AI ROI methodology for engineering teams

What does Weave measure?

Weave's AI ROI methodology connects AI usage and attributed spending with engineering output. Its output unit is an estimated expert hour: the amount of work an expert engineer would need to complete a pull request. Cost per output hour divides attributed AI spending by those output units for the same measured group and time period.

This ratio compares attributed AI spending with measured output for a group and period. By itself, it does not show incremental revenue, hours saved, or a causal return on investment. Pair it with adoption, cost fidelity, quality, and delivery data when evaluating impact.

How is engineering output calculated?

Code Intelligence analyzes the source code changes in a pull request. Silk 1 considers intent, complexity, risk, and dependencies across files, and provides reasoning for its estimate. The scoring scale is expressed in expert hours: a score of 3.0 represents an estimated three hours of expert effort.

Silk 1 expresses pull-request output in estimated expert hours so teams can compare work on a complexity-aware scale. An expert hour is not an engineer's recorded work time or an hour saved by AI, and it does not value the business result. PR counts and lines changed provide volume context; Silk 1 adds intent, complexity, risk, and cross-file dependencies. Review the underlying PR and Silk 1's reasoning when a score appears inconsistent with the work.

What costs and attribution signals are available?

Token Intelligence brings together provider billing, tool-usage telemetry, and configured subscription costs. For supported integrations, Weave reports spend by engineer and team, with model detail where the source provides it. Cost-source and fidelity labels distinguish vendor-billed actuals, estimates, modeled or client-reported amounts, and allocated seat costs.

Per-engineer cost reporting includes Claude Code, Claude.ai, Cursor, GitHub Copilot, Codex, OpenAI direct API, Devin, Windsurf, Amazon Q, Sourcegraph, Augment, Kiro, Firebender, and Oh My Pi. Modal reports metered spend at the organization level. Cline and Gemini CLI do not provide native per-user dollar costs; teams can configure and assign subscription costs in Weave when they need to include those plans.

Weave combines AI usage with code output, quality, and delivery measures. AI contribution can come from three types of evidence:

Direct, inferred, and estimated signals answer different questions. Weave labels their source so teams can compare usage and spend alongside output, quality, and delivery measures. These signals do not show that every prompt can be tied to a specific merged change or that every tool bill can be assigned to an engineer. Shared branches, multiple tools on one task, and missing telemetry can limit a comparison.

How does the public Weave Index calculate cost per output?

The Weave Index publishes two related monthly ratios. Both use only engineers with attributed AI spend and merged code output in the same month. Ignored PRs are excluded. Spend that cannot be attributed to a person, such as organization-level overages, is excluded from these ratios.

These are ratios of totals, not averages of each engineer's ratio. The second is the inverse of the first when calculated from the same unrounded inputs. The Index's name “return on token spend” refers to output per dollar. It is not a financial ROI percentage and does not show that AI caused the measured output.

Interpret missing values through data coverage: no observed spend may reflect disconnected billing, while no observed output may reflect repositories outside the connected scope. Confirm coverage before comparing team-level results. The Index's cost-per-employee and output-per-employee series use a broader population, so dividing those displayed averages does not recreate the matched-cohort ratio.

The public matched cohort contains 2,800 engineers in January 2026 and 9,151 in September 2026. It is a changing sample of Weave customer telemetry, not a randomized industry sample. Use the date, cohort definition, exclusions, and available sample information when citing a result. A total company AI budget can be higher than the attributed spend included in this series.

Worked example: cost per output hour

Hypothetical example, not a customer result or benchmark. Suppose a measured group has $1,200 in attributed AI costs and 300 expert hours of merged output during one month. The same group and period must supply both totals.

• Cost per output hour: $1,200 ÷ 300 = $4.00 per expert hour.
• Output per dollar: 300 ÷ $1,200 = 0.25 expert hours per dollar.

If another $200 of organization-level charges cannot be attributed to people, show it alongside the matched-cohort result. A separate full-budget calculation would use $1,400 and yield $4.67 per output hour. Label the two calculations clearly. Neither proves that AI caused the measured output.

Which quality signals belong beside the cost ratio?

Engineering Intelligence reports review activity, review cycles, reverts, code turnover, and delivery measures. A lower cost per output hour can still accompany defects, rework, or production failures, so review these signals beside the cost ratio.

Keep quality measures explicit. The Index cost-per-output formula does not apply a hidden quality multiplier. For a team evaluation, define the review or post-merge observation window and show reverts, defects, and rework beside the output total. Recent PRs may not yet have enough history to assess maintainability or production impact.

How should a team evaluate AI ROI?

1. Define the decision, such as renewing a tool or changing model use, and choose a consistent time period, repository set, and team.
2. Reconcile subscription and usage charges with billing records. Avoid charging the same included usage twice. Record allocations and costs that cannot be attributed.
3. Check identity mapping and tool coverage. Show how much spending and output is observed, and which contribution labels are direct, inferred, or estimated.
4. Compare similar work before and after adoption, or use a comparable group. Account for staffing, project scope, PR size, and workflow changes.
5. Inspect representative PRs and their quality outcomes. Separate measured output changes from assumptions about time saved or monetary value.

For a financial ROI percentage, use (estimated incremental benefit − total incremental cost) ÷ total incremental cost and disclose the baseline, benefit valuation, and assumptions. The Index provides cost and output inputs. Finance teams must establish incremental value. Estimated expert hours are neither recorded time nor payroll savings.

Choosing a platform? Compare AI ROI measurement tools for engineering teams.

Tool-specific measurement guides

• Claude Code: adoption, costs and engineering outcomes
• Cursor: usage, cost and code attribution
• Codex: usage, spending and output measurement

Sources and citation

This methodology describes the public Weave Index, Token Intelligence, Code Intelligence, and Engineering Intelligence. Cost coverage was updated on October 6, 2026 against the current AI tool cost guide. Integrations and account configurations can expose different data.

When citing this page, use: Weave, “AI ROI measurement methodology,” updated October 6, 2026, and link to this URL. For a numeric benchmark, cite its individual Index metric page and observation period as well.