep the initial learning period in the total: DORA describes this temporary productivity dip as a “tuition cost,” not a cost to remove from the calculation.
Build a fully loaded cost ledger
Use this accounting identity for the period you selected:
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Total AI-assisted development cost = direct tool and usage spend + infrastructure + training and rollout + loaded labor for AI-related workflow work + attributable operational and rework costs.
Separate measured expenses from estimates, and record the source and allocation rule for each line. DORA’s ROI calculator includes staff size and loaded salary, license and additional AI costs, infrastructure, training, net time saved, delivery targets, change failure rate, recovery time, and a modeled temporary productivity drop. That list can help check coverage; it is not a universal accounting standard.
| Cost category | Include | How to account for it |
|---|---|---|
| Tools and usage | Licenses or subscriptions, API or token usage, and platform or integration charges. | Use actual charges for the period; separate recurring fees from variable usage. |
| Infrastructure | Additional infrastructure attributable to the AI-assisted workflow. | Use observed spend where available; document the allocation for shared systems. |
| Adoption and learning | Training, dedicated learning time, rollout and administration, workflow integration, and maintaining prompts, agents, or internal guidance. | Include labor and direct expenses. Keep estimated opportunity cost distinct from directly observed spend. |
| Human workflow labor | Task specification, prompting or orchestration, output checking, code review, correction or rewriting, security and compliance review, and coordination or waiting when it displaces other work. | Track time attributable to the workflow and convert hours with your organization’s loaded labor rate. |
| Quality and operations | Attributable defects, failed changes, incident response, recovery, and follow-on rework. | State the attribution rule and count only costs reasonably connected to the measured workflow. |
Use a fully loaded labor rate and explicit allocation rules. Do not count a developer hour once in a salary allocation and again as a separate hourly expense. If time diverted from other work is estimated rather than directly observed, report it as opportunity cost on its own line.
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A total-cost figure alone does not tell you whether the workflow is economical. Define an output unit that means accepted, production-ready work—for example, a completed issue or feature that satisfies the same acceptance criteria in both periods.
For a chosen period, calculate:
Cost per accepted outcome = total development cost for the period ÷ number of accepted outcomes in that period.
Use the same scope and quality bar in the numerator and denominator. Pair the result with end-to-end cycle time, review time, rework, change failures, recovery time, and throughput. Add customer or business outcomes only where you can make a credible connection to the work. Lines of code, suggestions accepted, or faster typing do not establish value by themselves.
Compare like with like
- Choose a comparison: Use a pre-adoption baseline or, when feasible, a contemporaneous group that has not yet adopted the workflow.
- Keep the work comparable: Stratify results by task type, developer experience, and AI-use intensity; distinguish completion, chat, and agent workflows where relevant.
- Log changes that could affect results: Record task mix, staffing, acceptance criteria, and other tooling changes rather than attributing every difference to AI.
- Report adoption and steady-state periods separately: Do not omit early learning costs simply because later results look different.
- Show uncertainty: Separate observed costs and outcomes from modeled assumptions, and test how the result changes when adoption, net time saved, training duration, failure rate, or recovery cost varies.
A simple before-and-after average can be misleading if developers choose easier or more AI-suitable tasks, enthusiastic users are overrepresented, or effort shifts from initial coding into review and rework. METR’s February 2026 study-design update describes selection effects in participants and submitted tasks, as well as time-reporting difficulties for some multi-agent users. METR called the follow-up a weak signal and said its central estimate was a poor proxy for real-world productivity impact.
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Keep cost, benefit, and ROI distinct
Maintain a cost ledger separately from any modeled benefit. If you calculate ROI, disclose the assumptions used to translate capacity, feature delivery, or downtime into financial value. DORA’s calculator warns: “Treat these calculations as a high-uncertainty estimate meant to spark a conversation rather than a rigid mathematical formula.” Use local data and sensitivity ranges rather than presenting one modeled result as a guaranteed return.
Best Value
DORA’s ROI material also identifies capacity recovered from unnecessary rework as one possible source of value. Count that as a benefit only when the team can show the capacity was actually recovered and explain how it was valued.
Use published findings as context, not a forecast
Published results describe particular populations, tasks, and measures. They can help explain why local measurement matters, but they do not establish the result your team should expect.
- METR’s early-2025 experiment reported that tasks took 19% longer with AI among experienced open-source developers in that study setting. Its February 2026 update discusses limitations in interpreting later measurements.
- DORA’s 2024 report summary, on a page updated April 13, 2026, reports that a 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. This is an association reported in that study, not a causal forecast for an individual team.
- DORA’s 2025 report describes AI as an amplifier of an organization’s existing strengths and weaknesses. Its worldwide research included nearly 5,000 technology professionals and more than 100 hours of qualitative data.
These findings use different settings and outcomes. DORA’s emphasis on the surrounding delivery system is a reason to track delivery quality and operational results alongside coding activity, rather than treating code generation as the whole workflow.
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