ai-impact

Work out where you actually sit between using AI and architecting workflows with it, and what the next rung is. Places you on behavioural ladders across the three modes of engagement (automation, augmentation, agency), how the AI gets your context, verification rigour and leadership scope, then names one habit change worth making. Use when AI is clearly helping but the leverage has plateaued, when every session starts by re-explaining the same background, or before rolling AI practices out to a team. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it triages and routes here.

Find out whether your AI work is producing business traction or AI Theater, using evidence from what you have actually shipped rather than self-report. Places the organisation on behavioural ladders across strategic focus, execution model, team integration and pragmatic mindset, names the binding constraint, and ends in one 2-4 week experiment. Use when there is a lot of AI activity and no defensible line to a business outcome, before an AI budget or steering-committee conversation, or when someone asks how the AI programme is going and the honest answer is a list of pilots. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it triages and routes here.

Compatibility entrypoint for the former business-initiative-coach name. Use only when an existing instruction or invocation names it; new workflows should use frame-intent for outcomes, assumptions, and leading indicators before specification.

Find which scaling-framework concession has hardened into a constraint in your organization, then design one descaling experiment against it. Diagnoses across five named concessions, then produces a hypothesis, the smallest move, a leading indicator, the resistance to expect, and a retrospective question. Use when a scaling framework was adopted with compromises that were meant to be temporary and never revisited. Scoped to a scaling framework's own compromises — for the broader project-to-product operating-model question use portfolio-to-product-shift-coach, and for one initiative's readiness use sniff-test. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.

Extend a board that ends at Released, Deployed or Done with one adoption/learn lane, so a team stops treating deployment as success. Coaches where to put the lane and at what altitude, its entry and exit criteria, what telemetry has to exist first, how to limit it, how to backfill the work already closed as Done, and how to answer the overhead objection honestly. Use when features ship and nobody knows whether anyone uses them, when AI use cases are deployed and unused, or when a spec-driven board has a column for every step of the software and none for the human on the other end. For the numbers behind the limit this puts on the new state, use wip-limit-configuration-coach. If the epics themselves are written as output, run outcome-framing-coach first. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.

Decide whether flow metrics would actually help your context before adopting any. Interviews you about the symptoms and friction you want to improve, then connects those symptoms to the flow metrics that would make the problem visible — WIP, age, throughput, cycle time, distribution — and ends in one small 2-4 week experiment. Use when a team or portfolio is considering flow metrics, when someone is measuring flow but cannot say what decision the numbers change, or when a metrics dashboard exists and nothing has improved. This decides *whether* to adopt metrics at all; to set actual WIP numbers once you have, use wip-limit-configuration-coach. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.

Frame intent for an agentic workflow with a desired outcome, explicit assumptions, and leading indicators so specification is grounded in purpose. Use before discovery or specification, when work is described as the thing being built, or when a product, AI use case, internal capability, or operating change lacks evidence for steering. Produce a usable intent brief while coaching the human on outcome thinking and discovery through the decisions at hand. Applies at any scale of work and supplies intent to the receiving workflow's discovery, specification, and planning steps.

Reframe work from output/activity language to outcome language, on two surfaces — backlog items (Jira Epics and the like, classified on the Input→Activity→Output→Outcome→Impact taxonomy) and leadership asks (the drive-by directive that names a solution instead of a problem). Use when reviewing backlog epics in bulk, facilitating PI planning, running a value orientation audit, coaching a product owner on outcome-driven planning, or pressure-testing a message before a leader sends it. For whether an initiative is ready to commit rather than how it is worded, use sniff-test. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.

Prioritize open issues in a local GitHub repository workspace by value outcome framing and strategic goal alignment. Classifies backlog items across outcome altitude (Impact/Outcome vs Output vs Activity) and evaluates them against repository strategic goals (P0 Strategic Drivers down to P4 Distractions). Use when planning milestones or sprints, triaging an issue backlog, deciding what work to pull next, or auditing a repository queue for outcome and strategic focus.

Audit how far an organisation's operating model has actually moved from project to product, across strategic alignment, people and leadership, structure and governance, and the value cycle. Runs a ten-probe short read by default and a forty-probe deep audit on request, places the organisation as Project Silo, Transitional Lab or Product-Led Engine, and ends in one experiment against the binding dimension. Use before or during a project-to-product transformation, when "we do product now" needs a reality check, or when teams are visibly busy and the business strategy is not moving. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it triages and routes here.

Adapt your scaled agile ways of working for an agentic SDLC, deciding which coordination mechanisms still earn their keep once agents write much of the code. Inventories the events, boards, roles and gates you run, tests each against the problem it was bought to solve, decides whether human judgment belongs in the flow or on a cadence, and produces one experiment with a leading indicator. Use when adopting agentic engineering, spec-driven development or an AI SDLC raises the question of whether SAFe, LeSS, Nexus or your own scaling apparatus is still needed. Scoped to what an agentic SDLC changes about the cost of coordination — for a scaling framework's own compromises that hardened over time use descaling-experiment-coach, and for the WIP numbers themselves use wip-limit-configuration-coach. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.

Run a lightweight, evidence-based "sniff test" on a single initiative (epic, project, or Tier-1 bet) to find where its clarity is insufficient for its stage in the delivery lifecycle, so you can drive the right investment/commit and derisking conversations. Best run pointed at where the initiative is managed (its folder/workspace, Confluence space, or Jira epic) or given its Lean Product Canvas plus a recent sponsor update; with little input it asks for context first. Use when pressure-testing an initiative before a Plan/Commit decision or Execute/Build commitment, checking whether a Lean Product Canvas and Discovery plan match the real risks, or coaching on leading indicators and risk-appropriate derisking. For multi-initiative board patterns, use the sniff-test-portfolio add-on. Not the right skill for org-level operating-model questions — that is portfolio-to-product-shift-coach. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.

Add-on to the sniff-test skill that looks across many initiatives on a portfolio Kanban for board-level patterns — duplicate/shadowing initiatives, WIP clustering, stale-status sweeps, experiment scope creep, and portfolio risk balance (Explore/Discovery vs. Build & Deliver split). Use when reviewing a whole board or portfolio, prepping a sponsor/ELT portfolio review, or deciding where the portfolio is over- or under-invested. For sniffing a single initiative, use sniff-test. For the operating model behind the board rather than the board itself, use portfolio-to-product-shift-coach. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.

Choose real starting numbers for active-stage and queue-stage WIP limits in a human-agent workflow, rather than a universal formula or people-divided-by-two. Interviews you on workflow, human-agent topology, agent autonomy, shared constraints and replenishment evidence; computes candidate limits and cross-checks them with Little's Law; returns one starting configuration translated into board and agent pull policies. Use when agents write much of the code and existing WIP limits no longer describe the system. For the prior question of whether flow metrics would help you at all, use flow-metrics-self-assessment. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.