# CX AI Advisory Diagnostic — universal config (industry-independent).
#
# This file is the diagnostic's *_VERBATIM anchor (Mercury Notebook Pattern):
# the competency model, level descriptors, and capping heuristic that every
# industry overlay extends. Editing wording here is editing the instrument —
# do it deliberately, and re-run tests + notebooks afterwards.

version: "1.0"

dimensions:
  - id: strategy_value
    name: "Strategy & Value"
  - id: foundations
    name: "Foundations"
  - id: delivery
    name: "Delivery"
  - id: sustain
    name: "Sustain"

competencies:
  # ── Strategy & Value ─────────────────────────────────────────────────
  - id: automation_ai_strategy
    dimension: strategy_value
    name: "Automation & AI Strategy"
    description: "Is there a written thesis for why AI, where, and what it changes about the operating model?"
    failure_vignette: "We're doing AI because the CEO read an article."
    level_descriptors:
      1: "No thesis. AI driven by executive impulse or vendor pitch."
      2: "Aspirational vision, no operating model implications defined."
      3: "Documented strategy, partial linkage to operating model."
      4: "Strategy drives portfolio decisions and operating model changes."
      5: "Strategy is reviewed quarterly; operating model evolves with capability."

  - id: value_realization
    dimension: strategy_value
    name: "Value Realization"
    description: "Are AI benefits defined before investment, measured after go-live, and actually harvested?"
    failure_vignette: "The chatbot saved us four million dollars — nobody can say where it went."
    level_descriptors:
      1: "No benefit definition. Success is anecdote and vendor slideware."
      2: "Business cases exist pre-investment; nobody measures after go-live."
      3: "Benefits tracked for flagship initiatives; harvesting is ad hoc."
      4: "Standard value framework; benefits measured and attributed per initiative."
      5: "Value realization steers the portfolio: funding follows measured returns."

  - id: executive_alignment
    dimension: strategy_value
    name: "Executive Alignment"
    description: "Do the executives who own budget, operations, and technology pull in the same direction on AI?"
    failure_vignette: "The CIO and the COO each run their own AI program — neither knows the other's roadmap."
    level_descriptors:
      1: "No accountable executive. AI initiatives appear wherever budget leaks."
      2: "One sponsor evangelizes; peer executives are indifferent or resistant."
      3: "Named executive owner; cross-functional steering exists on paper."
      4: "Steering meets and decides; budget and priorities move as one portfolio."
      5: "AI accountability sits in executive scorecards and compensation."

  # ── Foundations ──────────────────────────────────────────────────────
  - id: process_discovery
    dimension: foundations
    name: "Process Discovery"
    description: "Do you know, at task level, how customer-facing work actually flows today?"
    failure_vignette: "We automated the process as documented — it turns out nobody follows it."
    level_descriptors:
      1: "Processes undocumented; the knowledge lives in agents' heads."
      2: "High-level process maps exist — stale and aspirational."
      3: "Priority journeys mapped at task level with volumes and handle data."
      4: "Discovery is instrumented (mining, analytics); maps reflect observed work."
      5: "Continuous process intelligence feeds an automation pipeline."

  - id: data_readiness
    dimension: foundations
    name: "Data Readiness"
    description: "Is the data AI needs — knowledge, interactions, customer context — accessible, clean, and governed?"
    failure_vignette: "The bot's knowledge base is a SharePoint folder last updated two reorgs ago."
    level_descriptors:
      1: "Data siloed and unmanaged; no owner, no quality measures."
      2: "Key sources identified; access is manual and quality unknown."
      3: "Priority data consolidated and cleansed for first use cases; stewardship assigned."
      4: "Governed pipelines feed AI in production; quality is monitored."
      5: "Data products with SLAs; a new use case onboards in days, not quarters."

  - id: technical_architecture
    dimension: foundations
    name: "Technical Architecture"
    description: "Can your platform stack integrate, orchestrate, and scale AI services safely?"
    failure_vignette: "Every new bot needs a six-month integration project and its own credentials spreadsheet."
    level_descriptors:
      1: "Legacy estate; point-to-point integrations; no API layer."
      2: "Some APIs exist; each AI effort builds bespoke plumbing."
      3: "Reference architecture defined; shared integration layer for priority systems."
      4: "Platform approach: reusable services, identity, and observability across AI workloads."
      5: "Composable architecture; new AI capability ships on a paved road."

  # ── Delivery ─────────────────────────────────────────────────────────
  - id: use_case_prioritization
    dimension: delivery
    name: "Use Case Prioritization"
    description: "Is there a managed portfolio that chooses AI work by value and feasibility?"
    failure_vignette: "We have forty AI ideas on a whiteboard, and the loudest stakeholder goes first."
    level_descriptors:
      1: "No pipeline; initiatives start on executive impulse."
      2: "An idea list exists; no scoring, no sequencing."
      3: "Value and feasibility scoring; a prioritized backlog is reviewed."
      4: "Portfolio managed against capacity and dependencies; stop/pivot rules are applied."
      5: "The portfolio rebalances continuously on measured value and capability growth."

  - id: delivery_capability
    dimension: delivery
    name: "Delivery Capability"
    description: "Can you take an AI use case from concept to production, repeatably?"
    failure_vignette: "Every pilot succeeds; nothing ever reaches production."
    level_descriptors:
      1: "No delivery method for AI; experiments die in the lab."
      2: "Vendor-led one-off projects; nothing reusable remains."
      3: "A standard delivery path exists; a few use cases run in production."
      4: "Product teams ship AI iteratively; reusable components accelerate delivery."
      5: "Factory model: concept-to-production in weeks, with automated quality gates."

  - id: talent_and_skills
    dimension: delivery
    name: "Talent & Skills"
    description: "Are the skills AI delivery needs — conversation design, prompting, data, MLOps — in the right seats?"
    failure_vignette: "Our bot team is one hero contractor whose contract ends in March."
    level_descriptors:
      1: "No AI-relevant skills in-house; total vendor dependence."
      2: "Isolated enthusiasts self-teach; no roles or paths defined."
      3: "Core roles staffed for current initiatives; a training program has started."
      4: "Skills strategy: career paths, internal academy, knowledge-transfer clauses with vendors."
      5: "Talent is a differentiator: bench depth, low key-person risk, a magnet for hires."

  # ── Sustain ──────────────────────────────────────────────────────────
  - id: ai_operations
    dimension: sustain
    name: "AI Operations"
    description: "Once AI is live, who watches it, tunes it, and fixes it — with what telemetry?"
    failure_vignette: "Containment fell for three weeks before anyone noticed — a menu change had broken the intents."
    level_descriptors:
      1: "No monitoring; failures surface as customer complaints."
      2: "Manual spot checks; tuning happens when someone escalates."
      3: "Dashboards for containment and accuracy; scheduled tuning cycles."
      4: "Full observability: drift alerts, feedback loops, a named run team."
      5: "Self-optimizing operations; automated retraining inside governed guardrails."

  - id: change_adoption
    dimension: sustain
    name: "Change & Adoption"
    description: "Are agents, supervisors, and customers brought along — or does AI happen to them?"
    failure_vignette: "Agents learned about the copilot from the go-live email — they've been closing it ever since."
    level_descriptors:
      1: "No change effort; adoption is assumed."
      2: "Announcement-and-training-deck change; adoption unmeasured."
      3: "Structured change program for major rollouts; adoption tracked."
      4: "Co-design with the front line; champions network; adoption is a launch KPI."
      5: "Change muscle is institutional; the front line pulls the roadmap forward."

  - id: governance_and_risk
    dimension: sustain
    name: "Governance & Risk"
    description: "Are AI risk, compliance, and ethics governed — at the speed production AI moves?"
    failure_vignette: "Legal found out about the voice bot when a customer complaint reached the regulator."
    level_descriptors:
      1: "No AI governance; risk is handled after incidents."
      2: "Generic IT policies stretched over AI; approvals ad hoc and slow."
      3: "AI policy and a review board for high-risk use cases."
      4: "Risk-tiered governance embedded in delivery; audit trails standard."
      5: "Governance is an accelerator: pre-approved patterns, continuous compliance."

# ── Value capping heuristic ────────────────────────────────────────────
# Applied to the WEAKEST foundational competency score: the fraction of
# theoretical annual value an organization at that level can realistically
# capture. All bands are ranges — never point estimates.
capping_heuristic:
  1: {realized_low: 0.00, realized_high: 0.15}
  2: {realized_low: 0.25, realized_high: 0.40}
  3: {realized_low: 0.50, realized_high: 0.65}
  4: {realized_low: 0.65, realized_high: 0.85}
  5: {realized_low: 0.80, realized_high: 1.00}

# Which competencies act as "foundational" — their weakness caps everything.
foundational_competencies:
  - process_discovery
  - data_readiness
  - technical_architecture