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AI Literacy & Adaptivity Lab

Our Four-Day Facilitation Deck

Leverage the full added value of AI without undermining the team’s judgment, accountability, and intellectual performance, or eroding trust within the team.

POSITIONING: What the AI Literacy Lab is—and what it isn’t

It is ➞ A practical, hands-on leadership lab | Every block ends in an artifact or a decision | Grounded in one real workflow per participant | A producer of credible AI-literacy evidence

It’s not ➞ A tool catalog or product demo | A prompt-tricks or hacks course | A legal compliance seminar

AI is rapidly becoming an issue for organizations in leadership, governance, and trust—not just a technology problem. Therefore:

  • AI Literacy should improve judgment.
  • AI belongs in real workflows, not random experimentation.
  • Human accountability must stay explicit.
  • AI output is probabilistic and must be reviewed.
  • Teams need psychological safety as roles shift.

What participants will be able to do after the course:

  • Decide what stays human, what AI can assist.
  • Review AI output without treating it as authoritative.
  • Reduce low-value work while protecting judgment and trust.
  • Brief AI with context, constraints, and quality criteria.
  • Explain AI-supported work to your team.
  • Run limited adaptive experiments instead of a grand transformation.

How we will work during the course:

  • With the use of real workflows
  • Without pasting confidential data
  • Treating AI output as a draft or a hypothesis
  • Keeping human accountability explicit
  • Asking for help early if we are stuck

INQUIRY SPINE—Five questions that run through the lab:

  1. What are you really trying to achieve?
  2. Where does AI add leverage, and where unacceptable risk?
  3. What must remain human?
  4. How do we verify quality when output is probabilistic?
  5. How do we introduce AI Literacy without breaking trust?

The ADAPT cycle forms the backbone of the course

DAY ONE → AI literacy, reality & judgment

  1. Demand & reality → OUTPUT: Baseline self-assessment
  2. AI as probabilistic assistant → OUTPUT: Risk & limit checklist

DAY TWO → AI briefing & new workflows

  1. From user to orchestrator → OUTPUT: AI briefing template
  2. Align & Accept → OUTPUT: Workflow intent canvas
  3. Discern & Diagnose → OUTPUT: Current workflow map

DAY TREE → Testing & Oversight

  1. Amend & Ascertain → OUTPUT: Sustainable distribution of responsibility
  2. Testing the workflow → OUTPUT: Workflow test notes & quality gates
  3. Human oversight → OUTPUT: Oversight protocol & checklist

DAY FOUR → Adoption—Turning the mapped workflow into a human-AI experiment

  1. Team adoption → OUTPUT: Team introduction & implementation script
  2. Propose, Pursue, Track, Testify → OUTPUT: Iterative experiment plan

Fundamental aspects of the curriculum:

AI Literacy is not tool familiarity; it is informed judgment in context → Compliance urgency + Leadership value = Informed judgment → (Day One 01)

Generative AI predicts and composes plausible output → Treat it as a draft, a hypothesis, or an assistant contribution—never as a final authority → (Day One 02)

From user to orchestrator → Stop using AI as a tool. Start briefing it like a capable assistant under supervision—the infinite interns you must direct and review → (Day Two 01)

Define context before redesign → Do not apply AI before the work’s purpose is clear. Align on purpose, stakeholders, constraints, sensitivity, accountability, and success → (Day Two 02)

Where AI helps, where it harms →  Find leverage in repeated drafting, summarizing, comparing, and checking. Find risk where context, accountability, fairness, or emotion matter → (Day Two 03)

Redesign around responsibility →  Redesign starts with responsibility, not automation. AI can draft, summarize, compare, critique, or monitor — but not every task should use AI → (Day Three 01)

Test the workflow → A workflow is not improved by intention—it improves through testing. Run one small, safe AI-supported step and compare it to the current process → (Day Three 02)

Human oversight → Oversight means defined checks, not vague responsibility. Name escalation triggers and no-AI zones, and decide what evidence to retain → (Day Three 02)

Team adoption → AI changes informal power, status, speed, visibility, and expectations. People may fear replacement, surveillance, deskilling, or embarrassment → (Day Four 01)

From redesign to a controlled experiment → The next step is not an agile transformation. It is a controlled iterative experiment (14 days up to 1 month) with a clear hypothesis, boundaries, measures, review rhythm, and stop criteria → (Day Four 02)

Enjoy AI Literacy & Adaptivity!