{ "schema": "yarn-pack/2", "id": "ai-maturity", "version": "2.0.0", "name": "AI Maturity (Agile Yarn)", "engagement": "AISG — AI maturity pre-read / front-door", "intro": "A short, guided conversation — not a form. Answer in your own words; attach a document if it helps. Takes 15–25 minutes.", "tone_default": "professional", "prefill_fields": { "department": [ "Executive", "Finance", "Operations", "Customer / Sales", "Technology / IT", "Data & Analytics", "People & Culture", "Risk & Compliance", "Marketing", "Product" ] }, "scales": [ { "id": "maturity5", "name": "Maturity (distilled model)", "levels": [ { "value": 1, "label": "Does not exist", "gloss": "No capability. Absent, or purely ad hoc / accidental." }, { "value": 2, "label": "Partially exists", "gloss": "Emerging and inconsistent. Pockets of activity, not joined up." }, { "value": 3, "label": "Fully exists", "gloss": "Defined, documented and operating across the organisation." }, { "value": 4, "label": "Fully exists & optimised", "gloss": "Measured, refined and improving against targets." }, { "value": 5, "label": "Fully exists & adaptive", "gloss": "Continuously self-adjusting; a source of advantage." } ], "signals": { "1": [ "no ", "not ", "none", "never", "don't", "do not", "nothing", "absent", "unaware", "haven't", "ad hoc", "ad-hoc", "nonexistent", "no idea", "not really" ], "2": [ "some ", "starting", "beginning", "emerging", "pilot", "trial", "informal", "inconsistent", "pockets", "a bit", "occasionally", "early", "experiment", "trying", "patchy" ], "3": [ "documented", "defined", "standard", "standardised", "established", "policy", "framework", "process", "consistent", "across the", "in place", "formal", "governed", "rolled out" ], "4": [ "measured", "metrics", "optimis", "improving", "kpi", "monitored", "reviewed", "refined", "benchmarked", "targets", "tracked", "mature", "regularly review" ], "5": [ "continuous", "adaptive", "self-", "automated end", "best in class", "best-in-class", "competitive advantage", "industry leading", "always", "real-time monitoring", "feedback loop" ] } } ], "categories": [ { "id": "strategy", "name": "AI Strategy", "order": 1, "target_default": 3 }, { "id": "value", "name": "AI Value", "order": 2, "target_default": 3 }, { "id": "org", "name": "AI Organisation", "order": 3, "target_default": 3 }, { "id": "people", "name": "AI People & Culture", "order": 4, "target_default": 3 }, { "id": "gov", "name": "AI Governance", "order": 5, "target_default": 3 }, { "id": "eng", "name": "AI Engineering", "order": 6, "target_default": 3 }, { "id": "data", "name": "AI Data", "order": 7, "target_default": 3 } ], "audiences": [ { "id": "exec", "name": "Executive / Strategy", "desc": "Leadership, strategy, investment", "deep_dive_sections": [ "strategy_dd" ] }, { "id": "data", "name": "Data / Engineering", "desc": "Builds & runs data and AI systems", "deep_dive_sections": [ "data_dd", "eng_dd" ] }, { "id": "gov", "name": "Risk / Governance / Legal", "desc": "Risk, compliance, responsible AI", "deep_dive_sections": [ "gov_dd" ] }, { "id": "people", "name": "HR / Change / People", "desc": "Workforce, skills, adoption", "deep_dive_sections": [ "people_dd" ] }, { "id": "biz", "name": "Business / Product", "desc": "Operates a business unit or product", "deep_dive_sections": [] } ], "sections": [ { "id": "core", "title": "Core capabilities", "blurb": "Everyone answers these.", "optional": false, "questions": [ { "id": "vision", "type": "scored_text", "category": "strategy", "name": "AI vision & ambition", "text": "How clear is your organisation's vision and ambition for AI? Is there a documented AI strategy, and who owns it?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "roadmap", "type": "scored_text", "category": "strategy", "name": "AI roadmap & investment", "text": "Is there a roadmap for AI with short- and long-term priorities? How are AI investments decided and sequenced?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "prioritise", "type": "scored_text", "category": "value", "name": "Use-case prioritisation", "text": "How do AI use cases get identified, evaluated and prioritised? Walk me through how a new idea becomes a funded project.", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "businesscase", "type": "scored_text", "category": "value", "name": "Business cases & value tracking", "text": "How do you build the business case for an AI initiative, and how is value actually measured after it ships?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "operatingmodel", "type": "scored_text", "category": "org", "name": "AI operating model", "text": "How is AI organised — central team, embedded, federated? Is there a defined operating model, or is it improvised per project?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "culture", "type": "scored_text", "category": "people", "name": "AI culture & change", "text": "How would you describe the organisation's appetite for AI? How is adoption and change managed?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "skills", "type": "scored_text", "category": "people", "name": "AI skills & gaps", "text": "How well do you understand the AI skills you have versus what you need? Is there a plan to close the gaps?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "training", "type": "scored_text", "category": "people", "name": "AI training & literacy", "text": "What AI training, upskilling or literacy programs exist, and who actually has access to them?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "ethics", "type": "scored_text", "category": "gov", "name": "Responsible AI policy", "text": "What policies or principles govern responsible and ethical use of AI? Are they actually enforced, or shelfware?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "risk", "type": "scored_text", "category": "gov", "name": "AI risk framework", "text": "How are AI-specific risks identified and managed? Is there a risk framework or register for AI?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "compliance", "type": "scored_text", "category": "gov", "name": "Compliance monitoring", "text": "How do you monitor and enforce compliance with AI policies and legal/regulatory obligations?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "architecture", "type": "scored_text", "category": "eng", "name": "AI architecture & platform", "text": "Describe the platforms and infrastructure you use to build and run AI. How standardised is it across teams?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "deploy", "type": "scored_text", "category": "eng", "name": "Deploy & operate AI", "text": "Once an AI solution is built, how does it get deployed, monitored and supported in production?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "dataacquire", "type": "scored_text", "category": "data", "name": "Acquire & prepare data", "text": "How easily can teams get the data they need for AI? Walk me through sourcing and preparing it.", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "dataquality", "type": "scored_text", "category": "data", "name": "Data quality & integrity", "text": "How confident are you in the quality, completeness and consistency of data used for AI?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "datagov", "type": "scored_text", "category": "data", "name": "Data governance for AI", "text": "What governance exists over data used in AI — ownership, access controls, lineage?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "datasec", "type": "scored_text", "category": "data", "name": "Security & privacy for AI data", "text": "How is sensitive data protected when used in AI? Any privacy controls specific to AI?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false } ] }, { "id": "strategy_dd", "title": "Strategy deep-dive", "blurb": "Extra depth for the areas you know best.", "audience": [ "exec" ], "optional": false, "questions": [ { "id": "trendscan", "type": "scored_text", "category": "strategy", "name": "Trend scanning", "text": "How do you monitor and interpret emerging AI trends and assess their impact on your business?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "readiness", "type": "scored_text", "category": "strategy", "name": "Readiness measurement", "text": "Do you formally assess organisational readiness for AI? How?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "innovation", "type": "scored_text", "category": "value", "name": "Innovation & experimentation", "text": "How are new AI ideas and experiments fostered, captured and scaled?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "ecosystem", "type": "scored_text", "category": "org", "name": "Ecosystem & partnerships", "text": "How do you use external partners, vendors and alliances in your AI strategy?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false } ] }, { "id": "data_dd", "title": "Data deep-dive", "blurb": "Extra depth for the areas you know best.", "audience": [ "data" ], "optional": false, "questions": [ { "id": "dataprep", "type": "scored_text", "category": "data", "name": "Data prep & feature engineering", "text": "How mature are your data preparation and feature-engineering practices for AI?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "dataintegration", "type": "scored_text", "category": "data", "name": "Data integration", "text": "How well integrated are your data sources for AI use, internal and external?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "lineage", "type": "scored_text", "category": "data", "name": "Data lineage & traceability", "text": "Can you trace the origin and transformation of data feeding AI models?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "metadata", "type": "scored_text", "category": "data", "name": "Metadata & semantics", "text": "What metadata, cataloguing or semantic-layer capabilities support AI?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "observability", "type": "scored_text", "category": "data", "name": "Data observability", "text": "How do you monitor the health and behaviour of data over time?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "analytics", "type": "scored_text", "category": "data", "name": "Analytics & visualisation", "text": "How are analytics and visualisation used to support AI insight?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false } ] }, { "id": "eng_dd", "title": "Engineering deep-dive", "blurb": "Extra depth for the areas you know best.", "audience": [ "data" ], "optional": false, "questions": [ { "id": "buildvsbuy", "type": "scored_text", "category": "eng", "name": "Build vs buy", "text": "How do you decide build vs buy for AI capabilities? Is there a rule or is it case-by-case?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "buildapps", "type": "scored_text", "category": "eng", "name": "Building AI applications", "text": "Describe how AI applications and models actually get built and trained here.", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "testing", "type": "scored_text", "category": "eng", "name": "Testing AI systems", "text": "How are AI models and applications tested before and after release?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "techdebt", "type": "scored_text", "category": "eng", "name": "AI technical debt", "text": "How do you track and manage technical debt in AI systems?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "scale", "type": "scored_text", "category": "eng", "name": "Scaling AI systems", "text": "How do you scale AI systems that prove valuable?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "mlops", "type": "scored_text", "category": "eng", "name": "Operationalisation / MLOps", "text": "What MLOps practices keep AI running in line with service levels?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false } ] }, { "id": "gov_dd", "title": "Governance deep-dive", "blurb": "Extra depth for the areas you know best.", "audience": [ "gov" ], "optional": false, "questions": [ { "id": "security", "type": "scored_text", "category": "gov", "name": "AI security & safety", "text": "What frameworks address AI-specific security and safety threats?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "legal", "type": "scored_text", "category": "gov", "name": "Legal & regulatory", "text": "How do you track and respond to AI legal and regulatory requirements?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "sustainable", "type": "scored_text", "category": "gov", "name": "Sustainable / cost governance", "text": "Are sustainability and cost-of-AI considerations governed at all?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "govteams", "type": "scored_text", "category": "gov", "name": "Governance teams & decision rights", "text": "Who governs AI? Are there cross-functional teams and clear decision rights?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "oversight", "type": "scored_text", "category": "gov", "name": "Oversight & assurance", "text": "What systems provide continuous oversight and assurance of AI in production?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false } ] }, { "id": "people_dd", "title": "People deep-dive", "blurb": "Extra depth for the areas you know best.", "audience": [ "people" ], "optional": false, "questions": [ { "id": "workforce", "type": "scored_text", "category": "people", "name": "Workforce planning", "text": "How do you forecast and plan for AI's impact on roles and staffing?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "recruit", "type": "scored_text", "category": "people", "name": "AI talent acquisition", "text": "How do you attract and recruit AI talent?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "trainingeff", "type": "scored_text", "category": "people", "name": "Training effectiveness", "text": "How do you measure whether AI training actually changes anything?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false }, { "id": "changemgmt", "type": "scored_text", "category": "people", "name": "Change management", "text": "How are AI-driven change initiatives executed and monitored?", "scale": "maturity5", "scored": true, "star": false, "ai_drafted": false } ] } ], "grids": {}, "outputs": [ "Overall maturity picture + by-area", "Contested-capability view", "Coverage check", "Confidence ramp", "Executive summary + priority gaps", "Printable client report" ], "report_defaults": [ "rpt-maturity-standard" ], "playbook": { "sequence": [ "strategy", "gov", "data", "org", "people", "eng", "value" ], "sequence_note": "Foundations first: strategy and governance decisions shape everything downstream; data and operating model make delivery repeatable; engineering and value work then compound instead of stalling.", "actions": { "strategy": { "to_2": [ "Name an executive owner for AI and write down the ambition in one page: where AI matters to the business and where it does not.", "Inventory what is already happening with AI across the organisation, sanctioned or not." ], "to_3": [ "Publish an AI strategy tied to business goals, with a 12–24 month roadmap and a decision forum that meets.", "Agree the handful of priority domains and say no to the rest for now." ], "to_4": [ "Set measurable targets per priority domain and review progress against them each quarter.", "Fund the roadmap as a portfolio, not project by project." ], "to_5": [ "Refresh the strategy on a cadence driven by results and market shifts, with scenario planning for model and regulatory change." ] }, "value": { "to_2": [ "Keep one shared list of AI use cases with an owner and a one-line value hypothesis each.", "Stop funding ideas that bypass the list." ], "to_3": [ "Adopt a single intake and prioritisation method (value × feasibility × risk) and apply it to every candidate.", "Define what 'done' means for a pilot: a decision to scale, hold or stop, with the evidence required." ], "to_4": [ "Track realised value against the hypothesis for every scaled use case and report it alongside cost.", "Retire or rework use cases that are not paying back." ], "to_5": [ "Run the portfolio as a continuous funnel with stage gates, benchmarks and reinvestment of returns." ] }, "org": { "to_2": [ "Assign clear accountability for AI delivery, even if it sits in an existing team for now.", "Write down how an AI idea gets resourced today so the gaps are visible." ], "to_3": [ "Choose an operating model (central, hub-and-spoke or federated) and staff the core roles: product, data, engineering, risk.", "Define the interfaces with IT, data and the business units." ], "to_4": [ "Measure delivery throughput and cycle time and remove the recurring blockers.", "Formalise a community of practice that shares patterns and reusable components." ], "to_5": [ "Let the model flex by domain with shared platforms and standards, and review it against outcomes annually." ] }, "people": { "to_2": [ "Run a skills stocktake: who can do what today, and who wants to learn.", "Give leaders a shared, plain-language briefing on what AI can and cannot do." ], "to_3": [ "Put an AI literacy programme in place by role type, with a small number of deep specialists.", "Set expectations for how staff should and should not use AI tools, and support the change explicitly." ], "to_4": [ "Tie AI skills to career paths and performance goals; measure adoption and confidence, not just attendance.", "Build change management into every AI rollout by default." ], "to_5": [ "Sustain a learning culture where teams experiment safely and share what they learn across the organisation." ] }, "gov": { "to_2": [ "Adopt a short, enforceable responsible-AI policy covering acceptable use, data and human oversight.", "Create a register of AI systems in use and who owns each." ], "to_3": [ "Stand up an AI risk assessment that every use case passes before build and before scale, proportionate to impact.", "Assign a governance forum with the authority to stop a deployment." ], "to_4": [ "Monitor deployed systems for drift, bias and incidents, with defined response paths.", "Audit against the policy and the emerging regulatory obligations at least annually." ], "to_5": [ "Embed governance into the delivery platform so compliance is largely automatic, and adapt controls as regulation and models change." ] }, "eng": { "to_2": [ "Pick one sanctioned path to build and run an AI solution, even if minimal, and route new work through it.", "Get the current pilots under source control and basic monitoring." ], "to_3": [ "Standardise the platform: environments, model access, deployment pipeline, logging and cost controls.", "Define engineering standards for prompts, evaluation and testing of AI components." ], "to_4": [ "Instrument quality, latency, cost and usage for every production system and act on the trends.", "Reuse components (retrieval, guardrails, evaluation) across use cases rather than rebuilding." ], "to_5": [ "Run a self-service platform with automated evaluation and rollback, evolving as model capabilities shift." ] }, "data": { "to_2": [ "Identify the data the priority use cases depend on and who owns it.", "Fix the most damaging quality issues in that data first, not everywhere." ], "to_3": [ "Put data governance in place for AI workloads: ownership, quality rules, classification, access controls and lineage.", "Make the priority data discoverable and reliably available to delivery teams." ], "to_4": [ "Measure data quality and availability as a service level and report against it.", "Extend governance to unstructured content and model inputs and outputs." ], "to_5": [ "Treat data as a managed product with automated quality, privacy-preserving access and continuous fitness-for-AI checks." ] } } } }