Nearly every person on the team now uses AI. Very few organizations use AI in a consistent, scalable, repeatable way, with results that show up in delivery. AI Sprints coaches and consults product, development, and QA teams to close that gap — on their real work, measured against their own baseline.
124 research sources53 organizationsMeasured on 170+ engineersTwo clients public with their results
From Accelerating Software Delivery with AI — Research Digest, October 2026. One number per line here; the evidence is in the report.
Across 22,000 organizations, the median team's delivery throughput rose 4% with AI; the top 5% of teams roughly doubled (CircleCI). Sections 1–2 →
Organizational factors explain 67% of AI impact against 32% for individual factors (Microsoft). And it does not fix itself: 81% of organizations provide AI tools, 46% provide training (Giray et al.). Section 3 →
Leadership · People · Practice · Quality · Workflows. Top performers invest in hands-on coaching 57% vs 20% (McKinsey). Section 4 →
An enterprise mandate paired with standardized tooling and re-engineered review reached 2.09x throughput with merge and revert rates stable (Stanford/CMU, 802 developers). Section 5 →
The research says the teams that accelerate do five things. Each one maps to an AI Sprints offering, delivered with your teams on your committed work.
Decides at the top: strategy, investment, initiative, baseline > target, and where saved time goes.
Baseline and target set before spend, and progress tracked against them.
Coach hands-on, on real work: competence to direct AI, confidence to use it, and the ability to explain and vouch for the result.
Role-by-role, hands-on, on your committed delivery work.
Make intent explicit: context, specs, standards, and rules files become the new control surface.
Written down and shared, so the practice lives across the team, not in two people.
Re-engineer review and verification for AI volume: risk-tiered review, tests with every change, human in control.
Redesign the workflows: before scaling the tools, the whole lifecycle, product and QA inside it, and beyond.
AI workflows help reimagine your workflows and implement them in days, not weeks.
The starter library includes role-specific workflows for individuals, teams, and end-to-end workflows.
Human-centered, tool- and LLM-agnostic, coaching with an exit: champions and credentialed practitioners keep the capability in-house when we leave.
Program measurements on tracked delivery work, Dec 2025 – Jul 2026 cohorts, 170+ engineers. Not survey self-reports.
50%–60% faster software delivery with AI Sprints Coaching.
Publicly announced its AI Sprints Coaching initiative: AI adoption built on supported teams, human judgment, and quality.
Your engineers are already faster with AI; your roadmap isn't — yet. One practice across product, development and QA, on the work already committed.
Your margin is engineering time. One structured practice across teams and accounts, with the governance and the baseline-to-result evidence your clients will ask for.
A 45-minute benchmarking and planning call. You leave with a baseline we can measure against, an ROI target set before any spend, and the shape of a first cohort — roles, format, dates. No preparation needed beyond deciding who should be in the room.