Bloom-aligned objectives

• Understand: how AI reshapes tasks, roles, and decision-making
• Analyze: which work patterns are most affected (knowledge work, coordination, decision loops)
• Evaluate: risks introduced by AI augmentation and why trust must be engineered

Topics

• AI augmentation vs automation vs autonomy: practical distinctions for leaders
• The “work system” impact model
  • tasks and workflows (cycle time, rework, handoffs)
  • roles and skills (new role expectations, judgment, verification)
  • decisions and controls (approval thresholds, auditability, traceability)
  • culture (trust, transparency, psychological safety)
• Trust dynamics in AI adoption
  • when people over-trust vs under-trust AI outputs
  • what builds confidence (verification norms, explainability practices, guardrails)
• Typical failure modes that break adoption
  • “shadow AI” usage
  • inconsistent outputs and quality drift
  • friction added to workflows (extra steps, unclear policies)
  • unclear accountability (who owns errors)

Activity (45 min): “Work impact heatmap”

Participants map 10 common workflows and mark:

• augmentation opportunities
• automation candidates
• high-risk or “human-only” areas
• likely resistance points

Micro-lab 1 (45 min): “Role impact snapshot”

For 2 roles, define:

• tasks that will change
• new skills/behaviors needed
• new risks to manage
• training and performance implications

Bloom-aligned objectives

• Apply: a structured change methodology to an AI program
• Analyze: stakeholder expectations and influence dynamics
• Create: an end-to-end change strategy aligned to business outcomes

Topics

• Change strategy components for AI programs
  • sponsorship model and decision cadence
  • scope definition: which workflows, which roles, which policies
  • adoption thesis: “what will be different” and “how we know it worked”
• Stakeholder segmentation
  • executive sponsors, managers, frontline users, risk/legal, IT, unions/works councils (where relevant)
  • change champions and super-user networks
• Communications strategy (AI-specific)
  • transparency: what AI is used for, what it is not used for
  • safe-use boundaries: what data cannot be used, what approvals are required
  • trust-building: demonstrate controls and quality standards
• Enablement strategy
  • capability building beyond prompts: workflow redesign, verification habits, decision protocols
  • role-based playbooks and templates

Lab 2A (75 min): “Stakeholder map + engagement plan”

Build:

• stakeholder map (influence vs impact)
• engagement plan with messages, channels, and cadence
• resistance risks and mitigation actions

Lab 2B (45 min): “Change strategy one-pager”

Create a one-page strategy:

• outcomes, scope, guiding principles
• adoption approach, governance cadence
• success measures and decision checkpoints

Bloom-aligned objectives

• Apply: learning design that drives behavior change
• Analyze: what should be embedded into workflows vs taught in training
• Create: a role-based enablement plan with reinforcement mechanisms

Topics

• Role-based capability design
  • baseline AI literacy
  • role-specific workflow skills (drafting, synthesis, analysis, coordination)
  • verification and compliance habits
• Learning architecture
  • onboarding + in-role learning + performance support (job aids)
  • community of practice, office hours, prompt libraries
• Workflow embedding patterns
  • templates inside tools
  • “definition of done” checklists
  • review and approval paths (draft vs send)
• Reinforcement mechanisms
  • manager enablement (coaching scripts, review routines)
  • incentives and recognition
  • policy alignment and consequences for misuse

Lab 3A (60 min): “Role-based learning path”

Design a 4-week enablement plan for one function:

• learning objectives, activities, artifacts, assessments
• job aids and templates to embed into workflows

Lab 3B (45 min): “Manager toolkit”

Create:

• manager conversation guide
• performance review prompts/checklist
• adoption reinforcement plan (weekly routines)

Bloom-aligned objectives

• Apply: a structured rollout plan across waves
• Analyze: pilot learnings and iterate the operating model
• Create: a rollout plan with clear guardrails and escalation paths

Topics

• Adoption rollout patterns
  • wave-based rollout (pilot cohorts → expansion cohorts → enterprise)
  • readiness gates for each wave (policy, training, support, monitoring)
• Support model
  • tiered support (self-serve, champions, central team)
  • handling incidents and escalations (misuse, hallucinations, data exposure)
• Process governance for rollout
  • change control: how prompts/templates/agents are updated
  • release notes and communication cadence

Lab 4A (75 min): “Rollout plan”

Create a rollout plan with:

• cohorts, timelines, readiness criteria
• enablement plan per wave
• support model and escalation flow

Lab 4B (30 min): “Incident tabletop”

Scenario-based exercise:

• AI-generated incorrect guidance causes operational impact
• design response steps, communications, and prevention actions

Module 5 — Measuring adoption and value realization (3 hours)

Bloom-aligned objectives

• Understand: what to measure and why (leading vs lagging indicators)
• Apply: a measurement plan tied to business outcomes
• Create: an adoption/value dashboard specification and review cadence

Topics

• Adoption measurement model
  • leading indicators: active users, repeat usage, template reuse, task completion rates
  • workflow indicators: cycle time, rework reduction, SLA improvements
  • risk indicators: policy violations, escalation volumes, audit findings
• Value realization discipline
  • baseline before rollout
  • attribution vs correlation
  • qualitative + quantitative evidence
• Governance cadence
  • weekly adoption reviews (operational)
  • monthly value reviews (business owners)
  • quarterly risk/compliance reviews (assurance)

Lab 5A (90 min): “Dashboard design”

Produce a dashboard spec:

• KPIs, definitions, sources, cadence, owners
• thresholds for action (scale/iterate/stop)

Lab 5B (30 min): “Value story”

Create a value narrative:

• what changed in the workflow
• what evidence proves it
• what remains risky and how it’s controlled

Bloom-aligned objectives

• Analyze: sustainability risks (drift, shadow usage, policy fatigue)
• Evaluate: operating model maturity and continuous improvement needs
• Create: a 90-day sustainability plan and governance routines

Topics

• Maturity model for AI augmentation
  • ad-hoc usage → standard playbooks → embedded workflows → managed continuous improvement
• Continuous improvement loops
  • feedback intake (users, managers, risk)
  • prompt/template/agent change management
  • quality audits and periodic red teaming
• Culture and ethics
  • transparency norms
  • “human accountability” principles
  • psychological safety in reporting issues
• Scaling enablement
  • role-based playbooks at scale
  • onboarding for new joiners and new managers

Workshop (75 min): “90-day sustainability plan”

Create:

• routines (office hours, champions, review forums)
• update cadence for templates and policies
• quality and risk audit cadence
• roadmap of next workflow waves

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

Teams present a change pack:

• stakeholder plan + comms plan
• enablement architecture
• rollout plan + support model
• measurement dashboard + review cadence
• sustainability plan and next-wave roadmap