Bloom-aligned objectives

• Understand: what qualifies as a “GenAI investable use case” vs a productivity tip
• Analyze: where GenAI creates value across workflows and decision loops
• Apply: a consistent framing method that produces comparable use-case entries

Topics

• Generative AI for Enterprises: recognizing opportunities, aligning to goals, optimizing processes
• Use-case classes (cloud/tool agnostic)
  • content acceleration (drafting, summarization, synthesis)
  • knowledge navigation (policy/product/process Q&A with grounding)
  • workflow coordination (intake, routing, follow-up, exception handling)
  • decision support (structured options, scenario framing with verification)
• The “investable use case” definition
  • target user, workflow step, measurable outcome, control boundary, success metric, owner

Activity (45 min): “Portfolio inventory and de-duplication”

Participants list 15–25 ideas and cluster into themes; remove duplicates; convert ideas into “workflow problems.”

Micro-lab 1 (60 min): “Use-case one-pager v1”

Create 3 use-case one-pagers (minimum) with:

• problem statement, user personas, workflow scope
• value hypothesis (time saved, quality uplift, risk reduction)
• acceptance criteria and verification needs

Bloom-aligned objectives

• Apply: a discovery funnel to source high-impact opportunities
• Analyze: feasibility and readiness signals early (data/process/change)
• Create: a standardized use-case canvas that supports prioritization

Topics

• Discovery sources and techniques (agnostic)
  • pain-point mining: cycle time, rework, escalations, backlog drivers
  • “cost of delay” framing
  • frontline-to-leadership translation
• Qualification dimensions (leader-friendly)
  • feasibility: process stability, data availability, integration needs
  • readiness: change impact, training needs, control needs
• Structured evaluation patterns (Microsoft Learn includes structured processes for researching and prioritizing AI agent use cases; adapted here tool-agnostically)

Lab 2A (75 min): “Use-case canvas v2 (evidence-based)”

Upgrade 3 canvases using provided evidence prompts:

• what evidence supports frequency and pain?
• what evidence supports value?
• what verification is mandatory?

Lab 2B (45 min): “Use-case risk tiering (early)”

Assign each use case a risk tier (low/medium/high) based on:

• sensitivity of data
• external impact
• decision criticality

regulatory exposure
(sets up Day 2 stage gates using NIST AI RMF concepts)

Bloom-aligned objectives

• Apply: scoring models to rank initiatives transparently
• Analyze: portfolio balance (quick wins vs strategic bets; risk vs value)
• Create: pilot waves and a sequencing roadmap with dependencies

Topics

• Portfolio scoring model (agnostic) aligned to transformation course patterns (identify opportunities, align investments) Microsoft Learn+1
  • value score: time saved, quality uplift, revenue/risk impact
  • feasibility score: readiness, complexity, dependencies
  • risk score: tier + control burden
  • confidence score: evidence strength
• Balancing the portfolio
  • wave 1: low-risk, high-confidence quick wins
  • wave 2: cross-functional workflows
  • wave 3: decision-critical or externally facing (highest control)
• Decision checkpoints and sponsorship model (who owns value; who owns risk)

Lab 3A (75 min): “Portfolio scorecard and ranking”

Score 10–12 use cases, produce:

• ranked list
• top 3 pilot candidates
• “park/kill” list with reasons

Lab 3B (30 min): “Roadmap and dependency map”

Create a 2-wave roadmap showing:

• prerequisites (policy, data, training)
• cross-team dependencies
• expected value delivery timeline

Bloom-aligned objectives

• Understand: value drivers and how to quantify them credibly
• Apply: a business case template that separates hypothesis vs measured results
• Create: a one-page executive business case for top pilots

Topics

• Business case components (agnostic)
  • baseline: time/cycle/rework/error cost today
  • benefit model: productivity, quality, risk reduction
  • cost model: enablement, governance, operations, change management
• Adoption economics
  • leading indicators: active usage, reuse of templates, cycle-time reduction
  • lagging indicators: measurable savings, SLA improvement, reduced rework
• Aligning AI initiatives to measurable business value (leader learning paths emphasize aligning AI with goals and maximizing impact; adapted here tool-agnostically)

Lab 4A (90 min): “Business case one-pager (top 2 pilots)”

Produce for each pilot:

• value hypothesis + KPI targets
• cost estimate ranges (low/likely/high)
• risk tier + required controls
• sponsor, owner, rollout approach

Lab 4B (30 min): “Value narrative and decision ask”

Create an executive narrative:

• why now, why this, why us
• what decision is needed (funding, policy, data access, change support)

Bloom-aligned objectives

• Apply: experiment-style pilot planning with measurable success criteria
• Analyze: what must be true to scale (controls, reliability, adoption)
• Create: a pilot charter with stage gates and evidence plan

Topics

• Pilot charter structure (agnostic)
  • scope, cohorts, workflows covered, exclusions
  • measurement plan (baseline, target, collection method)
  • human-in-the-loop design (draft vs send; approvals for sensitive outputs)
• Evidence plan
  • what to measure weekly
  • what constitutes “scale,” “iterate,” or “stop”
• Transformation leadership emphasis on practical strategy and responsible adoption

Lab 5A (75 min): “Pilot charter + measurement plan”

Create:

• pilot goals, KPIs, instrumentation approach
• adoption plan (enablement + prompt/library assets)

scale criteria and stop criteria

Bloom-aligned objectives

• Understand: governance structures suitable for AI portfolios
• Evaluate: initiatives through risk gates and assurance checkpoints
• Create: a governance cadence and a “stage gate” checklist for the organization

Topics

• NIST AI RMF functions (Govern, Map, Measure, Manage) translated into portfolio gates
• Generative AI risk considerations and profiles (NIST’s GenAI Profile referenced on NIST AI RMF resources)
• ISO/IEC 42001 management-system lens (policy, accountability, lifecycle governance) to make governance auditable and repeatable
• Portfolio operating cadence
  • intake → triage → pilot approval → go-live → scale
  • owners and escalation paths (business, risk, legal, IT)

Workshop (75 min): “Stage gates and governance operating cadence”

Create:

• a 5-gate model (idea, qualified, pilot, go-live, scale)
• gate checklists (value evidence, control readiness, adoption readiness)
• a quarterly portfolio review format (what leaders review and approve)

Final simulation (45 min): “Executive portfolio review”

Teams present a portfolio pack:

• ranked use-case list + rationale
• top 2 business cases
• pilot charters + KPIs
• stage gates and governance cadence