Written by: Mark Hull, Co-Founder and CEO, Exceeds AI | Last updated: July 13, 2026
Key Takeaways
- A GitHub Copilot board report connects AI tool usage to engineering outcomes like cycle time, rework, and ROI using commit-level data instead of adoption metrics alone.
- Provenance-backed platforms such as Exceeds Ink attribute AI-generated lines across multiple tools, giving leaders defensible numbers for board presentations.
- Key metrics in the template show 58% AI-touched commits, an 18% productivity lift, and a 24% cycle-time reduction for AI-assisted PRs, balanced against higher review burden and flagged rework rates.
- Longitudinal tracking of AI code over 30, 60, and 90 days exposes technical debt patterns that metadata dashboards miss, so organizations can set governance thresholds and trigger coaching when churn exceeds 5%.
- Exceeds AI delivers the commit-level visibility and multi-tool attribution required to turn estimates into reproducible board reports, so you can start your free pilot today.
Copy-Paste Markdown Template for Your Next Board Meeting
The template below is self-contained and ready to populate. Every placeholder has been replaced with realistic figures drawn from commit-level provenance data. GitHub Copilot Analytics surfaces acceptance rates and suggested-line counts, but 84% of developers now use or plan to use AI tools, so any single-tool dashboard leaves most AI activity unattributed. The sections that follow explain each block and the data sources required to populate it with defensible numbers rather than estimates.
The template highlights three patterns that separate provenance-backed reports from metadata dashboards: commit-level attribution with concrete PR examples, interaction-mode breakdowns that show how engineers use AI tools, and longitudinal debt tracking that extends well beyond merge time.
# GitHub Copilot Board Report — Q2 2026 **Prepared by:** VP of Engineering **Period:** April 1 – June 30, 2026 **Provenance layer:** Exceeds Ink (refs/notes/exceeds-ink, schema authorship/3.0.0) --- ## Executive Summary - AI-touched commits: 58% of all commits (3,412 of 5,883) - Verified productivity lift (AI vs. human PRs, same engineers): +18% - Cycle time, AI-assisted PRs: 3.9 days vs. 5.1 days human-only (−24%) - 30-day rework rate, AI-touched lines: 6.2% vs. 4.1% human (flagged) - Post-merge incident rate, AI-touched modules: 1.1 per 100 PRs vs. 0.8 human - Net ROI estimate (time saved minus rework cost): $340K annualized - Token spend, Q2: $48,200 (GitHub Copilot $31,400 + Cursor $16,800) --- ## Adoption & Utilization ### Active Users - Monthly active AI users: 247 of 300 engineers (82%) - Daily active users: 189 (63%) - Tool breakdown: GitHub Copilot 71%, Cursor 41%, Claude Code 18% (engineers use multiple tools; percentages sum >100%) ### Interaction Mode Distribution (Exceeds Ink) - Inline autocomplete: 54% of AI sessions - Chat / ask mode: 28% - Agent mode: 18% (up from 9% in Q1) ### Commit-Level Attribution - PR #1523: 623 of 847 lines AI-generated (Cursor, agent mode) - PR #1487: 211 of 340 lines AI-generated (GitHub Copilot, inline) - PR #1601: 0 AI lines — human-only baseline for comparison --- ## Engineering & ROI Impact ### Cycle Time - AI-assisted PRs: median 3.9 days first commit → merge - Human-only PRs: median 5.1 days - Delta: −1.2 days (−24%), statistically significant at p < 0.01 ### Throughput - Total PRs merged: 1,847 (+31% vs. Q2 2025) - AI-assisted PRs: 1,072 (58%) - Median PR size: 412 lines (up from 209 in Q2 2025) Note: batch-size growth requires review-time monitoring. ### Cost per Feature - Q2 2025 baseline: $8,400 per shipped feature - Q2 2026 with AI: $6,900 (−18%) - Rework adjustment applied: −$420 per feature --- ## Operational & Security Compliance ### Code Quality Signals - AI-touched lines with static-analysis warnings: 4.8% (vs. 2.9% human) - 30-day churn rate, AI lines: 6.2% (threshold: <5%, flagged) - OWASP-mapped issues in AI PRs: 3 identified, 3 remediated before merge ### Review Burden - Median review time, AI-assisted PRs: 4.1 hours - Median review time, human PRs: 2.8 hours - Senior engineer review load: +22% QoQ (monitoring required) ### Governance - AI authorship attestation coverage: 100% of repos in scope - Provenance format: Git Notes (machine-readable JSON, auditable) - Sensitive-path AI authorship threshold: 0 breaches (policy: <40%) --- ## Strategic Initiatives ### Multi-Tool Visibility - Tools tracked with line-level fidelity: GitHub Copilot, Cursor, Claude Code - Aggregate AI code share across all tools: 58% - Tool-by-tool net cycle-time delta: - GitHub Copilot: −18% - Cursor: −27% - Claude Code: −31% (smaller sample, n=214 PRs) ### Onboarding Acceleration - Median time to 10th PR, new hires using AI: 19 days - Median time to 10th PR, Q1 2024 cohort (pre-AI): 38 days - Reduction: −50% ### Coaching & Skill Transfer - ink-prompting-coach deployed to: 3 underperforming teams - Rework rate change, coached teams (2 sprints): −31% - Best Practices insights distributed: 4 org-wide patterns --- ## 30-Day AI Technical Debt Tracking | Cohort (merge month) | AI lines shipped | 30-day churn | 60-day incidents | 90-day follow-on edits | |----------------------|-----------------|--------------|------------------|------------------------| | April 2026 | 142,300 | 5.8% | 0.9 per 100 PRs | 11.2% | | May 2026 | 168,700 | 6.4% | 1.2 per 100 PRs | 13.1% | | June 2026 | 191,200 | 6.2% | Pending (30 days) | Pending | Threshold: churn >5% triggers coaching review. May cohort flagged. --- ## Appendix: Data Sources - Commit-level provenance: Exceeds Ink Git Notes (refs/notes/exceeds-ink) - Cycle time, review time: GitHub PR metadata via Exceeds AI platform - Incident correlation: PagerDuty integration, 90-day lookback - Token spend: Cursor billing state DB (exact), GitHub Copilot invoice - Rework cost: $150K fully loaded engineer rate ÷ 2,080 hours See how Exceeds AI populates this template with your repo data
Executive Summary Insights for Board Conversations
The executive summary block above uses numbers a provenance-backed platform can produce on day one. The AI-touched commit figure mentioned earlier comes from Exceeds AI customer data at a 300-engineer software company. The 18% productivity lift comes from comparing cycle time on AI-assisted versus human-only PRs from the same engineers over the same period. Controlling for engineer identity removes the confound where high-performing engineers simply adopt AI faster.
The rework flag signals a trade-off that boards need to see. A DX longitudinal study across engineering organizations with over 100 engineers found median PR throughput increased by just under 8% as AI tool usage rose 65%, which falls far below the 3× or 10× expectations many executives bring into board meetings. The gap between headline productivity claims and measured outcomes is where a provenance-backed report earns credibility, because it shows both the gain and the cost.
Adoption & Utilization
Active Users and Tool Distribution
The adoption metrics in the template separate monthly active users from daily active users, because that ratio shows whether AI is part of daily workflow or used only when convenient. Laura Tacho’s analysis of 121,000 developers across 450+ companies found 92.6% use an AI coding assistant at least monthly while 75% use one weekly, so monthly active user counts overstate meaningful adoption.
Interaction Mode Distribution
Exceeds Ink records whether each session ran in plan, ask, agent, edit, or headless mode. This signal does not appear in GitHub Copilot Analytics or any metadata-only tool. Agent-mode sessions at 18% of Q2 sessions, up from 9% in Q1, correlate with the batch-size growth flagged in the review-burden section. Swarmia data shows median PR batch size roughly doubled between Q1 2025 and Q1 2026 as agent adoption became mainstream.
Commit-Level Attribution
The three PR examples in the template show what line-level provenance enables. PR #1601 provides a human-only baseline that makes the AI-versus-human comparison statistically valid. Without a provenance layer, boards receive adoption percentages with no denominator they can trust.
Engineering & ROI Impact
Cycle Time
The −24% cycle-time figure uses the interval from first commit to merge, not from ticket creation. That interval is the metric most sensitive to AI’s direct effect on coding speed. Lead time for changes measured from first edit to successful merge can be a useful metric for assessing whether GitHub Copilot accelerates delivery.
Throughput and Batch Size
The 31% PR volume increase looks strong in isolation. The 97% increase in median PR size introduces the caveat in the template, because reviewers now face much larger diffs. LinearB’s analysis of 8.1 million pull requests found agentic AI pull requests wait 5.25× longer for pickup than human-written ones. Boards need both numbers to understand the trade-off.
Cost per Feature
The $6,900 cost-per-feature figure includes a rework adjustment. A reliable ROI formula for AI coding tools subtracts rework cost from AI-introduced churn before reporting net savings. Omitting that adjustment produces a number that will not survive a CFO’s first follow-up question.
Operational & Security Compliance
Code Quality Signals
An empirical study tracking 302,579 AI-authored commits across 6,299 GitHub repositories found more than 15% of commits from every AI coding assistant studied introduce at least one detectable issue, a risk that metadata dashboards cannot surface because they never read the code. The 4.8% static-analysis warning rate in the template sits below that threshold but above the human baseline, so it deserves a clear callout in the board report.
Review Burden
The Harness State of Engineering Excellence 2026 report, based on 700 respondents, found 81% of engineering leaders report developers spend more time in code review after adopting AI coding tools. The +22% senior engineer review load in the template acts as a leading indicator of burnout risk that belongs alongside the productivity gains.
Governance
The 100% attestation coverage figure only becomes achievable with a provenance layer that writes structured data alongside every commit. GitHub Copilot Analytics cannot produce this number. Exceeds Ink writes a Git Note at refs/notes/exceeds-ink for every commit in scope, giving legal counsel, auditors, and patent examiners a machine-readable record of AI authorship without relying on estimates.
Strategic Initiatives
Multi-Tool Visibility
The tool-by-tool cycle-time delta section closes a gap no single-vendor dashboard can fill. Teams in 2026 often use GitHub Copilot for inline autocomplete, Cursor for feature development, and Claude Code for large-scale refactoring within the same sprint. As noted in the adoption data, this code spans multiple tools rather than one vendor’s dashboard. A board report that attributes all AI output to one vendor understates the program’s scope and misattributes outcomes.
Onboarding Acceleration
In Q4 2025, Time to 10th PR was cut in half from 91 days with no AI usage to 49 days with daily AI use. The template’s 19-day versus 38-day comparison mirrors that benchmark at the organization level, anchored to actual PR timestamps rather than survey self-reports.
AI vs. Human Outcome Comparison
The comparison section in the template presents five dimensions where AI-assisted and human-only PRs diverge. The data requires commit-level provenance to remain defensible, because without knowing which lines in a PR were AI-generated, cycle-time comparisons blend engineer skill, task complexity, and AI contribution into a single number.
Across the three primary tools in the template, the outcome pattern aligns with published research while still varying by tool and interaction mode. Cursor agent-mode sessions produce the largest cycle-time reductions and the highest rework rates, a trade-off that only becomes visible when interaction mode is captured alongside the commit. Claude Code sessions show the strongest cycle-time delta in the template’s small sample, consistent with its use on large-scale refactoring tasks where human-only cycle times run longest. GitHub Copilot inline sessions show the most stable quality metrics, consistent with its use on bounded autocomplete tasks where the engineer retains more authorship.
Anthropic’s randomized controlled trial with 52 software engineers found that AI-assisted participants scored 17% lower on post-task mastery quizzes, which reinforces the review-burden data in the operational section. Engineers who delegate more to AI agents may move faster in the short term and become slower to catch the errors those agents introduce.
Empirical studies report that less experienced developers benefit disproportionately from AI assistance, achieving higher relative productivity improvements than experienced practitioners. That pattern means the aggregate cycle-time delta in a board report can hide a bimodal distribution where junior engineers accelerate while senior engineers absorb review overhead.
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30-Day AI Technical Debt Tracking
The longitudinal table tracks three cohorts of AI-touched code across 30, 60, and 90 days post-merge. This section has no equivalent in GitHub Copilot Analytics, Jellyfish, LinearB, or Swarmia, because those platforms measure outcomes at merge time and stop. The table below shows how the May 2026 cohort’s 6.4% churn rate exceeds the 5% governance threshold, which triggers a coaching review and highlights a pattern that only appears when tracking extends beyond merge time.
He et al.’s analysis of open-source repositories adopting Cursor AI found increases in code complexity and static-analysis warnings, with velocity gains being transient while the debt persists. The longitudinal table in the template turns that finding into an operational signal, because it shows whether the organization’s AI-touched code accumulates debt fast enough to erode the cycle-time gains reported in the ROI section.
SonarSource’s State of Code Developer Survey found 88% of developers report at least one negative impact of AI on technical debt, while 53% attribute negative impacts to AI generating code that appears correct but introduces hidden defects. A 30-day tracking table converts that qualitative concern into a board-reportable number with a defined threshold and a defined response.
Exceeds AI’s longitudinal outcome tracking monitors AI-touched code over 30, 60, and 90 days for incident rates, rework patterns, and maintainability issues, anchored to Exceeds Ink’s per-commit attestation. Without that attestation, leaders have no reliable way to isolate which post-merge incidents trace back to AI-generated lines versus human-authored ones.
Implementation Notes, Limitations, and Next Steps
The template above depends on three data sources that GitHub Copilot Analytics alone cannot supply.
- Line-level AI authorship attribution across all tools in use, not just Copilot
- Interaction-mode classification per session, including agent, inline, chat, and headless
- Longitudinal outcome tracking anchored to specific AI-touched commits
GitHub Copilot Analytics provides acceptance rates and suggested-line counts. It does not distinguish which merged lines were AI-generated versus human-edited after suggestion, does not cover Cursor, Claude Code, or Codex sessions, and does not track post-merge outcomes. Faros’s analysis of telemetry from 22,000 developers across 4,000 teams found bugs per developer up 54% under high AI adoption, the incident-to-PR ratio more than tripled, and median PR review time up 441%, none of which appears in a metadata-only dashboard.
Jellyfish and similar pre-AI engineering intelligence platforms surface PR cycle times and commit volumes but remain structurally blind to AI’s code-level impact. They cannot tell a board which lines are AI-generated, whether AI-authored diffs are higher quality or riskier, or which adoption patterns actually work. The security risk flagged earlier is confirmed by Veracode’s 2025 GenAI Code Security Report, which found 45% of AI-generated code samples introduce known OWASP Top 10 vulnerabilities when tested across over 100 LLMs.
Before presenting this template to a board, engineering leaders should answer four questions that test whether the report can survive scrutiny. First, can the AI authorship figures be reproduced by an auditor with access to the repository, without relying on a vendor’s proprietary cloud? Reproducibility forms the foundation, because if the numbers cannot be verified, the remaining questions do not matter. Second, does the cycle-time comparison control for engineer identity, task complexity, and tool selection, or does it compare different engineers using different tools on different tasks? Valid comparisons require controlling for confounds that metadata dashboards ignore.
Third, is the rework rate calculated from actual line-level churn on AI-attributed commits, or from a survey asking engineers how much they rewrote AI output? Self-reported rework estimates often diverge from measured churn by a factor of two or three. Finally, does the technical debt table cover all AI tools in use, or only the one vendor whose telemetry the analytics platform ingests? Partial coverage produces a report that understates both the program’s scope and its risk.
If any answer is “no” or “we’re not sure,” the report rests on estimates. Estimates rarely survive a CFO’s follow-up question or an audit. PwC’s 2026 CEO Survey found 56% of CEOs report neither increased revenue nor decreased costs from AI investments in the last 12 months. The 12% who report both outcomes share one characteristic: they measure work type, task complexity, and output quality, not login activity or acceptance rates.
The path from estimates to code-level truth runs through a provenance layer that captures AI authorship on the developer’s machine, writes a portable attestation alongside every commit, and tracks outcomes longitudinally. Exceeds Ink provides that layer and turns the template above into a reproducible report instead of a purely illustrative example.
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Frequently Asked Questions
What is a GitHub Copilot board report, and how is it different from the built-in Copilot Analytics dashboard?
A GitHub Copilot board report is an executive document that connects AI coding tool usage to engineering and business outcomes such as cycle time, rework rate, incident rate, cost per feature, and technical debt trajectory. GitHub Copilot Analytics is a usage dashboard that shows acceptance rates, lines suggested, and active users, but it does not show whether accepted suggestions survived code review, whether AI-touched modules had higher incident rates 30 days later, or what Cursor and Claude Code sessions contributed alongside Copilot. A board report requires outcome data, while Copilot Analytics provides activity data. The gap between the two is where most AI ROI conversations stall.
Why do existing board report templates use placeholder numbers instead of real figures?
Most published templates, including those from GitHub and third-party analytics vendors, use placeholders because they rely on metadata such as PR cycle times, commit volumes, and acceptance rates that do not require reading the code. Populating a template with real, defensible numbers requires line-level attribution across every AI tool in use, longitudinal outcome tracking anchored to specific commits, and interaction-mode data that separates agent sessions from inline autocomplete. None of that appears in a metadata-only platform. The template in this article uses realistic figures drawn from Exceeds AI customer data and published benchmarks because those data sources exist when a provenance layer is in place.
How should engineering leaders handle the multi-tool problem when building a board report?
Most engineering teams in 2026 use GitHub Copilot for inline autocomplete, Cursor for feature development, and Claude Code for large-scale refactoring within the same sprint. A board report that attributes all AI output to one vendor’s dashboard understates the program’s scope and misattributes outcomes. The correct approach captures AI authorship at the commit level across all tools, then aggregates by tool for the strategic initiatives section and by outcome for the ROI section. This approach requires a tool-agnostic provenance layer with dedicated adapters for each AI coding tool in use. Exceeds Ink provides first-class adapters for GitHub Copilot, Cursor, Claude Code, Codex, and Windsurf, with lighter-weight detection across up to approximately 50 additional tools, so the board report reflects the full AI program rather than one vendor’s slice of it.
What is the right threshold for flagging AI technical debt in a board report?
A 30-day churn rate above 5% on AI-touched lines offers a reasonable starting threshold, based on published benchmarks showing pre-AI industry churn rates around 3.3% rising to between 5.7% and 7.1% as AI adoption became widespread. The threshold should align with the organization’s own pre-AI baseline rather than an industry average, because codebase age, language mix, and team tenure all affect baseline churn. The 60-day incident rate and 90-day follow-on edit rate in the template provide two additional longitudinal signals that catch debt patterns the 30-day churn rate misses, particularly for AI-generated code that passes review cleanly but contains architectural misalignments that only surface under production load. All three thresholds require commit-level provenance to be meaningful, because without knowing which lines are AI-generated, churn and incident rates cannot be attributed to AI versus human authorship.
How long does it take to produce a board report with real commit-level numbers using Exceeds AI?
Setup requires GitHub, GitLab, or Azure DevOps authorization plus a lightweight Exceeds Ink install on engineer machines. First insights appear within 60 minutes of authorization. Complete historical analysis, covering up to 12 months of prior commits, completes within four hours. Real-time updates appear within five minutes of new commits. A board-ready report with populated cycle-time, rework, incident, and technical debt figures is achievable within the first two weeks of deployment, compared to the approximately nine-month average time-to-ROI reported for platforms like Jellyfish. The difference comes from architecture, because Exceeds AI reads the code directly instead of waiting for metadata pipelines to accumulate enough signal to produce a trend line.