Education

Your team already knows where AI could help. Let’s surface it.

A hands-on workshop that builds shared AI fluency and produces a real shortlist of opportunities from inside your own workflows—not from a deck about what other companies are doing.

Half-day or full-day Remote or on-site Up to 20 participants No technical background required

Workshop agenda

Half-day format shown. Full-day adds deeper exercises and a working prototype session.

Opening — 30 min

What AI Actually Does (and Doesn’t)

Cut through the hype with a clear, non-technical framing of what today’s AI is genuinely capable of—and where it consistently falls short.

  • The three modes: automation, assistance, and augmentation
  • Why most AI failures are design failures, not technology failures
  • A practical vocabulary your whole team can use
Module 1 — 45 min

Design Thinking Applied to AI Problems

Apply human-centered design principles to find where AI creates real leverage in your specific context.

  • Empathy mapping: what your team actually spends time on
  • Defining the right problem before reaching for a solution
  • The AI Opportunity Canvas—a structured framing tool
Module 2 — 60 min

Hands-On: Map Your Own Workflows

The core exercise. Small groups pick a real workflow, map it step by step, and identify where AI could meaningfully reduce friction or improve output.

  • Structured workflow mapping in groups of 3–4
  • Evaluating candidates: Value / Effort / Risk scoring
  • Group readout and facilitator synthesis
Module 3 — 45 min

From Idea to a Real Experiment

Turn the strongest opportunity from the workflow exercise into a scoped pilot concept your team can actually run.

  • What a meaningful AI pilot looks like vs. a proof-of-concept
  • Defining success before you start
  • Common failure modes—and how to design around them
Module 4 — 30 min

Governance, Risk & Responsible Use

What your team needs to understand before experimenting—not as a brake on progress, but as a foundation for doing this well.

  • Data privacy, accuracy, and oversight basics
  • Who should review what before AI touches real work
  • Practical guardrails for safe experimentation
Closing — 30 min

Building Your Team’s AI Practice

How to sustain momentum after the room disperses—including the habits, structures, and conversations that keep AI thinking alive inside your team.

  • Making AI decisions as a team, not just a champion
  • How to evaluate new tools and vendor claims critically
  • Next steps and owner assignments from today’s work

Who this is for

Leadership & Strategy Teams

Leaders who need to make informed AI decisions without becoming technical experts. Builds the shared understanding required to evaluate opportunities, set direction, and ask the right questions of vendors and internal teams.

Functional & Operations Teams

Teams closest to the work—finance, HR, legal, operations, customer success. They know where the friction lives; this workshop gives them the framework to translate that knowledge into AI opportunities with business cases.

Mixed Cross-Functional Groups

Particularly effective when technical and non-technical team members are in the same room. Creates a shared vocabulary across functions and surfaces connections between opportunities that siloed conversations miss.

What your team walks away with

The workshop produces real outputs, not just increased awareness. Every team leaves with work product they can act on immediately.

A shared AI vocabulary A common language across technical and non-technical team members—so your next AI conversation starts from the same baseline instead of talking past each other.
A prioritized opportunity shortlist 3–5 AI use cases sourced directly from your team’s own workflows, scored by value, feasibility, and risk—not from an industry trends deck.
One scoped pilot concept Your top opportunity developed into a concrete experiment: defined scope, success criteria, and a rough estimate of what it would take to test it in 30–60 days.
Workflow maps you keep Documented process maps from the group exercise—structured, annotated, and useful beyond AI planning for any operational improvement initiative.
Practical governance guardrails A starting-point framework for responsible AI experimentation: what needs review, who owns decisions, and how to handle data safely as you begin testing.
Momentum and a clear next step Assigned ownership for post-workshop follow-through, with specific next actions for each opportunity—so the energy from the room doesn’t dissipate by Thursday.
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