Capabilities
27 departments and 267 skills, all first drafts. Browse by outcome along the journey, by Microsoft technology, or search.
AI Strategy first version, draft
Define the organisation's AI ambition, target outcomes, principles, operating model and roadmap, starting from business outcomes rather than products.
ai-strategy:ai-maturity-assessmentdraftDiagnoses an organisation's current AI maturity across strategy, people and adoption, knowledge, governance, platform and delivery capability, and value measurement, using observed evidence rather than self-scored opinion, and maps the result to a CopilotForge archetype. Use when a programme is starting, when leaders disagree about how ready they are, or when a strategy refresh needs an honest baseline.
ai-strategy:ai-operating-modeldraftDesigns the target operating model for AI - who decides, who builds, who supports and who owns value - choosing a central, hub-and-spoke or federated shape to suit the edition and archetype, and placing the Centre of Excellence within it. Use when AI work is spreading without clear ownership, when leaders ask how AI should be organised, or before a CoE is chartered.
ai-strategy:ai-roadmapdraftBuilds a prioritised, sequenced AI roadmap across the Adopt AI, Build with AI and Scale AI journeys and the lifecycle stages, showing dependencies, capacity, foundations and review points against the agreed outcomes. Use when leaders need a plan for the next twelve to twenty-four months, when initiatives compete for the same people, or when an existing roadmap has drifted from its outcomes.
ai-strategy:ai-visiondraftArticulates the organisation's AI ambition - a deliberately chosen level of ambition, a short vision statement staff can repeat, a narrative of how work will change and a handful of guiding principles - agreed by the leadership team and written for the whole workforce. Use when leaders cannot say in a sentence why the organisation is adopting AI, when functions hold competing pictures of it, or before outcomes and a roadmap are set.
ai-strategy:executive-sponsorshipdraftSecures and equips an active executive sponsor for the whole AI programme - choosing the right executive, agreeing time, decision rights and visible behaviours in a sponsor agreement, building a peer sponsor coalition and checking sponsor engagement each quarter. Use when an AI programme has no sponsor or only a nominal one, when the sponsor has gone quiet, or when the sponsor changes role.
ai-strategy:investment-casedraftBuilds the funding case for the AI programme as a whole across the Adopt, Build and Scale journeys - investment options, full cost of ownership by category, benefit ranges tied to strategic outcomes, staged funding tranches released at evidence gates and the funding model. Use when leaders must decide how much to invest in AI overall, when a budget round needs a programme request, or when funding was approved for licences alone.
ai-strategy:platform-strategydraftSets the organisation's strategic position on the Microsoft AI platform mix - Copilot Chat, Microsoft 365 Copilot, agents, Copilot Cowork, Copilot Code, Copilot Studio and Microsoft Foundry - giving each a posture, audience, owner, entry criteria and sequence. Use when leaders ask which Microsoft AI platforms the organisation should offer and to whom, when teams adopt platforms ad hoc, or when Microsoft change prompts a rethink.
ai-strategy:strategic-outcomesdraftTurns an AI ambition into three to five measurable business outcomes, a short set of AI principles and an explicit statement of where AI will not be used, with clear expectations of the executive sponsor. Use when leaders say they want an AI strategy, when a programme has activity but no agreed outcomes, or when a sponsor needs to explain why the organisation is investing in AI.
ai-strategy:strategy-refreshdraftRefreshes an existing AI strategy on a fixed cycle or a defined trigger, testing the vision, outcomes, platform position and roadmap against measured outcomes, a re-run maturity baseline, Microsoft product change and new regulation, and records what changed and why. Use when the annual strategy review is due, when outcomes are off track, or when a major Microsoft or regulatory change makes the current strategy doubtful.
Opportunity Management first version, draft
Create a disciplined path from business problem to prioritised and governed initiative, including the decision about whether AI is the right answer at all.
opportunity-management:ai-suitabilitydraftDecides whether a framed business problem should be solved with AI at all, and if so which pattern fits - Copilot Chat, a Cowork task, an agent, Copilot Code, a Foundry solution or plain automation - with the reasoning recorded. Use when an idea arrives at intake, when a team has already picked a tool, or when a sponsor asks why AI is preferable to process change or training.
opportunity-management:backlog-managementdraftSets up and operates the AI opportunity backlog as one visible board - choosing the tool, defining stages with entry and exit rules, mandatory fields per stage, work-in-progress limits, ageing rules, merges, archiving and a regular hygiene routine - so it stays the single route to resources. Use when there is no backlog or several competing lists, when the board is stale or cluttered, or when nobody trusts its status fields.
opportunity-management:business-casedraftWrites a proportionate business case for one significant AI initiative - choosing no case, a light case or a full case by cost, risk and reach, then setting out options including a non-AI option, build and run costs, benefits from the value hypothesis, risks, the ask and the decision. Use when a single agent, automation or engineered solution needs funding or approval beyond delegated limits, or when a sponsor asks for a case before committing.
opportunity-management:effort-assessmentdraftEstimates what an AI opportunity will really take - build effort by pattern, the scarce skills and owners it needs, its dependencies on knowledge, data, platforms and reviewers, and the ongoing run cost after release - as ranges with stated confidence. Use when an opportunity needs a feasibility score, when a team says an agent will take a week, or when nobody has asked who will run and pay for a solution after launch.
opportunity-management:idea-capturedraftDesigns and runs the organisation's open route for staff to submit AI ideas - the submission channels, a short form, acknowledgement and triage timings, duplicate checks, idea campaigns and feedback to every submitter - so ideas reach the backlog instead of side requests. Use when there is no obvious place to send AI ideas, when ideas arrive through managers and chat threads and get lost, or when submissions have dried up.
opportunity-management:opportunity-prioritisationdraftScores and balances a backlog of framed AI opportunities on value, feasibility, risk, readiness of knowledge and ownership, and strategic fit, and shapes a portfolio of quick wins and strategic bets. Admits nothing without an owner, problem, outcome, risk tier and suitability decision. Use when ideas outnumber capacity, when a steering group must choose what goes next, or when the backlog has lost its link to outcomes.
opportunity-management:pilot-selectiondraftChooses which AI opportunities to pilot and designs each pilot so it can prove or disprove its value hypothesis - a representative cohort, a comparison, a fixed duration, success and stop thresholds set in advance, quality checks, risk containment and an exit plan. Use when an opportunity is ready to test with real users, when pilots keep running indefinitely, or when a pilot ended and nobody could tell whether it worked.
opportunity-management:portfolio-balancingdraftReviews the whole AI opportunity portfolio, planned and in flight, and rebalances its mix across quick wins, strategic bets and foundation work, risk-tier exposure, scarce owner and reviewer capacity, business units, outcomes and platforms, then decides what to start, pause, accelerate or stop. Use when the portfolio feels lopsided, when reviewers or owners are overloaded, or at each quarterly portfolio review.
opportunity-management:problem-framingdraftTurns a raw idea or tool request into a framed business problem with a named business owner, affected roles, current cost, expected outcome and baseline, and politely sends solution-first requests back to the problem. Use when an idea arrives at intake, when someone asks for an agent or Copilot feature before saying what is wrong, or when a pilot cannot say what it was meant to improve.
opportunity-management:risk-classificationdraftAssigns a progressive risk tier from 0 to 4 to an AI opportunity or solution by rating impact, reach, data sensitivity and autonomy, then derives the minimum governance posture and the reviewer gates it must pass, and re-classifies when scope changes. Use when an idea has a chosen pattern, when a solution is about to be shared more widely, or when someone asks which reviews a build needs.
opportunity-management:scale-or-stopdraftMakes the evidence-based decision after a pilot or first release to scale, iterate, pivot or stop, judged against the value hypothesis, adoption, quality and cost, and treats a well-evidenced stop as a success. Use when a pilot reaches its review date, when a sponsor wants to roll a solution out wider, or when a live initiative has been running for months without anyone deciding its future.
opportunity-management:value-hypothesisdraftStates the expected value of a framed AI opportunity as a testable hypothesis before anything is built - the mechanism of change, the measure, its baseline, an expected range, the date it should show, leading indicators and the result that would disprove it. Use when an opportunity is about to be prioritised or piloted, when a team claims an agent will save time without saying how, or when nobody can say what would count as failure.
Value Realisation first version, draft
Define baselines, measures and ownership so adoption and deployed solutions create demonstrable value.
value-realisation:adoption-to-value-metricsdraftBuilds the measurement chain from adoption to benefit - usage, then scenario behaviour, then work outcome, then benefit - choosing leading indicators for their link to a named benefit and diagnosing where the chain breaks, so usage is never reported as value. Use when reports show active users but no outcomes, when a sponsor treats adoption as return, or when early warning is needed before benefits can be measured.
value-realisation:baseline-designdraftDesigns the baselines that must be captured before an AI change goes live - exact measure definitions, method, window, data source, sample and a comparison group where fair - so benefits can be proven later rather than asserted. Use when a launch or pilot date is set and nothing has been measured, when baselines rest on memory or vendor figures, or when seasonal or parallel change would make a simple before-and-after misleading.
value-realisation:benefit-hypothesisdraftTurns an approved value hypothesis into formal benefit statements, each with one change, one measure, a direction, a baseline, a target range, timing, an owner role, the enabling changes it depends on and any expected dis-benefits, ready for the benefits register. Use when an opportunity has been approved, when a case lists vague benefits such as efficiency or better insight, or when two solutions claim the same benefit.
value-realisation:benefit-ownerdraftAssigns an accountable business owner role to every benefit, agrees what ownership means - influencing the change, signing off baseline and target, deciding how released capacity is used, reporting and escalating - and sets up the support, hand-over and escalation owners need. Use when benefits sit with IT or the CoE, when an owner has moved role, or when owners have signed up to benefits they cannot influence.
value-realisation:benefit-realisation-plandraftPlans how AI benefits will be realised over time - the benefits register, each benefit's profile by period, the enabling changes and decisions as milestones, measurement points, owners, review cadence, tolerances and closure rules - so value is tracked to closure rather than declared at launch. Use when a case is approved, when benefits have no dates or tracking, or when a portfolio needs one register and review rhythm.
value-realisation:financial-benefitsdraftQuantifies the financial benefits of AI work with the finance function, classifying each as cashable, non-cashable, cost avoidance or revenue, agreeing the valuation method, evidence and budget line, and keeping released time apart from cash until a decision redeploys or removes cost. Use when a case or CFO needs money figures, when hours saved are being multiplied into savings, or when benefits must be banked in budgets.
value-realisation:non-financial-benefitsdraftMeasures AI benefits that should not be forced into money - customer and employee experience, risk and compliance reduction, decision quality, resilience and organisational capability - with defined indicators, baselines, owners and an evidence standard for each, and no spurious cash value. Use when a case rests on experience or risk reduction, when finance dismisses soft benefits, or when capability gains need evidence a sponsor will accept.
value-realisation:post-implementation-reviewdraftRuns a post-implementation review an agreed period after an AI solution or programme goes live, comparing actual outcomes, costs, adoption, quality and risk with the approved case, explaining each variance, capturing lessons and agreeing actions for benefits still to come. Use when a live solution reaches its review date, when a funding body asks whether the case was delivered, or when lessons should improve how future cases are estimated.
value-realisation:productivity-measurementdraftMeasures the productivity effect of Copilot, agents, Cowork tasks and automation credibly - throughput, cycle time and capacity released from work data, sampled timing and comparison groups - and tracks where released capacity went, keeping self-reported hours saved as a perception measure only. Use when an hours-saved survey is challenged, when a sponsor asks what people did with saved time, or when a pilot must show a productivity effect.
value-realisation:quality-measurementdraftMeasures how AI-assisted work changes business quality after launch - accuracy, rework, errors reaching customers, compliance exceptions and consistency - through sampled review against a rubric, operational defect data and comparison with unassisted work. Use when a sponsor asks whether AI-assisted work is better or only faster, when rework or complaints rise after launch, or when productivity gains need a quality guard.
value-realisation:roi-modeldraftBuilds a transparent return-on-investment model for an AI initiative or portfolio - full costs across licences, consumption, build, run, review effort, support and change, benefits by class and timing, low, central and high ranges, sensitivity to key assumptions and an assumptions log. Use when a case or renewal needs a return figure, when an ROI number cannot be traced to its inputs, or when estimated and actual return must be compared.
value-realisation:value-dashboarddraftSpecifies a sponsor value dashboard for the AI portfolio across value, adoption, capability and governance, with each tile's measure, method, confidence, baseline, owner and source, plus the data flow from the benefits register, refresh, access, minimum group size and caveats, ready for a report builder. Use when sponsors need one view of realised value, when a dashboard shows usage alone, or when a chosen scorecard now needs building.
Programme Management first version, draft
Mobilise and govern a coherent adoption, delivery or transformation programme.
programme-management:budget-and-capacitydraftPlans and tracks an AI programme's budget and people capacity - services, internal time, licences as advised, and consumption costs such as Copilot Studio and Foundry usage - with forecasts, variance against tolerance, and monthly capacity of scarce roles such as knowledge owners and reviewers. Use when a programme needs a cost and capacity baseline, when consumption costs are growing, or when the same people are committed twice.
programme-management:dependency-managementdraftManages the dependencies an AI programme relies on - between workstreams, on platform and tenant changes, on suppliers and partners, on other programmes and on Microsoft releases and retirements - each with a giver, receiver, need-by date, status and fallback, watched through a release watch. Use when milestones wait on others, when a Microsoft change could move the plan, or when a partner or IT team must deliver something first.
programme-management:integrated-roadmapdraftIntegrates every AI programme workstream plan - adoption, Champions, learning, knowledge, platform, agent delivery, governance and value - into one dated delivery plan with milestones, a critical path, cross-workstream dependencies and a baseline that changes only by decision. Use when workstreams plan separately, when nobody can say what the next milestone depends on, or when the strategic roadmap needs turning into a plan people can run.
programme-management:programme-board-cadencedraftSets the governance rhythm of an AI programme - programme board, workstream leads meeting, design and change forum and sponsor checkpoints - with terms of reference, reporting flow, paper deadlines and a programme decision log, aligned with the CoE's forums rather than duplicating them. Use when a chartered programme needs its meetings set up, when the board has become a status briefing, or when programme decisions cannot be traced.
programme-management:programme-charterdraftCharters an AI adoption, delivery or transformation programme - mandate, the outcomes it serves, scope and exclusions, workstreams, governance and decision rights, tolerances, funding envelope, roles and closure criteria - so the sponsor signs one agreed basis for running it. Use when an agreed pack or roadmap needs mobilising, when AI initiatives run without a common mandate, or when a sponsor asks what the programme is committing to.
programme-management:programme-healthdraftAssesses and reports AI programme health across outcomes, schedule, cost, capacity, risk, adoption, assurance, governance and stakeholder confidence, with defined RAG criteria, an evidence-based narrative, corrective actions with owners, and a recovery plan when health is red. Use when the board needs a monthly health report, when status reports are all green but the programme feels stuck, or when a programme needs a health check or recovery.
programme-management:programme-risk-logdraftRuns the risks, assumptions, issues and decisions log for an AI programme, with AI-specific risk prompts, consistent scoring, owners by role, responses, review dates and escalation to the board, kept separate from solution risk tiers. Use when a programme is mobilising, when risks are known but owned by nobody, when assumptions about licences, knowledge or reviewers go unchecked, or when the board cannot see what threatens delivery.
programme-management:stage-gatesdraftDefines the stage gates of an AI programme along the CopilotForge lifecycle - from mobilisation through enablement, delivery, launch and scale to closure - with the evidence each gate needs, who signs it off, how reviewer findings carry into it, and the outcomes go, conditional go, hold or stop. Use when a programme needs control points, when gates have become ceremonies, or when a sponsor asks what must be true before the next stage.
programme-management:stakeholder-governancedraftGoverns how stakeholders take part in an AI programme's decisions - decision rights by decision type, who must be consulted or must approve (security, data protection, legal, HR, employee representatives, business units), escalation routes and time limits, and an engagement plan for governance stakeholders. Use when nobody knows who can approve a change, when decisions stall between functions, or when a required voice was left out.
programme-management:supplier-managementdraftSelects and manages the partners and suppliers an AI programme relies on - deciding what to source, setting outcome-based requirements and criteria, testing Microsoft AI capability claims on evidence, structuring work packages, knowledge transfer and exit, and reviewing delivery at service level. Use when a programme needs a Microsoft partner or ISV, when a supplier underdelivers, or when the organisation risks depending on a partner for good.
Copilot Adoption first version, draft
Design and run organisation-wide or business-unit Microsoft AI adoption programmes, from readiness and scenarios through launch, reinforcement and measurement.
copilot-adoption:adoption-measurementdraftDefines how Copilot adoption is measured across usage, capability, behaviour change and business outcomes, with baselines set before launch, team or role-level reporting above a minimum group size, and feedback loops that change the programme. Use when setting adoption measures, when a sponsor asks whether Copilot is working, or when someone proposes tracking individual usage.
copilot-adoption:adoption-readinessdraftAssesses whether an organisation or business unit is ready to adopt Microsoft Copilot across sponsorship, licences assigned versus used, IT-led technical enablement, data and permissions hygiene, approved-use guidance, support and communications, and records the gaps to close before launch. Use when licences have been bought or are about to be, before a launch date is fixed, or when early use has stalled.
copilot-adoption:adoption-reinforcementdraftRuns the always-on reinforcement cycle for Copilot after launch - a calendar of nudges and refreshers, scenario refreshes, manager follow-up prompts, re-engagement of teams whose use has faded and refreshers when Copilot changes - triggered by team-level signals, never individual tracking. Use when launch is over and use is plateauing, when teams drift back to old habits, or when a Copilot change must reach existing users.
copilot-adoption:adoption-support-modeldraftDesigns the user support model for Copilot - self-help, Champions as first-line peer guidance, office hours, the IT service desk, admin escalation to Microsoft, the security route for suspected data exposure and the intake route for ideas - with triage categories, capacity and a feedback loop. Use when Copilot is about to launch, when Champions or the service desk are overwhelmed, or when nobody knows where Copilot questions go.
copilot-adoption:audience-segmentationdraftBuilds the adoption audience model for a Copilot programme by grouping the workforce on work pattern, Copilot access, device and shift constraints, information sensitivity and change impact, using aggregate HR and IT data, then sizing each audience and setting how it is reached, onboarded and supported. Use when one message is going to everyone, when waves need defining, or before personas and scenarios are chosen.
copilot-adoption:copilot-onboardingdraftDesigns the onboarding experience each newly licensed or enabled Copilot user receives - access confirmed before the welcome, approved-use guidance in plain words, a first session on starting scenarios, first-week tasks and where to get help - as a repeatable flow for launch waves, new joiners and people changing role. Use when access is being switched on, when new joiners get Copilot with no guidance, or when onboarding was a one-off launch.
copilot-adoption:executive-enablementdraftEnables executives to use Copilot in their own work and lead its adoption visibly - private or small-group sessions on their real tasks, support for executive assistants and chiefs of staff, care with sensitive leadership content, and the visible behaviours that give others permission to experiment. Use when leaders hold licences but rarely use them, before a launch where executives go first, or when leaders' messages about AI ring hollow.
copilot-adoption:frontline-enablementdraftEnables frontline and deskless workers to adopt AI safely - confirming with IT and the licensing route which experience and device each frontline audience can use, choosing a few shift-relevant scenarios, teaching in huddles and on mobile within paid time, involving supervisors and employee representatives, and adapting support and Champions. Use when frontline teams join a rollout, were left out of one, or found desk-based training useless.
copilot-adoption:launch-plandraftPlans a phased Copilot launch with onboarding by persona, executive and manager enablement, reinforcement through office hours and Champions, a support model and a feedback loop that keeps improving the programme. Use when readiness gaps are known and a rollout needs sequencing, when a big-bang launch is being proposed, or when a launch happened but nothing followed it.
copilot-adoption:role-based-adoptiondraftDesigns an adoption journey for each priority role - first scenarios, guided practice on real work, team routines and later extension to Cowork tasks or agents - with role-specific onboarding, manager expectations, peer support, milestones and team-level success signals. Use when a role group needs more than generic onboarding, when a sponsor names priority roles, or when a role's use has stayed shallow.
copilot-adoption:scenario-discoverydraftSegments the workforce into personas such as desk, hybrid and frontline workers, discovers role-based Copilot scenarios with business owners, prioritises them by value and frequency, and builds a scenario catalogue with prompt patterns that users can verify. Use when planning what people will actually do with Copilot, when usage is generic and shallow, or when a new team or role joins the rollout.
Champions first version, draft
Create evidence-led Champion programmes that turn curiosity into capability and capability into contribution, with clear boundaries on what Champions do and do not do.
champions:capability-frameworkdraftDefines the three Champion capabilities - Chat, productivity and agents - each with an objective, a training option, an evidence option, verification criteria and an outcome, so training is one route rather than the gate, and maps learning sessions open to the wider business onto the journey. Use when setting what a Champion must show, when status needs to mean something, or when experienced colleagues want to skip training.
champions:champion-advisordraftSpecifies the Champions advisor, a self-service agent that explains requirements, routes, criteria and next steps from the champion guide - its question set, grounding, what it must refuse such as verification decisions and other people's status, escalation and test questions - before agent design. Use when the programme owner is a bottleneck for questions, or when a Champions advisor is proposed or misbehaving.
champions:champion-challengesdraftDesigns the Champion Challenge - one practical contribution after the three capabilities are verified, such as an envisioning workshop, team training, a governed business agent, a prompt library, a departmental playbook or support for the Centre of Excellence - with a safety and scope check and assessment on impact, reusability, quality and strategic alignment. Use when Champions are ready to contribute or a challenge idea needs checking.
champions:champion-community-designdraftDesigns the Champion community - the space and its channels, a community manager and moderation, rituals such as community calls, show-and-tell and prompt clinics, peer learning through pairing and learning circles, local spokes, and the route by which good material reaches the scenario catalogue. Use when a new network needs a home, when the Champion channel has gone quiet, or when peer learning depends on one organiser.
champions:champion-learningdraftDesigns the learning offer behind the Champion journey - training-route sessions and guided exercises for each capability, exemplars and clinics for the evidence route, Challenge preparation, refreshers when Copilot changes and advanced learning for recognised Champions - shared with the workforce where possible. Use when capabilities need a training route, when sessions feel too basic, or when material is stale.
champions:champion-metricsdraftDefines the programme-level measure set for Champions - coverage, journey conversion and completion time, contribution, community, boundary drift and adoption context - each with a definition, source, baseline, target, owner, minimum group size and reporting rhythm, plus rules against individual rankings. Use when a sponsor asks what the programme delivers, before a dashboard is built, or when a leader board is proposed.
champions:champion-programme-designdraftDesigns an evidence-led Copilot Champions programme as a progression system - purpose, the six-stage journey, operating boundaries, its place between the adoption programme and the Centre of Excellence, sponsorship, agreed time, community, tracker and self-service guidance. Use when starting or replicating a Champion network, when Champions have become an unpaid help desk, or when status means attendance.
champions:champion-recruitmentdraftRecruits a representative, motivated Champion cohort - coverage targets by team, site and persona, an open expression of interest on declared interest, skills and availability, manager agreement before acceptance, fair selection without performance data, a waiting list and a welcome. Use when a Champions programme opens, when the network is all enthusiasts from one function, or when Champions joined without manager agreement.
champions:champions-health-checkdraftRuns the periodic, usually annual, review of a whole Champions programme - purpose, sponsorship, manager time agreements, boundaries, framework currency against product change, coverage, community and measures - ending in a decision to renew, reshape or close it, with renewed commitments and a refreshed charter. Use when the charter review date arrives, when the sponsor or operating model changes, or when the programme has drifted.
champions:evidence-assessmentdraftSets up fair, consistent assessment of Champion evidence against each capability's verification criteria and of Champion Challenges, by trained assessors who assess the evidence rather than the person, keep a restricted progress tracker, give useful feedback, handle appeals and never rank anyone. Use when submissions start arriving, when assessors disagree, when progress status is unclear or when a decision is challenged.
champions:recognition-and-progressiondraftDesigns how Champions progress through the six-stage journey to recognised Champion, with recognition linked to demonstrated contribution, access to advanced materials, pilots and specialist pathways, renewal, a regular programme health check and programme-level measures such as milestone conversion. Use when designing Champion recognition, when status has gone stale, or when a sponsor asks whether the programme is healthy.
Learning and Skills first version, draft
Create role-based learning, practice, assessment and progression pathways.
learning-and-skills:advanced-pathwaysdraftDesigns advanced AI pathways for makers, power users and specialists - entry on declared role, approved access and maker commitments, tracks for delegated Cowork work, agent and automation makers, Copilot Code and Microsoft Foundry, learning by building in governed sandboxes with mentoring, and evidence from a reviewed build. Use when building opens to more people, when makers learn in production, or when specialists lack a route.
learning-and-skills:ai-learning-pathwaydraftDesigns the progressive AI learning pathway shared across roles - foundation, applied, proficient and advanced levels with entry conditions and exit evidence, a route to skip a level by showing evidence, where role curricula and the Champion journey plug in, and refreshers when Microsoft AI changes. Use when every team builds its own course, when experienced users are forced through basics, or when nobody knows what follows foundation.
learning-and-skills:ai-role-curriculumdraftBuilds a role-based AI curriculum for one priority role at a time from its skills needs statement - outcomes tied to the role's real scenarios, modules in sequence, formats that fit how the role works, practice on its own work, the evidence each outcome needs, agreed time and owners. Use when a role's needs are agreed and its learning must be designed, when generic courses are not changing work, or when a new role joins a wave.
learning-and-skills:ai-skills-needs-analysisdraftAnalyses AI skills needs role by role, comparing the skills people have declared with what their future work will demand - the scenarios, agents and delegated tasks heading their way - and produces a prioritised needs statement per role, never per person. Use when a learning budget is being set, when training is about to be bought before anyone knows what roles need, or when a new group of roles joins the rollout.
learning-and-skills:facilitator-guidedraftWrites facilitator guides for AI learning sessions - outcomes, a timed running order, live demonstrations with a fallback when Copilot answers differently, protected hands-on practice, fictional materials, likely questions with safe answers, accessibility and an evidence step - so any prepared facilitator can run the session well. Use when a session depends on one presenter, when demos fail live, or before handing delivery to Champions.
learning-and-skills:learning-evidence-rubricdraftDefines the evidence rubrics that verify workforce AI learning - acceptable evidence types per pathway level, dimensions such as real work, safe use, verification, judgement and transfer with Not yet, Meets and Strong descriptors, who checks evidence and how, feedback and redaction rules - so completion means demonstrated practice, not attendance. Use when learning is counted by attendance, or when a pathway level or module needs exit criteria.
learning-and-skills:office-hoursdraftRuns AI office hours and drop-in clinics as a learning service - open and themed formats, rhythm and timing, who staffs them, booking, safe screen-sharing, a light triage that routes faults and data concerns to the right support route, and capture of recurring questions by theme into curricula and guidance. Use when learners stall after training, when the same questions keep reaching the service desk, or when clinics have gone quiet.
learning-and-skills:practice-labsdraftDesigns safe hands-on practice for AI learning - realistic fictional scenario packs, practice spaces matched to what is being learnt from a learner's own approved tools to isolated maker and specialist sandboxes, rules on what may enter them, reset and clean-up, and lab exercises with expected results and a check step. Use when training is all demonstration, when people practise on live customer data, or when makers need somewhere safe to build.
learning-and-skills:skills-measurementdraftMeasures AI skills growth at role and team level - declared-skills re-surveys against the needs baseline, verified evidence rates by pathway level, progression and time to evidence - and tests whether groups that built skills changed how they use AI, with a minimum group size and no per-person reporting. Use when a sponsor asks whether learning worked, before a learning dashboard is built, or when learning and adoption data disagree.
Change and Communications first version, draft
Build sponsorship, communications, manager engagement, behavioural reinforcement and change readiness.
change-and-communications:adoption-campaign-plandraftPlans a time-boxed adoption campaign of a few weeks around one scenario theme for a defined audience - the behaviour it should change, tested scenarios, a weekly arc, tasks for managers and Champions, assets, a team-level baseline and a closing review that hands practice to reinforcement. Use when a scenario is under-used, when a new capability needs a push, or when leaders want a campaign but cannot say what it should change.
change-and-communications:ai-communication-strategydraftSets the communication strategy for an AI change - the narrative and core messages, honest answers to hard questions on jobs, monitoring and data, an audience, message, sender and channel matrix, a phased calendar, listening routes and the approval path for sensitive messages. Use when an AI programme needs to reach the workforce, when messages conflict or ring hollow, or before a wave, policy change or new AI experience is announced.
change-and-communications:ai-stakeholder-mappingdraftMaps the stakeholders of an AI change by role and group - their interests, influence on the outcome, stated concerns about AI such as jobs, monitoring, quality and ethics, and the engagement each needs - so HR, employee representatives, functions and affected teams are involved early rather than told late. Use when an AI programme or wave is starting, when a group pushes back unexpectedly, or when engagement has been launch emails only.
change-and-communications:behaviour-reinforcement-plandraftPlans how new AI-enabled ways of working are embedded so they stick - naming target behaviours, then building them into processes, templates, team routines, role expectations, onboarding and leadership routines, removing old-way pull-backs and handing ownership to the business. Use when adoption gains fade after launch or a campaign, when new practice depends on enthusiasts, or before a programme hands over to business as usual.
change-and-communications:change-networkdraftBuilds and runs a change network of local change agents alongside Champions - agents carry messages into units, gather concerns and readiness signals and feed back, while Champions build capability - with coverage, manager-agreed time, a briefing and feedback cycle, boundaries and one coordinated ask of managers. Use when change lands unevenly across units, when feedback never reaches the programme, or when Champions are doing change work.
change-and-communications:manager-enablementdraftEquips line managers to lead AI adoption in their own teams - a briefing before their team hears anything, conversation guides for purpose and concerns, team norms for approved use and verification, protected practice time, a monthly question and clear routes for ideas and issues - without asking managers to monitor individuals. Use when managers are bypassed or unsure, before a wave reaches their teams, or when teams stall.
change-and-communications:people-change-impactdraftAssesses how an AI change affects each group of people - tasks, skills, workload, decisions and accountability, tools, team routines and role expectations - with the group's readiness and capacity for change, any consultation needs, and the support each group requires, assessed by role group and never by person. Use when AI will change how a role works, before waves or curricula are planned, or when staff ask what AI means for their jobs.
change-and-communications:resistance-managementdraftUnderstands and responds to resistance, fear and scepticism about AI as information about the change - listening at group level, diagnosing causes such as job fear, monitoring worries, quality doubts, ethical objection, workload and change fatigue, separating legitimate risks from misunderstanding, and choosing a fitting response with an owner. Use when a group pushes back, when fear or rumour spreads, or when scepticism stalls adoption.
change-and-communications:success-storiesdraftCaptures and shares credible success stories about Microsoft AI at work - chosen for real work, evidence and a visible verification step, checked with value owners, cleared for consent and data, honest about limits, and kept in a story bank with review dates. Use when the programme needs proof people will believe, when stories carry inflated or untraceable claims, or when a sponsor wants examples to share.
Knowledge Management first version, draft
Make organisational knowledge discoverable, owned, current, classified and suitable for AI grounding.
knowledge-management:ai-ready-contentdraftGives authors and content owners practical guidance and templates for writing and structuring documents and pages that Copilot and agents can use reliably - one topic per section, explicit scope and dates, descriptive headings, text not images, defined terms, real tables and stated exceptions. Use when authors ask how to write for AI, when answers miss rules buried in long documents, or when knowledge templates are being refreshed.
knowledge-management:content-qualitydraftSets the quality standard for knowledge content that Copilot and agents will draw on - accuracy, currency, authority, completeness, clarity, consistency and metadata - and runs sampled quality checks on collections to produce a quality rating and a remediation list for owners. Use when answers are wrong because the content is, when a collection needs a quality baseline, or when owners ask what good looks like.
knowledge-management:knowledge-classificationdraftClassifies knowledge collections by sensitivity, intended audience and knowledge status (authoritative, reference, working, archive) using the organisation's existing scheme, then aligns labels, permissions and AI grounding eligibility with that classification. Use when collections are unlabelled or labelled inconsistently, when audiences are unclear before Copilot or agent rollout, or when owners ask who should see their content.
knowledge-management:knowledge-cleanupdraftPlans a clean-up of redundant, outdated and trivial content in the knowledge estate - triage rules, owner decisions to keep, update, archive or delete, retention checks, sequencing ahead of Copilot and agent rollout, and proof the clean-up worked. Use when duplicates and old versions surface in AI answers, before a Copilot wave reaches a cluttered domain, or when a site needs rationalising before agents ground on it.
knowledge-management:knowledge-gap-analysisdraftFinds and classifies knowledge gaps revealed by unanswered questions, agent fallbacks, wrong answers, user feedback, search failures and service desk themes - missing, unfindable, outdated, contradictory or inaccessible - and routes each to an owner with a fix. Use when agents often say they cannot help, when the same questions keep reaching people, or when feedback shows Copilot cannot find what staff need.
knowledge-management:knowledge-grounding-standardsdraftDefines the organisation-wide standard that knowledge must meet before any agent may ground on it - entry criteria scaled by risk tier for ownership, quality, classification, access, review and authoritative status - plus the approved-source designation and how it is withdrawn. Use when makers point agents at any content they like, when each agent team invents its own bar, or when governance needs one grounding policy.
knowledge-management:knowledge-inventorydraftBuilds and maintains a register of the organisation's knowledge sources - sites, libraries, wikis, Teams files, connector content and curated collections - recording domain, owner, audience, sensitivity, condition, review date and which Copilot or agent uses depend on each. Use when nobody can say where authoritative knowledge lives, before choosing agent sources at scale, or when the register is stale.
knowledge-management:knowledge-lifecycledraftDesigns the end-to-end knowledge lifecycle - create, approve, publish, use, review, update, archive and dispose - with the states, metadata, roles and controls at each stage, so knowledge AI grounds on enters correctly, stays current and leaves cleanly. Use when there is no consistent route from draft to published to archived, when superseded content lingers beside current, or when setting knowledge management up for AI.
knowledge-management:knowledge-owner-modeldraftDefines the knowledge ownership model for the organisation - domain owner, content owner, steward, subject matter reviewer and site administrator - with responsibilities, decision rights, time expectations, succession and escalation, so every source AI grounds on has an accountable role. Use when sources have no owner, when owners leave and content goes stale, or when IT is being asked to vouch for business content.
knowledge-management:knowledge-readiness-assessmentdraftAssesses how ready the organisation's whole knowledge estate is for Copilot and agents across ownership, currency, authority, findability, permissions, classification and lifecycle, rates each domain on evidence and sets a prioritised remediation plan. Use when a Copilot rollout or agent programme is being planned, when a sponsor asks whether content is fit for AI, or when answers across many agents keep failing for knowledge reasons.
knowledge-management:knowledge-review-cyclesdraftSets how often each kind of knowledge is reviewed, what events trigger an early review, what a review must check and record, and how overdue reviews escalate, with reviews linked to the agents and Copilot scenarios that depend on the content. Use when content has no review dates, when reviews are agreed but never happen, or when a policy change is not reflected in AI answers.
knowledge-management:sharepoint-knowledge-designdraftDesigns SharePoint hubs, sites, libraries, pages, content types and metadata as AI-ready knowledge sources - authoritative content separated from working areas, permissions aligned to audience, consistent metadata and clear archive locations. Use when a knowledge domain is being set up or restructured for Copilot and agents, when one site mixes policy with drafts, or when makers cannot point an agent at a clean library.
Microsoft 365 Copilot first version, draft
Help organisations introduce Microsoft Copilot as an everyday working capability.
m365-copilot:copilot-chat-rolloutdraftPlans a safe workforce-wide rollout of Copilot Chat as the organisation's approved everyday AI assistant, deciding settings for web grounding, agents and app placement, approved-use guidance, displacement of unapproved AI tools, light-touch enablement and the route to licensed Copilot. Use when Copilot Chat is to be made available to everyone, when staff use consumer AI tools for work, or when Chat was enabled with no plan.
m365-copilot:copilot-for-frontlinedraftAssesses, scenario by scenario, whether Copilot use is feasible and safe for a frontline role - shared-device sign-in, connectivity, the mobile surface, the currency of procedures it would answer from and safety-critical limits - and records a constrained scenario set and safety boundary. Use when a frontline scenario is proposed, when safety-critical or regulated procedures are involved, or when shared or personal devices are in play.
m365-copilot:copilot-for-leadersdraftDefines the scenario set and safeguards for leaders' own Copilot use - meeting preparation, mail triage, long papers, staff messages, working rules for delegates - and the firm limit on using Copilot near decisions about people, each scenario tested on real leadership work. Use when leaders need scenarios that fit their week, when delegate rules are unclear, or when Copilot has been used near a decision about a person.
m365-copilot:copilot-role-scenariosdraftTurns a prioritised role task into worked Copilot scenarios across the Microsoft 365 apps, specifying the app surface, the work context it draws on, the prompt pattern, the verification step, prerequisites and known limits, each tested hands-on with people in the role. Use when a role needs concrete app-by-app guidance, when scenarios fail in practice, or when a role's apps or content change.
m365-copilot:copilot-technical-readinessdraftChecks the technical readiness of a tenant and a target audience for Microsoft Copilot - identity and licence assignment, Microsoft 365 apps and update channels, tenant-wide oversharing controls, sensitivity labels, admin settings for web content and agents, audit and retention - and records owned fixes. Use when IT is asked to confirm prerequisites before a rollout, when a pilot hits technical faults, or when settings were never reviewed.
m365-copilot:copilot-usage-reportingdraftSets up and interprets Microsoft's Copilot usage reports and dashboards responsibly - who may access them, privacy and name-concealment settings, minimum group sizes, what each metric can and cannot show - and produces aggregate team or role reports with stated caveats. Use when usage reports are being switched on or shared, when a sponsor asks for usage figures, or when someone asks to see individual activity.
m365-copilot:department-scenario-designdraftWorks with a department head and process owners to redesign a department workflow that spans several roles around Copilot, agreeing where Copilot helps at each step, the shared team conventions it needs, the human checks, the department's data boundaries and a department-level measure. Use when a function such as finance or HR wants a joined-up way of working with Copilot, or when individual role scenarios do not add up.
m365-copilot:m365-copilot-rolloutdraftPlans the wave-by-wave rollout of licensed Microsoft 365 Copilot, choosing which roles receive licences first, the technical and work-context gates each wave must pass, licence allocation and reallocation rules agreed at role level, wave exit criteria and the rollout plan that ties them together. Use when licences are being allocated, when a wave is about to start, or when licences sit unused.
m365-copilot:prompting-practicedraftTeaches people to prompt Microsoft Copilot effectively and safely - stating goal, context, sources and expected output, iterating, choosing what not to enter - and to verify outputs by checking citations, numbers and omissions before use. Produces a prompting guide, a verification checklist and prompt library standards. Use when users get generic or wrong answers, when a prompt library is being built, or when verification habits are weak.
m365-copilot:work-context-readinessdraftPrepares the everyday work context licensed Copilot draws on for a team or role - where files are stored, how sites and libraries are shared, whether meetings are recorded or transcribed, how mail and chat are used - and fixes the gaps that make answers thin or expose content. Use when a group is about to receive Copilot, when answers miss obvious content, or when a team's sites look overshared.
Microsoft 365 Agents first version, draft
Turn organisational knowledge and repeatable information needs into appropriately scoped, owned and tested agents with a managed lifecycle.
m365-agents:agent-groundingdraftConfigures and controls what a Microsoft 365 agent grounds on - choosing specific sites, libraries, files or connectors rather than broad locations, deciding on web content, restricting answers to named sources, checking permission trimming and citations - and keeps a controlled knowledge list with owners and change approval. Use when a maker is adding knowledge to an agent, when answers come from the wrong places, or when knowledge changes.
m365-agents:agent-instructionsdraftWrites and maintains the instructions that govern a Microsoft 365 agent - role, audience, scope, how to use and cite knowledge, uncertainty and refusal behaviour, redirects, tone, response format and conversation starters - under version control with a change log and a regression run after every edit. Use when a brief is ready to build, when an agent misbehaves, or when someone wants to edit a live agent's instructions.
m365-agents:agent-lifecycledraftManages an individual Microsoft 365 agent after release through defined states - live, under change, under review, deprecated and retired - with change control, scheduled and triggered reviews, responses to platform changes, and a retirement path that tells users and disposes of the agent cleanly. Use when a live agent needs changing, when its review date arrives, when its owner or knowledge lapses, or when it should be retired.
m365-agents:agent-opportunity-assessmentdraftTests whether a request that already points to an agent really warrants a dedicated, owned agent rather than a shared prompt, a scenario or an existing agent, and if so which kind - knowledge, task or autonomous - and which build path tier, recorded as a short assessment for intake. Use when a team asks for an agent, when several teams want similar agents, or when a prompt pattern is spreading and might become one.
m365-agents:agent-ownershipdraftAssigns and records the business owner, knowledge owners, technical owner, maker and support route for an individual agent, with deputies, written acceptance, succession on role change and handling of ownerless agents, kept on an agent card. Use when an agent is being built or released, when its owner leaves or changes role, or when the inventory shows agents nobody claims.
m365-agents:agent-release-readinessdraftRuns the release gate for an agent, confirming owner, support route, sharing scope matched to risk tier, reviewer evidence, communications, monitoring, review date and retirement criteria, and registers the agent in the solution register. Use when an agent has passed testing and someone wants to share it, when its audience is about to widen, or when a live agent has no clear owner.
m365-agents:agent-testingdraftBuilds a proportionate test plan and a small evaluation set of real user questions with expected answers, covering groundedness, refusal and out-of-scope behaviour, permission trimming across different users and regression after change, against a threshold the business owner agrees. Use when an agent is ready for testing, before widening its audience, or after its instructions or knowledge change.
m365-agents:agent-use-case-designdraftTurns an approved agent opportunity into an agent brief covering audience, purpose, scope and exclusions, instructions approach, knowledge sources, actions, the build-or-reuse decision, owner, support and success measures. Use when an idea has passed suitability as an agent, when a maker wants to start building, or when an existing agent needs its scope rewritten.
m365-agents:agent-value-measurementdraftMeasures whether an individual agent delivers the value its hypothesis promised, combining audience-level usage, resolution and answer-quality signals, user feedback and the business outcome measure against the baseline, and produces a value report for the agent's review. Use when an agent has been live long enough to judge, when its review date approaches, or when someone claims an agent is or is not worth keeping.
m365-agents:knowledge-source-readinessdraftAssesses each knowledge source an agent will ground on for owner, currency, accuracy, permissions and oversharing, classification and review cycle, and records a ready, ready-with-conditions or not-ready verdict. Use when an agent brief names its sources, when a maker wants to add a site or library as knowledge, or when an agent starts giving outdated or surprising answers.
Copilot Cowork first version, draft
Design safe, effective delegation of complex work with explicit context, checkpoints and review.
copilot-cowork:cowork-adoptiondraftPlans and drives adoption of Copilot Cowork in teams - choosing starter tasks, building delegation habits, publishing validated task designs, enabling Champions and team leads, and measuring outcomes at team level - so Cowork becomes a trusted way of working rather than a novelty. Use when Cowork is reaching a new team, when early use stalls after the first weeks, or when a sponsor asks how teams will learn to delegate well.
copilot-cowork:cowork-governancedraftSets the organisation's governance for Copilot Cowork - who may use it and for what, which plugins, skills and custom instructions are approved, how shared task designs and skills are owned, published and retired, and which actions always need approval. Use when Cowork is being enabled, when teams start sharing skills or plugins, or when someone asks who approved a Cowork customisation now in wide use.
copilot-cowork:cowork-use-case-assessmentdraftAssesses whether a specific piece of work suits delegation to Copilot Cowork, or is better served by Copilot Chat, an agent, an automation, a Copilot Code build or no AI, by testing outcome clarity, verifiability, context access and consequence. Use when a person or team proposes handing a task to Cowork, when Cowork results disappoint, or when a team cannot decide between Cowork and an agent.
copilot-cowork:delegated-work-reviewdraftSets and applies the review a person carries out on a finished Copilot Cowork output before it is sent, filed, published or acted on, checking it against the definition of done, its sources, the audience and any actions taken, with a proportionate depth by consequence. Use when someone is about to use delegated output, when errors from Cowork reached others, or when a team needs a shared standard for checking delegated work.
copilot-cowork:grounding-readinessdraftChecks that the specific files, folders, mail, meetings and data a Copilot Cowork task will draw on are current, single-version, owned and shared no wider than they should be, and gives each a ready, ready-with-conditions or not-ready verdict before the task runs. Use when a task design names its sources, when Cowork outputs quote outdated or conflicting material, or before a shared task design is reused by others.
copilot-cowork:human-checkpoint-designdraftDesigns the human checkpoints inside delegated Copilot Cowork work - where the run pauses, who confirms, what evidence they see, what they may change and what happens on rejection - so approvals are real decisions rather than reflex clicks. Use when a Cowork task will send, file or change something, when a checkpoint is being skipped or rubber-stamped, or when a reviewer asks how people stay in control of delegated work.
copilot-cowork:multi-step-work-designdraftBreaks a delegated Copilot Cowork task into ordered steps, each with a named intermediate output, its inputs, a completion test and a safe restart point, so long-running work can be followed, checked and resumed. Use when a Cowork task spans several sources or stages, when long runs go off course part-way, or when a team wants a repeatable structure for complex delegated work.
copilot-cowork:task-context-designdraftDesigns the context pack a Copilot Cowork task needs - the goal, the definition of done, the named inputs and sources, constraints, examples and the custom instructions or skills to apply - and records it as a reusable task design. Use when a delegated task gives vague or inconsistent results, when a team wants to share a task design others can reuse, or before running a Cowork task that draws on several sources.
copilot-cowork:work-delegation-designdraftDesigns the delegation boundary for work handed to Copilot Cowork, deciding which parts Cowork does, which stay with people, who stays accountable and what authority Cowork is given to read, draft, send or change. Use when a team has a task that suits Cowork and must agree what to hand over, when delegated work has overstepped, or when a manager asks who answers for delegated output.
copilot-cowork:workflow-validationdraftValidates a Copilot Cowork task design or shared workflow before people rely on it, by running it on representative and deliberately awkward cases, planting errors before checkpoints and checking outputs against the definition of done, then recording a fit-to-rely verdict. Use when a task design is about to be shared or reused, when a step is moving from drafting to acting, or after a material change to a skill or its sources.
Copilot Code first version, draft
Translate desired outcomes into governed software prototypes and production-ready engineering work.
copilot-code:ai-assisted-prototypingdraftSets the boundaries for prototyping with Copilot Code - a question, a time box, a sandbox, safe data, no production connections and an end decision to discard, rebuild or promote - so quick prototypes never become unowned software. Use when a team wants to try an idea in code with AI, when a prototype has started being used for real, or when sponsors ask how to encourage experiments safely.
copilot-code:application-validationdraftValidates an application built with Copilot Code against its solution brief, tracing every acceptance criterion to reviewed tests, negative and edge cases, non-functional checks and business owner acceptance, and records a pass, conditional pass or fail. Use when an AI-built application is feature-complete, when a business owner must accept a build, or when generated tests look thorough but nobody has checked them against requirements.
copilot-code:code-deployment-readinessdraftChecks that an AI-built application is ready to deploy and run - validated, security-reviewed, deployed through a pipeline to approved hosting, with least-privilege identity, monitoring, a rollback plan, a support route, named owners and recorded release approval for its risk tier. Use when an AI-built application is about to go live, when a prototype is being promoted, or when an application is running somewhere nobody approved.
copilot-code:code-solution-briefdraftWrites the solution brief that frames an AI-assisted software build - outcome, users, scope and exclusions, data and systems touched, constraints and non-functional needs, acceptance criteria, owners and route to production - so the coding agent and reviewers work to one agreed definition. Use when a Copilot Code build has been approved, when a prototype is being promoted, or when a build is drifting because nobody wrote down what it is for.
copilot-code:code-use-case-assessmentdraftAssesses whether a desired outcome genuinely needs software built with Copilot Code, or is better met by configuration, an existing product, a Power Platform app or flow, an agent, a Cowork task or no build, and whether the organisation can own what gets built. Use when a business team asks AI to build them an app or script, when a prototype is proposed, or when someone asks why not just have AI write it.
copilot-code:generated-code-reviewdraftSets and applies code review standards for changes produced by an AI coding agent - reviewable size, understanding before approval, tests checked against the work item, conventions and no unnecessary changes - so human review stays meaningful at agent speed. Use when a team starts accepting agent pull requests, when reviews have become rubber stamps, or when generated changes are merging that nobody on the team can explain.
copilot-code:generated-code-security-reviewdraftReviews AI-generated code for weaknesses coding agents commonly introduce - injection, broken authorisation, secrets, unsafe dependencies, broad permissions and data leakage - and classifies each finding as blocking or advisory with a fix. Use when an AI-built application or significant generated change nears release, when generated code touches identity or data access, or when a new dependency was added by the agent.
copilot-code:maintainability-reviewdraftReviews whether an AI-built application can be understood, changed and kept secure by people over its life - ownership, comprehension without the agent, documentation, tests, dependency upkeep and spread of knowledge - and recommends keep, invest, consolidate or retire. Use when an AI-built application reaches its scheduled review, when its owner is leaving, or when a growing estate of AI-built tools needs triage.
copilot-code:repository-contextdraftPrepares the repository context an AI coding agent works within - custom instructions, conventions, architecture notes, approved dependencies, protected areas, test commands and never-touch rules - so generated changes follow the organisation's standards. Use when a repository is first opened to a coding agent, when agent output keeps ignoring conventions, or when a team asks how to stop the agent touching secrets or sensitive code.
copilot-code:requirements-to-builddraftTurns an agreed solution brief into small, ordered, testable work items an AI coding agent and its human reviewers can deliver - each with a clear outcome, acceptance tests, boundaries and dependencies - so generated changes stay reviewable. Use when a Copilot Code build is about to start, when agent-generated pull requests are too large to review, or when work items keep coming back because acceptance was unclear.
Copilot Studio first version, draft
Design and deliver low-code and enterprise agents with appropriate actions, data, ALM and lifecycle controls.
copilot-studio:conversation-designdraftDesigns how a Copilot Studio agent talks to people - greeting and AI disclosure, tone, clarifying questions, confirmations, error and fallback messages, escalation and live-agent hand-off with context, and ending a conversation well - so exchanges are short, honest and recoverable. Use when writing an agent's conversational behaviour, when users abandon or go round in loops, or when a hand-off to people needs designing.
copilot-studio:dataverse-integrationdraftDesigns how a Copilot Studio agent works with Dataverse - tables as knowledge for questions about business records, reads and writes through tools, and the agent's own configuration and conversation data - covering table and column selection, security roles, capacity questions and environment placement. Use when an agent must answer from or update Dataverse records, or when agent data storage needs planning.
copilot-studio:multi-agent-designdraftDesigns Copilot Studio solutions in which agents work together - child agents, connected agents reused across teams and calls to agents on other Microsoft platforms - deciding when to split, how the parent routes and passes context, which identity each uses, who owns each and how failures are contained. Use when one agent has grown too broad, when teams want to reuse each other's agents, or when an agent must hand work to another.
copilot-studio:power-automate-actionsdraftDesigns Power Automate flows that a Copilot Studio agent calls as tools - when a flow is the right action, its input and output contract, response time within a conversation, the identity it runs under, error returns the agent can explain, solution packaging and flow ownership. Use when an agent needs a multi-step or cross-system action, when a flow called by an agent times out or fails silently, or when a maker wants to reuse an existing flow.
copilot-studio:studio-actions-and-toolsdraftDesigns the tools a Copilot Studio agent can call - connector actions, agent flows, prompts, custom connectors, REST APIs and MCP servers - with names and descriptions the orchestrator can use, typed inputs and outputs, read versus write, user confirmation, identity and failure behaviour. Use when an agent must do more than answer, when it calls the wrong tool or fills inputs badly, or when a write action needs safeguards.
copilot-studio:studio-agent-designdraftTurns an agreed agent brief into a Copilot Studio solution design - orchestration approach, topics, knowledge, tools and flows, authentication, channels, environment and solution packaging, and the run model - recording each design decision with the alternatives rejected. Use when a brief has chosen Copilot Studio, when a maker is about to build without a design, or when a growing agent needs its architecture reset.
copilot-studio:studio-agent-lifecycledraftKeeps the technical estate of Copilot Studio agents healthy from creation to retirement - owners and co-owners, orphaned, inactive or duplicate agents, periodic recertification, Microsoft platform changes, and retiring an agent with its flows, connections, environment variables and stored data. Use when the number of Studio agents is growing, when a maker leaves, or when an agent has gone unused for months.
copilot-studio:studio-almdraftSets application lifecycle management for Copilot Studio agents - solutions and publishers, environment variables and connection references, source control, pipelines between development, test and production, managed versus unmanaged imports, versioning, change control and rollback. Use when an agent must move beyond one environment, when changes are made directly in production, or when several makers work on the same agent.
copilot-studio:studio-analyticsdraftUses Copilot Studio analytics and conversation records to improve a live agent - sessions, outcomes such as resolved, escalated and abandoned, unanswered questions, knowledge and tool use, satisfaction and recurring themes - through a regular improvement review at aggregate level that turns findings into prioritised fixes. Use when an agent is live and its owner asks how it is doing, when resolution falls, or when deciding what to fix next.
copilot-studio:studio-authenticationdraftDesigns how a Copilot Studio agent knows who the user is and whose identity it uses - the agent's authentication setting, Microsoft Entra sign-in or another identity provider, single sign-on per channel, end-user versus maker or service credentials for knowledge and tools, and anonymous use. Use when choosing an agent's authentication setting, when users are asked to sign in repeatedly, or when an action runs as the maker.
copilot-studio:studio-connectorsdraftChooses the connectors a Copilot Studio agent depends on - prebuilt, certified or custom - and governs their use in the agent through connection references, the account each connection runs under, data policy grouping, ownership of custom connectors and their change control. Use when a maker needs a connector the agent does not yet use, when a custom connector is proposed, or when a data policy blocks an agent.
copilot-studio:studio-deploymentdraftPublishes a released Copilot Studio agent to its channels - Teams and Microsoft 365 Copilot, websites, custom apps and others - configuring per-channel authentication, sharing to the approved audience, admin approval and availability, channel-specific behaviour and a staged rollout with a rollback route. Use when an agent has passed its release gate, when it is being added to a new channel, or when users cannot find or open it.
copilot-studio:studio-environmentsdraftApplies the organisation's environment strategy to Copilot Studio work - the concrete personal, team, test and production environments makers use, environment routing for new makers, Managed Environment and agent settings per zone, security groups, data policies and how agents move between zones. Use when makers start building agents, when agents pile up in the default environment, or when a team asks for its own development space.
copilot-studio:studio-knowledge-architecturedraftDesigns the knowledge layer of a Copilot Studio agent - which ready sources to connect and by which knowledge type, how each is scoped, described and permission-trimmed, whether content is live or uploaded, how answers cite sources and how the layer is refreshed and owned. Use when connecting knowledge to a Studio agent, when answers come from the wrong source, or when a maker wants to upload copies of documents.
copilot-studio:studio-testing-and-evaluationdraftPlans layered testing of a Copilot Studio agent - topic paths, orchestration choices, knowledge answers, tool and flow calls, authentication per channel, failure handling and regression after change - using the authoring test tools, repeatable evaluation sets and owner-agreed thresholds in a test environment. Use when a Studio agent with tools, topics or several sources needs testing, or when a change must be regression-tested.
copilot-studio:topics-and-orchestrationdraftDesigns how a Copilot Studio agent decides what to do - which requests generative orchestration handles from the descriptions of knowledge, tools, topics and child agents, which need authored topics with fixed steps, and how triggers, variables, system topics and redirects fit together. Use when planning an agent's topics, when it picks the wrong topic or tool, or when authored topics keep multiplying.
Microsoft Foundry first version, draft
Engineer specialist and custom AI solutions where configured agents are insufficient.
ai-foundry:agentic-workflow-designdraftDesigns multi-step agentic workflows in Microsoft Foundry with bounded autonomy - the steps, which are deterministic and which are agentic, the decisions each agent may take alone, checkpoints, budgets, stop conditions and recovery. Use when a process spans several agents or steps, when a team wants an agent to run unattended, or when an autonomous prototype keeps looping or overreaching.
ai-foundry:foundry-agent-architecturedraftArchitects a single agent, or a small set of cooperating agents, on Foundry Agent Service - its responsibilities, instructions, model, tools, knowledge, state, identity and the surfaces it is published to - so it can be built, secured and operated. Use when a Foundry agent has been approved, when an agent prototype needs a production design, or when a Copilot Studio agent is being rebuilt in Foundry.
ai-foundry:foundry-cost-managementdraftEstimates, budgets and controls the running cost of a Microsoft Foundry solution - model consumption, reserved capacity, search, storage, compute and monitoring - with cost per outcome, tagging, budgets, alerts and named owners. Use when a Foundry build needs a cost estimate, when consumption spend is rising or unexplained, or when a team is choosing between pay-per-use and reserved capacity.
ai-foundry:foundry-deployment-architecturedraftChooses how a Microsoft Foundry solution is deployed - model deployment type, data processing location, capacity and quota, resource and project layout, environments, network exposure and failover - and records each choice with its trade-off. Use when a chosen model needs deploying, when data must stay in a geography, when throttling or regional outages hit, or when a prototype must move into governed environments.
ai-foundry:foundry-grounding-strategydraftChooses how a Microsoft Foundry solution grounds its answers and actions - supplied context, retrieval over indexed content, live lookups through tools, web grounding or none - per knowledge need, with ownership, permissions, freshness and citation decided before retrieval is engineered. Use when a Foundry agent needs organisational knowledge, when answers are confidently wrong, or when a team proposes fine-tuning to teach facts.
ai-foundry:foundry-observabilitydraftDesigns tracing and observability for Microsoft Foundry agents and applications in production - what each run records across model calls, retrieval, tool calls and checkpoints, how content is protected, which signals and online evaluations show quality drifting, and who looks. Use when a Foundry solution is heading to production, when a misbehaving agent cannot be explained, or when traces capture more personal data than anyone agreed.
ai-foundry:foundry-production-readinessdraftAssembles and tests the evidence that a custom Microsoft Foundry solution is ready for live use - evaluation and safety results, security and identity, deployment and capacity, observability, cost controls, resilience, support, model lifecycle and rollback - and records a go, go with conditions or no-go. Use when a Foundry build is nearing release, when a pilot is about to widen, or when a partner hands over a solution.
ai-foundry:foundry-prompt-engineeringdraftEngineers the system prompts and prompt templates of a Microsoft Foundry model or agent for production - structure, scope and refusal rules, grounding and citation instructions, output contracts, injection resistance - and keeps them versioned, reviewed and regression-tested like code. Use when a Foundry prototype prompt must become production-grade, when outputs drift after a prompt edit, or when prompts change outside source control.
ai-foundry:foundry-safety-evaluationdraftConfigures content safety controls for a Microsoft Foundry solution - input and output filtering, prompt attack detection, groundedness and protected material checks, blocklists - and runs the builder's safety evaluations against the solution's harm profile so the release gate receives evidence. Use when a Foundry model or agent is being prepared for release, when filters block legitimate work, or after a harmful output.
ai-foundry:foundry-solution-assessmentdraftTests whether a need that has reached the engineered-solution rung really justifies a Microsoft Foundry build, and whether the organisation can own, fund and operate it, recording the case and its preconditions on a solution design. Use when suitability points to Foundry, when a team proposes pro-code AI before trying configured agents, or when a sponsor asks what a Foundry build commits the organisation to.
ai-foundry:foundry-tool-designdraftDesigns the individual tools and functions a Foundry agent can call - name, description, input and output schema, side effects, identity, confirmation, error behaviour and limits - so the model calls them correctly and they cannot be misused. Use when an agent's tool list is agreed and each tool needs a contract, when an agent calls tools wrongly, or before a write action goes live.
ai-foundry:mcp-integrationdraftIntegrates Model Context Protocol servers with Microsoft Foundry agents safely - vetting who runs each server, allowing only the tools needed, authentication and identity, approval for consequential calls, pinning versions and handling changes in what a server offers. Use when a team wants to connect an MCP server to a Foundry agent, when an internal service should be exposed over MCP, or when a connected server changes its tools.
ai-foundry:model-evaluationdraftRuns the comparative evaluation that decides which candidate model, prompt or configuration performs a specific task best in a Microsoft Foundry solution - task-specific test set, quality and tool-calling measures, latency and cost per task, thresholds set before results, repeated runs and a written comparison. Use when a model shortlist needs testing, when a model version is being replaced, or when two configurations must be compared fairly.
ai-foundry:model-selectiondraftShortlists and selects models from the Microsoft Foundry catalogue, including partner and third-party models, against task fit, data handling, hosting terms, lifecycle, latency and cost, and records the choice and its fallback on a model selection record. Use when a Foundry solution needs a model, when a team asks for a specific third-party model, or when a model is being retired or replaced.
ai-foundry:retrieval-designdraftDesigns the retrieval layer of a custom Microsoft Foundry solution - which content is indexed, how it is cleaned and chunked, which metadata and embeddings are kept, keyword, vector or hybrid search with reranking, permission trimming, freshness and how passages reach the prompt with citations. Use when a Foundry solution needs its own search index, when answers miss content that exists, or when retrieval returns content users should not see.
Automation first version, draft
Combine AI with deterministic workflow automation where repeatability and integration matter.
automation:automation-assessmentdraftAssesses a process put forward for automation step by step - volume, stability, input structure, rules, exceptions and systems - and decides which steps suit a deterministic flow, which genuinely need an AI step, which need a desktop flow and which should stay human. Use when a team asks to automate a process, when an automation request already assumes AI, or when several automation candidates need consistent treatment.
automation:automation-connector-governancedraftApplies connector and data policy governance to individual automations in delivery - which connectors a flow may use in its environment, whose connections run it, custom connectors and AI services, and how a maker requests and justifies an exception. Use when a flow needs a connector its environment blocks, when production flows run on personal connections, or before an automation moves to production.
automation:automation-monitoringdraftDesigns monitoring for an individual automation before hand-over - run failures, missing runs against expected volume, exception queue ageing, AI step quality signals such as low confidence and human override rates, connection expiry - and routes each alert to a named owner. Use when an automation is about to go live, when flows stop without anyone noticing, or when an owner asks what to watch.
automation:automation-process-designdraftRedesigns a business process before it is automated - removing waste, standardising inputs, writing down decision rules, defining exceptions and owners - and produces a future-state design that marks each step as human, flow, AI or desktop flow. Use when an assessment says a process is not ready to automate, when teams run the same process differently, or before build starts on any automation with AI steps.
automation:automation-valuedraftMeasures the value an automation delivers against its baseline - cycle time, handling effort, errors and rework, throughput - net of human review, exception handling, run cost and support, and reports it to the process owner without counting runs as value. Use when an automation goes live, when a sponsor asks what an automation saved, or when deciding whether to extend, fix or retire it.
automation:desktop-automationdraftDecides when desktop flows that drive a user interface are justified for legacy systems with no connector or API, and sets the conditions for using them with care - attended or unattended runs, machines, credentials, fragile selectors, AI on screen content, ownership and an exit plan. Use when a step can only be done through a screen, when desktop flows keep breaking after updates, or when unattended runs are proposed.
automation:exception-handlingdraftDesigns how an automation with AI steps handles what goes wrong - transient failures and retries, permanent technical faults, business exceptions, invalid or low-confidence AI output, content filtering and partial runs - so every failure is caught, routed to an owner and recoverable. Use when building an automation, when flows fail silently or leave half-finished work, or when exception queues grow without an owner.
automation:flow-design-patternsdraftSets the build patterns for robust Power Automate cloud flows that include AI steps - triggers, solution-aware structure, connection references, environment variables, structured AI outputs validated before use, idempotency, concurrency and error scopes. Use when a maker starts building an approved automation design, when a flow with an AI step is fragile, or when the CoE needs standard flow patterns.
automation:human-in-the-loopdraftDesigns the human approval and review steps inside an automation - where a person checks AI output before it takes effect, what they see, how long they have, who covers absence, and how to stop reviews becoming rubber stamps. Use when an automated flow acts on AI output, when approvals sit unanswered or are approved without reading, or when a reviewer asks what checks an automation needs.
AI Engineering first version, draft
Provide pro-code engineering patterns shared across custom AI solutions.
ai-engineering:ai-api-and-toolingdraftDesigns the APIs and shared tooling that custom AI solutions call or expose - contract-first interfaces, narrow tool surfaces a model can use correctly, input validation, user-scoped authorisation, idempotent writes, rate limits, versioning and errors a model can act on. Use when building a service an agent will call as a tool, when exposing an AI capability as an API, or when shared tool libraries are needed across solutions.
ai-engineering:ai-ci-cddraftDesigns CI/CD pipelines for custom AI solutions that build, test, evaluate and deploy code, prompts, model configuration, tools and infrastructure together, with evaluation and safety gates, environment promotion, approvals, model version upgrades and rollback. Use when a pro-code AI solution needs a release pipeline, when prompts or models change outside the pipeline, or when a model version is being retired.
ai-engineering:ai-engineering-standardsdraftSets and maintains engineering standards for custom AI work - minimum practices by risk tier for repositories, identity, tools, testing, evaluation, pipelines, telemetry, performance, resilience and ownership - with a checklist, exception route and review cycle. Use when pro-code AI work starts, when teams build AI solutions inconsistently, or when standards need refreshing after platform change.
ai-engineering:ai-performance-engineeringdraftEngineers the latency and throughput of custom AI solutions against agreed targets - latency budgets per step, token budgets, streaming, caching, parallel calls, right-sized models, quota and capacity planning, and load testing - while protecting quality and cost. Use when an AI solution is too slow, when usage is about to grow, when throttling appears under load, or before a tier 3 solution goes to production.
ai-engineering:ai-resiliencedraftEngineers resilience into custom AI solutions - timeouts, bounded retries, circuit breakers, evaluated fallback deployments or models, degraded modes, content filter and malformed output handling, idempotent tools - so they fail safely and honestly. Use when building a pro-code AI solution, after an outage or throttling incident, or when nothing defines what happens if the model is unavailable.
ai-engineering:ai-solution-repositorydraftSets the repository structure for custom AI solutions so that code, system prompts, tool definitions, evaluation datasets, infrastructure as code and per-environment configuration are versioned together, owned and reviewable, with no secrets in source. Use when starting a pro-code AI solution, when prompts live outside source control, or when a team needs a standard repository template for Foundry or Azure AI work.
ai-engineering:ai-solution-testingdraftSets the testing strategy for pro-code AI solutions - unit tests around non-model code, contract tests for tools, recorded or stubbed model responses, integration, adversarial and failure-path tests - and how each layer fits alongside evaluation. Use when planning tests for a Foundry or Azure AI solution, when tests are flaky because they call live models, or when testing stops at a few manual prompts.
ai-engineering:ai-telemetrydraftDesigns the telemetry a custom AI solution emits - traces across orchestration, model, retrieval and tool calls, versions, tokens, latency, safety and quality signals and correlation ids - and decides what prompt and response content is captured, redacted and kept. Use when building a pro-code AI solution, when failures cannot be diagnosed, or when logs hold more personal data than they should.
ai-engineering:evaluation-harnessdraftBuilds the automated evaluation harness for a custom AI solution - dataset loading, repeatable runs against pinned models and prompts, code-based and model-graded evaluators, scoring against agreed thresholds, results history and reports - so quality is checked on every change. Use when an AI solution needs repeatable evaluation in its pipeline, when manual spot checks no longer scale, or when evaluation results cannot be reproduced.
ai-engineering:secrets-and-identitydraftEngineers the identities and secrets of a custom AI solution - managed identities and keyless access to model endpoints, on-behalf-of user access for data and tools, agent and workload identities, least-privilege role assignments, a managed secret store, rotation and secret scanning. Use when building a Foundry or Azure AI solution, when keys appear in code or configuration, or when an agent's service identity has broad access.
AI Architecture first version, draft
Select appropriate patterns and define target architecture across Microsoft 365, Power Platform, Azure and data services.
ai-architecture:ai-identity-designdraftDesigns the identities an AI solution uses end to end - signed-in users, agent identities, service principals and managed identities - mapping which identity each hop runs under, how consent, scopes and conditional access apply, and who owns each non-human identity. Use when an agent or AI solution calls systems on someone's behalf, when an agent needs its own identity, or when nobody can say whose rights a solution acts with.
ai-architecture:ai-integration-patternsdraftChooses how an AI solution connects to business systems - standard or custom connectors, flows as actions, APIs, MCP servers, events, Dataverse or Graph - deciding direction, identity, synchronous or asynchronous timing, write confirmation and failure behaviour for each interface. Use when an agent or AI solution must read from or act in other systems, when an interface list is growing unplanned, or when a write action needs a safe pattern.
ai-architecture:ai-non-functional-requirementsdraftSpecifies the non-functional requirements of an AI solution - quality thresholds, latency, availability, capacity, cost per use, resilience, observability, security, retention, accessibility and supportability - scaled to the risk tier, each measurable with an owner. Use when a solution moves from idea to design, when a pilot is about to become a service, or when nobody can say how good, fast or available the solution must be.
ai-architecture:ai-reference-architecturedraftMaintains a small set of reference architectures for recurring Microsoft AI patterns - knowledge agent, business-data agent with actions, AI-assisted automation, custom Foundry solution - each with components, identity and data flows, mandatory controls, known limits and an owner. Use when the same design questions keep recurring, when reviewers need a baseline to judge conformance, or when a new pattern has been proven and should be reused.
ai-architecture:environment-topologydraftDesigns the technical topology a set of AI solutions runs across - Microsoft 365 tenant settings, Power Platform environments, Azure subscriptions and resource groups, regions and the paths between them - so production is separated and data stays within agreed boundaries. Use when the first pro-code or multi-environment AI solution is planned, when Azure and Power Platform estates grow apart, or when data residency affects placement.
ai-architecture:grounding-patternsdraftChooses how an AI solution grounds on organisational data - Microsoft 365 work context, curated SharePoint knowledge, Graph connectors, Dataverse or structured data, or a custom retrieval index - judged on permissions, freshness, data movement and ownership. Use when a solution's knowledge spans several stores, when content would be copied or indexed outside its source, or when teams build overlapping indexes of the same data.
ai-architecture:platform-selectiondraftSelects the Microsoft platform for one solution once suitability is agreed - Agent Builder, Copilot Studio, Power Platform automation, Copilot Code or Microsoft Foundry - weighing audience, data, actions, identity, operability and skills, and records the choice in an architecture decision record. Use when a solution has a pattern but no platform, when makers and engineers disagree where to build, or when a build has outgrown its platform.
Security and Privacy Reviewer-class first version, draft
Reviewer-class department that assesses identity, access, data exposure, connectors, privacy and retention for AI solutions and blocks release while material risks are unresolved.
security-privacy:ai-identity-and-access-reviewdraftReviews the identities an AI solution runs under and the access around it - who can use, edit, share and administer it, agent and service identities, privileged roles, guest access, sign-in controls and what happens when owners leave - and classifies each finding as blocking or advisory with a fix. Use when an agent or AI solution reaches its security gate, when ownership or audience changes, or when nobody knows whose identity an agent uses.
security-privacy:ai-incident-readinessdraftChecks before release that an AI solution's security and privacy incidents can be detected, contained, investigated and reported - signals, a tested way to switch the solution off, evidence available to investigators, responders who understand AI failure modes and a route to the DPO - classifying each gap as blocking or advisory. Use when a solution reaches its security gate, before widening its audience, or after an incident exposed gaps.
security-privacy:ai-interaction-retentiondraftReviews how a specific AI solution stores prompts, responses, transcripts, traces, agent memory and generated files - where each lands, who can read it, which retention and deletion controls apply, and whether audit, eDiscovery and legal hold reach it - classifying each finding as blocking or advisory. Use when a solution reaches its security gate, when transcripts or tracing are switched on, or when nobody can say where conversations are kept.
security-privacy:ai-security-testingdraftReviews the security test plan and evidence for an AI solution - direct and indirect prompt injection, jailbreaks, instruction leakage, exfiltration through outputs and links, tool misuse and privilege escalation - and classifies each finding as blocking or advisory with a fix. Use when an agent with actions reaches its security gate, when it reads untrusted content, or when someone asks whether it can be talked into misbehaving.
security-privacy:connector-reviewdraftReviews the connectors, actions, tools, MCP servers and APIs an agent or automation uses, covering user versus maker authentication, least privilege, data policies, data leaving the tenant and confirmation for write actions, and classifies each finding as blocking or advisory with a fix. Use when an agent gains an action or tool, when a flow uses a new connector, or when a maker shares a connection.
security-privacy:data-exposure-reviewdraftReviews what content an AI solution can reach and reveal - oversharing, permissions, sensitivity labels, the agent's sharing scope, web and public knowledge, and where outputs travel - and classifies each finding as blocking or advisory with a fix. Use when an agent or Copilot rollout reaches its security gate, when a maker adds a large knowledge source, or when sensitive content surfaces in answers.
security-privacy:privacy-assessmentdraftAssesses personal data in an AI solution for lawful basis, purpose limitation, transparency, data minimisation, retention of prompts, responses and transcripts, DPIA screening under UK GDPR and ICO guidance, and people-related uses, classifying findings as blocking or advisory. Use when an agent or AI solution processes personal data, when transcripts are kept, or when AI outputs inform decisions about people.
Responsible AI Reviewer-class first version, draft
Reviewer-class department that identifies impact, accountability, human oversight, unacceptable uses and mitigating controls.
responsible-ai:ai-impact-assessmentdraftAssesses who an AI solution affects - users, people it makes outputs about, workers whose roles change and the organisation - covering intended and foreseeable uses, benefits, harms, severity and likelihood, and the mitigations each needs, then classifies residual concerns as blocking or advisory. Use when a solution reaches its responsible AI gate, when it informs decisions about people, or when a sponsor asks what could go wrong for whom.
responsible-ai:ai-risk-acceptancedraftRuns the formal acceptance of residual AI risk by an accountable executive when a blocking finding is disputed or risk remains after mitigation - who may accept at which tier, the evidence they must see, conditions, expiry and review, and what can never be accepted - and records it in writing. Use when a team and a reviewer disagree, when a tier 4 solution needs sign-off, or when an acceptance is due for review.
responsible-ai:ai-transparencydraftReviews how an AI solution tells people about AI - disclosure that users are dealing with AI, labelling of AI-generated content, what it can and cannot do, where answers come from, and notice to people affected by outputs who never use it - classifying gaps as blocking or advisory. Use when an agent faces customers or staff, when AI content is sent or published, or when people may not know AI shaped an outcome.
responsible-ai:escalation-and-appealdraftReviews the routes people have when an AI solution gets something wrong for them - reaching a person during an interaction, questioning or appealing an AI-influenced outcome, correcting information and reporting harm - including timescales, independent review and learning, classifying gaps as blocking or advisory. Use when outputs affect people, when an agent serves customers, or when complaints about an AI outcome have nowhere to go.
responsible-ai:fairness-reviewdraftReviews whether an AI solution could work worse for some groups or treat them unfairly - quality of service across language, dialect, accessibility and role, allocation of opportunities or resources, and stereotyping in generated content - and checks the testing and data behind it, classifying findings as blocking or advisory. Use when outputs affect people differently, when a solution serves a diverse audience, or when someone suspects bias.
responsible-ai:human-oversightdraftDecides the level of human oversight an AI solution needs - review of every output, approval before action, sampling or monitoring - and reviews whether the reviewers have the time, information, competence and authority to catch and overturn errors, classifying gaps as blocking or advisory. Use when outputs affect people, money or safety, when an agent will act with less supervision, or when human review exists only on paper.
responsible-ai:prohibited-use-reviewdraftScreens a proposed or live AI use against the organisation's prohibited and restricted uses, Microsoft's acceptable use terms and regulation as advised by legal - covert monitoring of staff, emotion inference, social scoring, manipulation, unreviewed decisions about individuals - and records a stop, restrict or proceed verdict. Use when a sensitive idea arrives at intake, when a solution's purpose drifts, or when asked if a use is allowed.
Data Governance first version, draft
Ensure the data used by AI is appropriately owned, classified, accessible, lawful and of sufficient quality.
data-governance:ai-data-access-modeldraftDefines the organisation-wide model for how AI reaches data - when access must be user-delegated, when an agent or service identity may act, which identity patterns each data class allows, who approves access, and how it is reviewed - so every solution starts from approved patterns. Use when agents need data beyond the user's own access, when makers keep inventing access patterns, or when data owners ask what AI may reach.
data-governance:ai-data-inventorydraftBuilds and maintains an inventory of the data assets AI solutions use and create - databases, Dataverse tables, Fabric items, lists, files, connector data and AI-generated records - with owner, purpose, sensitivity, location and the solutions that depend on each. Use when nobody can say which data feeds which agent, before a data source changes or is retired, or when an audit or rights request needs to know where data flows.
data-governance:ai-data-lineagedraftTraces how data travels from systems of record into AI outputs - copies, indexes, embeddings, prompts, tool calls and generated records - so the organisation can say where an answer came from, which solutions a source change affects, and where personal data has spread. Use when a tier 3 or 4 solution needs explainable provenance, when a source changes or is withdrawn, or when a rights request or incident needs the full path.
data-governance:ai-data-qualitydraftSets data quality requirements for structured and operational data used by AI - accuracy, completeness, currency, consistency, uniqueness and meaning - with thresholds by use and risk tier, ways to measure them, owners and remediation, so agents and analytics do not confidently repeat bad data. Use when an agent reads business records, when Copilot analyses tables or semantic models, or when AI answers trace back to wrong data.
data-governance:ai-data-retention-rulesdraftSets the organisation's retention rules for data AI creates or depends on - prompts and responses, transcripts, traces, generated documents, write-backs, indexes and embeddings, agent memory and evaluation sets - aligned to the records schedule, with owners and deletion that reaches every copy. Use when no rule covers AI interactions, when records or legal teams ask how long AI data is kept, or before switching on transcripts or memory at scale.
data-governance:data-classification-for-aidraftDefines how the organisation's classification scheme and sensitivity labels govern AI - which classes may ground AI, be typed into prompts, reach which models or external services, or train and tune models - plus how labels carry into AI outputs and how unlabelled data is treated. Use when there is no rule for which data AI may use, when labels exist but ignore AI, or before broad Copilot or agent rollout.
data-governance:data-product-readinessdraftAssesses whether a data product - a curated, owned dataset, table set or semantic model - is ready for AI to consume, checking ownership, descriptions and business meaning, quality, classification, access, lineage, refresh and change commitments, and records a ready, ready-with-conditions or not-ready verdict. Use when an agent, Copilot analysis or Foundry solution will read structured business data, or when a data team must make data AI-ready.
Evaluation and Quality Reviewer-class first version, draft
Reviewer-class department that defines evaluation sets, acceptance criteria, failure analysis and release evidence for probabilistic systems.
evaluation-quality:ai-evaluation-strategydraftSets and reviews the evaluation strategy for a probabilistic AI solution - which quality dimensions matter, which methods and datasets measure them, thresholds agreed before results and when evaluation repeats - classifying gaps as blocking or advisory. Use when a tier 2 or higher agent, workflow or Foundry solution enters design, or when nobody can say how the team will know it is good enough.
evaluation-quality:ai-quality-rubricdraftWrites and reviews the quality rubric used to score AI outputs - dimensions with plain definitions, anchored scoring levels, zero-tolerance failures, pass thresholds agreed with the business owner before results, and calibration of human and AI-assisted graders - with findings classed blocking or advisory. Use when evaluators disagree on scores, when thresholds are being set, or when a rubric is submitted to the gate.
evaluation-quality:ai-release-evidencedraftAssembles and reviews the evaluation evidence pack an AI solution needs at its release gate - plan, dataset and rubric versions, thresholds dated before results, results per dimension, groundedness, safety and red-team outcomes, known limits and monitoring baseline - and gives the evaluation-quality verdict with findings classed blocking or advisory. Use when a tier 2 or higher solution or material change seeks release.
evaluation-quality:ai-safety-testingdraftTests and reviews whether an AI solution avoids harmful outputs for its audience - hateful, violent, sexual or self-harm content, dangerous or regulated advice, demeaning statements about people, and poor handling of users in distress - checking platform content controls as configured, classing findings blocking or advisory. Use when a solution reaches the public or a broad workforce, or after harmful output is reported.
evaluation-quality:golden-datasetdraftBuilds, maintains and reviews the golden dataset an AI solution is judged against - real questions or tasks from users, expected answers and sources confirmed by knowledge owners, edge, refusal and adversarial cases, versioning and safe handling of test data - with findings classed blocking or advisory. Use when evaluation needs a trusted reference set, when expected answers have drifted, or when a test set looks too friendly.
evaluation-quality:groundedness-testingdraftTests and reviews whether an AI solution's answers are supported by the permitted sources it cites - claim-level support, citation accuracy, behaviour when knowledge is silent, stale or contradictory, and blending with model or web knowledge - classing findings blocking or advisory. Use when a knowledge-grounded agent or retrieval solution approaches release, after knowledge changes, or when users report invented or wrongly cited answers.
evaluation-quality:red-team-plandraftPlans and reviews a red-team exercise for an AI solution - objectives, scope, harm and misuse scenarios, a team with varied perspectives, rules of engagement, safe test environments, evidence recording, tester welfare and follow-through to fixes and re-tests - classing plan gaps and findings blocking or advisory. Use when a tier 3 or 4 solution nears release, when it gains new actions or audiences, or after a serious incident.
evaluation-quality:regression-evaluationdraftDecides and reviews what must be re-evaluated after a change to a live AI solution - instructions, knowledge, actions, model or platform updates - selecting the regression subset, comparing with the last baseline while allowing for normal output variation, and recommending release, hold or rollback, with findings classed blocking or advisory. Use when a change is ready to deploy or Microsoft announces a model or platform change.
Platform Governance first version, draft
Govern environments, makers, connectors, agents, deployment, licensing and portfolio visibility.
platform-governance:ai-admin-monitoringdraftSets up administrative monitoring of the AI estate - new and widely shared agents, policy and exception drift, orphaned solutions, unusual connector use, consumption and capacity trends - reported at estate, zone and business unit level, each signal with an owner. Use when governance relies on finding problems by accident, when the estate grows faster than reviews, or when steering asks what is happening across the platform.
platform-governance:ai-environment-strategydraftSets the environment strategy for AI building as governance zones - personal productivity, team collaboration and managed production - deciding who builds in each zone, its controls and sharing limits, and how a solution moves up a zone. Use when there is no organisation-wide rule for where AI building happens, when experiments become business-critical without controls, or when a team solution needs a governed home.
platform-governance:ai-licensing-and-capacitydraftFrames the licensing and capacity questions for Microsoft AI - who needs which kind of access, consumption-based usage, Dataverse and platform capacity, growth and cost owners - and gathers demand evidence for the Microsoft licensing guide or a licensing partner, never concluding itself. Use when a rollout or agent build raises licence or capacity questions, when consumption costs surprise budget holders, or before a renewal.
platform-governance:ai-release-policydraftSets the organisation's release policy for agents, flows and AI solutions by risk tier - the evidence, reviewer sign-offs, approver, permitted audience and channels, and post-release review each tier requires - plus rules for widening an audience, emergency changes and exceptions. Use when there is no consistent rule for publishing agents, when release takes the same effort at every tier, or when solutions reach wide audiences without review.
platform-governance:ai-solution-inventorydraftBuilds and keeps current an inventory of agents, flows, Cowork plugins and custom AI solutions across Microsoft 365, Power Platform and Azure - discovered from admin sources, reconciled with the agent portfolio register, with owners, zone, risk tier, sharing reach and review dates, and orphans flagged. Use when nobody can say how many agents exist, when an audit asks for a list, or when orphaned or unregistered solutions need finding.
platform-governance:connector-policydraftWrites the organisation-wide data and connector policies for AI building in each zone - business, non-business and blocked connector groups, custom connectors, HTTP and MCP access, web and public knowledge, endpoint filtering - with an exceptions process that has owners and review dates. Use when data policies predate agents, when makers keep requesting blocked connectors, or when AI features such as web grounding or MCP need a policy position.
platform-governance:maker-governancedraftGoverns who can build and share agents and flows - maker enablement, onboarding commitments, sharing rights by zone and audience, ownership on creation, handover when makers move on, and the limits that stop personal builds being shared organisation-wide - without ranking makers. Use when agent building is opened to more people, when agents are shared too widely, or when makers leave and their solutions become orphans.
AI Operations first version, draft
Operate deployed agents and AI solutions throughout support, monitoring, review, improvement and retirement.
ai-operations:ai-continuitydraftPlans business continuity for work that depends on AI - impact of unavailability or forced switch-off, fallback to manual or simpler routes, decision rights for suspending a solution, user communications, preserving the skills and procedures people need without AI, and exit routes when a model or feature is withdrawn. Use when a solution becomes business-critical, before tier 3 release, or when a model or feature deprecation is announced.
ai-operations:ai-feedback-loopdraftRuns the feedback loop for live agents and AI solutions - collecting in-product ratings, comments, support tickets and Champion reports, triaging them by cause to knowledge, instruction, scope or training owners, acting on themes, and telling users what changed. Use when a solution goes live, when feedback piles up unread, or when users say nothing happens after they report a problem.
ai-operations:ai-incident-managementdraftRuns incident management for AI failures and harms in live solutions - recognising AI incident types, setting severity, containing fast, preserving evidence, communicating, involving security, privacy and legal roles, and post-incident review before return to service. Use when an agent gives harmful, exposing or widely wrong outputs, takes a wrong action, or when AI incident procedures are being prepared.
ai-operations:ai-review-calendardraftMaintains a calendar of scheduled reviews for every live agent and AI solution, with frequency set by risk tier, a standard agenda covering ownership, quality, usage, feedback, incidents, knowledge, risk tier and cost, and a recorded outcome to continue, improve, re-tier, escalate or retire. Use when live solutions have no review dates, when reviews keep slipping, or when nobody can say which solutions are still needed.
ai-operations:ai-solution-monitoringdraftSets up operational monitoring for each live agent or AI solution - availability and errors, failed actions, usage by team, sampled answer quality, safety triggers, feedback, knowledge freshness and consumption - with thresholds, alert routing to named roles and a review rhythm. Use when a solution goes live, when users find failures before the owners do, or when alerts are so noisy that everyone ignores them.
ai-operations:ai-solution-retirementdraftRetires a live agent or AI solution safely once the decision is made - notifying users and pointing them to alternatives, removing sharing, disabling the solution, revoking connections, service identities and secrets, handling transcripts and logs under retention rules, cleaning up dependent flows and indexes, and closing the inventory record. Use when a review or owner decides to retire a solution, or when ownerless solutions accumulate.
ai-operations:ai-support-modeldraftDesigns the support model for live agents and AI solutions - support tiers from service desk to solution owner, knowledge owners, makers and platform team, AI-specific triage categories, response expectations by risk tier, runbooks and the handover from build to run - so every live solution has someone who answers and someone who fixes. Use when a solution is about to go live, or when issues bounce between teams with no owner.
ai-operations:knowledge-refreshdraftKeeps a live agent's knowledge current in operation - detecting source changes, moves and expiry, applying routine and urgent refreshes, confirming the solution picks up the change, retiring superseded content and re-running affected evaluation items. Use when a policy or document an agent relies on changes, when answers lag behind updated content, or when a knowledge library is moved or restructured under a live agent.
Design Authority Reviewer-class first version, draft
Reviewer-class department that challenges inappropriate platform choice, unnecessary AI, fragile architecture and unsupportable designs.
design-authority:ai-change-impact-reviewdraftReviews the impact of a proposed change to a live AI solution - instructions, knowledge, model, actions, identity, audience, channel or platform - classifying the change as minor, material or re-design, naming the re-tests and re-reviews triggered, and recording findings as blocking or advisory. Use when a live agent or solution is about to change, when Microsoft retires a model or feature it depends on, or when audience is widening.
design-authority:ai-design-reviewdraftRuns the formal design authority gate for an AI solution - checking the design pack is complete, applying the necessity, platform fit, conformance and supportability lenses proportionately, and recording every finding as blocking or advisory with a fix and owner and a single verdict. Use when a tier 2 or higher solution finishes design and before build commits, when a partner's design needs independent review, or when a design changed materially.
design-authority:ai-necessity-challengedraftChallenges, as a reviewer, whether a designed solution needs AI at all or needs as much AI as proposed, testing it against process change, automation and simpler Copilot patterns, and classifies each finding as blocking or advisory with the simpler alternative named. Use when a design arrives at the gate with an agent or custom model, when the suitability record looks written after the fact, or when AI adds cost or risk without clear benefit.
design-authority:architecture-conformancedraftReviews, as a reviewer, whether an AI solution's design conforms to the organisation's reference architectures, mandatory tier controls and architecture standards, records approved deviations with owners and expiry, and classifies each finding as blocking or advisory with the fix. Use when a design claims to follow a reference pattern, when a solution deviates from standards, or when deviations across solutions suggest a reference needs changing.
design-authority:platform-fit-reviewdraftReviews, as a reviewer, whether the Microsoft platform chosen for an AI solution fits its audience, identity, data, actions, lifecycle, operability and cost, and whether the decision record is reasoned and current, classifying each finding as blocking or advisory with the fix. Use when a design reaches the gate with a platform already chosen, when a partner proposes a platform, or when a live solution shows signs of outgrowing its platform.
design-authority:supportability-reviewdraftReviews whether an AI solution can be owned and supported after release - accepted business, knowledge and technical owners, an operating team with the skills and hours, support route, runbook, monitoring, review dates, dependencies and retirement criteria - classifying each finding as blocking or advisory with the fix. Use when a solution approaches its design or release gate, when a pilot becomes a service, or when owners are lost.
AI Centre of Excellence first version, draft
Orchestrate a business-owned AI Centre of Excellence covering charter, operating model, team, governance, opportunity flow, knowledge, cadence, RACI and value measurement.
ai-centre-of-excellence:coe-cadencedraftSets the meeting cadence and terms of reference for an AI CoE - a weekly working group, monthly steering where the sponsor decides, a quarterly strategy review and a community forum - with agendas, a decision log and sizing by edition. Use when a new CoE needs its forums set up, when meetings have become status updates that decide nothing, or when decisions are being taken in corridors and nobody can find them later.
ai-centre-of-excellence:coe-champion-integrationdraftConnects the Champions network to the AI Centre of Excellence - assigning the knowledge, process, adoption and AI Champion roles to CoE pillars, defining what each contributes to intake, knowledge upkeep, testing and stories, what they never approve, and how work and feedback flow both ways. Use when a CoE and a Champions programme run side by side without links, or when Champions have become the CoE's unpaid build team.
ai-centre-of-excellence:coe-charterdraftDrafts the charter for an AI Centre of Excellence - purpose, who owns what between the business, AI enablement and the sponsor, scope across the six pillars, decision rights, launch commitments and what the CoE is not - or the lighter terms of an AI working group. Use when leadership asks for an AI CoE, when a group has drifted into a reporting forum or request queue, or when a sponsor needs one page to agree before people are appointed.
ai-centre-of-excellence:coe-delivery-modeldraftDefines how the CoE actually builds and releases agents and automations once work is approved - delivery routes from self-service makers to CoE-assisted, CoE-built, partner and pro-code teams, a definition of ready and of done by risk tier, maker support, reusable patterns and handover to operations. Use when approved work has no clear way to get built, when makers and partners work differently, or when solutions go live unsupported.
ai-centre-of-excellence:coe-governance-modeldraftWrites a proportionate AI governance framework and acceptable-use starter - five governance areas, progressive risk tiers 0 to 4 with minimum controls, an agent portfolio register with business, knowledge and technical owners, and reviewer gates - so low-risk experimentation is not blocked. Use when there is no approved AI policy, when departments build agents with no register or owner, or when governance is so heavy people work around it.
ai-centre-of-excellence:coe-knowledge-pillardraftRuns the CoE's knowledge management pillar - a pillar lead, the layered knowledge asset register, owners and review dates for the content agents rely on, a knowledge check in intake, knowledge champions, and a loop that turns agent failures into content fixes - applying the knowledge-management standards. Use when the pillar exists on paper only, when agents give outdated answers, or when nobody owns the content solutions ground on.
ai-centre-of-excellence:coe-kpi-frameworkdraftBuilds a KPI framework and scorecard for an AI CoE across adoption, capability, portfolio flow, quality, governance and value, each measure with a baseline, target, source and owner, rolled up into a four-quadrant sponsor dashboard at team level. Use when a sponsor asks how they will know AI is working, when reporting relies on licence counts or hours saved, or when steering needs a scorecard before its first value review.
ai-centre-of-excellence:coe-launch-roadmapdraftTurns an agreed CoE charter and operating model into a dated launch plan across the Establish, Foundation, Deliver and Scale phases - workstreams, milestones, exit criteria, quick-win selection, first-ninety-day actions, dependencies, launch communications and go or hold decisions at each phase boundary. Use when the CoE is approved and needs a plan to start, or when a launch has drifted with no clear next milestone.
ai-centre-of-excellence:coe-maturity-assessmentdraftAssesses how mature a running AI Centre of Excellence is across sponsorship, team, opportunity pipeline, knowledge, delivery, governance, adoption and Champions, portfolio and value, using evidence from its own records, and sets the three to five next moves, including whether it is ready for spokes or federation. Use when a CoE has run for six months or more, at its annual review, or when the sponsor asks whether it is working.
ai-centre-of-excellence:coe-operating-modeldraftDesigns how an AI Centre of Excellence runs - a business-owned structure with sponsor, lead, core team, four Champion roles and central AI enablement support, its shape by edition, the collaboration space, the intake-to-delivery flow and a phased launch. Use when a charter is agreed and the CoE now needs people, routes and a workable flow, or when an existing CoE has become a bottleneck.
ai-centre-of-excellence:coe-opportunity-pipelinedraftRuns the CoE's opportunity management pillar as a working pipeline - a pillar owner, stages from Identify to Improve with entry and exit criteria, the opportunity-management method used at each stage, triage rhythm, work-in-progress limits, service levels and pipeline health measures. Use when ideas pile up without decisions, when nobody owns the pillar, or when pilots start without passing through assessment.
ai-centre-of-excellence:coe-portfolio-managementdraftManages the CoE's whole portfolio of live and planned agents, automations and AI solutions as one view - lifecycle state, owners, tier, value against hypothesis, run cost and review dates - finding duplicates to consolidate, patterns to reuse and solutions to retire, through a quarterly portfolio review. Use when nobody can say what is live or who owns it, when similar agents multiply, or when steering wants to know what the estate delivers.
ai-centre-of-excellence:coe-racidraftBuilds a role-based RACI for AI work across the opportunity lifecycle, governance decisions, knowledge ownership, release and value, with exactly one Accountable per activity and the business accountable for outcomes. Use when nobody can say who approves an agent, when IT is being held to account for business benefits, or when a new CoE needs clear responsibilities before its first steering meeting.
ai-centre-of-excellence:coe-readinessdraftChecks whether an organisation is ready to launch an AI Centre of Excellence - a sponsor who will decide, an owning business unit, a lead with real time, core team capacity, starting demand, knowledge owners, approved-use guidance and enablement support - and returns a launch, launch-with-conditions or not-yet verdict. Use when leadership wants a CoE before anyone has checked the preconditions, or when an earlier CoE attempt stalled.
ai-centre-of-excellence:coe-sponsor-roledraftDefines the executive sponsor's role in an AI Centre of Excellence - the decisions only the sponsor takes, time and attendance, a delegate with real authority, visible support for adoption, and what happens when sponsorship fades - and secures the sponsor's written commitment. Use when a CoE needs a sponsor, when the sponsor only receives reports, or when steering keeps deferring decisions upwards.
ai-centre-of-excellence:coe-team-designdraftDesigns the people side of an AI Centre of Excellence - the lead, pillar leads and core team roles, specialists borrowed from security, data protection, architecture and partners, the skills each role needs, agreed time, sourcing and backfill - and writes the team's terms of reference. Use when the CoE shape is chosen and roles need filling, when the core team is all enthusiasts and no specialists, or when members have no agreed time.
ai-centre-of-excellence:coe-value-trackingdraftRuns the CoE's routine for tracking value delivered - a value ledger with one line per solution showing estimate at intake, baseline, measured result, method and confidence, a monthly update rhythm, roll-up to the sponsor scorecard and the value review, and honest treatment of attribution and unproven claims. Use when steering asks what the CoE has delivered, when value claims vary by teller, or when nobody revisits estimates after release.
Organisation Tailoring first version, draft
Turn an organisation profile into a recommended edition and maturity archetype, a scaled programme, an ownership map and a transformation pack.
organisation-tailoring:ai-profile-intakedraftCreates or validates an organisation profile - size, business units, regulatory context, Microsoft estate, current AI use, knowledge, governance, adoption and delivery capability - by short interview or from a file, capturing only what tailoring needs and marking unknowns. Use when someone asks for help with an AI programme and no profile exists, when a profile file is supplied, or when circumstances have changed since the last pack.
organisation-tailoring:ai-role-cataloguedraftMaintains the catalogue of AI transformation roles - department lead roles plus sponsor, business, knowledge, solution and reviewer roles - each with purpose, responsibilities, decisions, capabilities, separation rules and typical time, and maps them to the job roles the organisation has declared. Use when an operating model names roles nobody recognises, when job titles must be matched to AI roles, or when the catalogue needs updating.
organisation-tailoring:capability-gap-analysisdraftCompares what the recommended AI programme demands - capabilities and time by role and quarter - with what the organisation has declared, classifies each shortfall as capability, capacity or vacancy, and chooses how to close it (develop, recruit, partner, borrow, re-sequence or defer) with the effect on the programme. Use when a pack or roadmap looks unstaffable, when capacity is the stated constraint, or before committing to delivery dates.
organisation-tailoring:edition-and-archetypedraftApplies the CopilotForge tailoring rules to a validated organisation profile to recommend an edition (starter, scale or enterprise), an assurance overlay and a maturity archetype, citing the rule id behind each, reporting emerging signals and explaining the result to the sponsor before any pack is built. Use when a profile is ready, when a sponsor asks why they got a lighter or heavier programme, or when circumstances change.
organisation-tailoring:operating-model-tailoringdraftRight-sizes the AI target operating model for one organisation from its confirmed edition, overlay, archetype, signals and declared constraints - which structures to adopt now, which roles to combine, which forums to merge, what a partner covers, what to defer and what would trigger a change - each decision traced to a rule or constraint. Use when a generic operating model looks too heavy or too light, or when constraints change.
organisation-tailoring:ownership-mapdraftProduces a role-based ownership map for an AI programme from declared roles only - the accountable role for each engaged department and each pack artefact, named owners only where the organisation has declared them, and roles left without an owner. Use when a transformation pack needs owners, when a sponsor asks who will do this work, or when nobody can say who owns adoption, governance or agents.
organisation-tailoring:transformation-pack-refreshdraftRefreshes an existing transformation pack when the organisation's profile, signals or the CopilotForge library change - compares old and new profiles and tailoring results, classifies each change as material or minor, maps it to affected artefacts, protects decisions already taken, and agrees a change summary before any document is replaced. Use when a pack is due for review, after a merger or new regulation, or when agents or shadow AI appear.
organisation-tailoring:transformation-packdraftOrchestrates a tailored AI transformation pack from a confirmed profile - assessment, target operating model, adoption and Champions programmes, CoE, opportunity process, agent delivery, governance, roadmap, ownership map, templates and success measures - at the depth the edition sets, each artefact naming its responsible skill and sources. Use when an organisation asks for help adopting Copilot, scaling agents or setting up an AI CoE.