The five things the teams that accelerate do (report, Section 4) are the spine of both. Coaching covers People — developers, QA engineers, product owners and managers. Consulting covers Leadership — program management and benchmarking. Practice, at team, function and organization level, and AI Workflows, for Quality and other end-to-end processes.
Product Owners (10 principles) · Developers (12 principles) · QA Engineers (9 principles). Practiced via hands-on work on sprint-like projects, personalized feedback, evaluation, and ongoing coaching. Participants also practice presenting their work, sharing standardized metrics, exercising human judgment, and quality assurance.
Product Owners (24 competencies) · Developers (36) · QA Engineers (38). Each pairs a hands-on workflow with a personal ownership layer, so the whole team improves the same way: competence to direct AI, confidence to use it, and the ability to explain and vouch for the result.
Goals › Learn › Practice › Present › Evaluate › Feedback › Coach › Reflect — almost all of it hands-on on your real backlog, full-time or part-time, with results measured against your own baseline. Start with one discipline or run PO, Dev and QA in parallel from shared specs.
Artifacts and application in daily work, not completion surveys. Practitioners earn the AI Sprints Practitioner credential; champions progress to the Champion credential (issued through BadgeCert). Tool- and LLM-agnostic; coaching with an exit.
Consulting covers Leadership — program management and benchmarking — and works with the coaching on function and organization practices and AI workflows: a baseline and an ROI target before any spend; review, verification and governance redesigned for AI volume; the whole lifecycle redesigned with product management and QA inside it; and a decision, in advance, on where saved time goes. It runs alongside the cohorts, with a leadership track of its own, and ends when your champions can run the cycle without us.
Your delivery numbers and an optional anonymous team pulse, read against the research. That reading is the baseline everything after is measured from.
Set before any spend: roles, format, dates, and whether consulting is needed alongside — as small as a single cohort.
Framework 2.0 mapped to your product owners, developers and QA engineers, and what changes for each of them in the first two weeks.
Coaching builds the competencies: developers, QA engineers, product owners and managers practice directing AI on their own committed work. Consulting covers leadership — program management and benchmarking — and works with the coaching on function and organization practices and AI workflows.
Product owners, developers and QA engineers, with managers alongside. You can start with one discipline or run product, development and QA in parallel from shared specs.
Goals, learn, practice, present, evaluate, feedback, coach, reflect — almost all of it hands-on on your real backlog, full-time or part-time, with results measured against your own baseline.
No. AI Sprints is tool- and LLM-agnostic. The practice is the asset; the tools change every few months.
Baseline first, on the team's own delivery data — throughput, cycle time, rework, defects and incidents — using the tracking the team already has. Tracked work only, speed and quality together, and competency evidence rather than course completion.
Yes. Practitioners earn the AI Sprints Practitioner credential, issued through BadgeCert, based on artifacts and application in daily work rather than completion surveys.
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.