Two half-day sessions over two weeks
Plan AB Global Holding / Leadership and capability
AI Era Leadership
Build the judgment, decision habits and governance reflexes leaders need when AI enters everyday work.
Discuss this engagement →View a sample output ↓A focused first engagementIndicative duration Starting scope Working format
Start with Leadership Decision Lab.
A leadership team of approximately 6–16 participants, working on one institutional case.
Decision simulation, facilitated review and an adoption-planning workshop.
Leave with: A decision-rights map, leadership checklist and a 30-day team experiment.
What we need from you: A leadership sponsor and one real decision or adoption challenge to frame the exercises.
This is a suggested starting format. Scope, schedule, availability and fees are confirmed in a written proposal before kickoff.
Request a starter proposal →The challenge
Where the work begins.
Leaders cannot delegate accountability to a model. They need to know which decisions to augment, which to challenge and when a human must intervene.
Who it is for: Boards, executives, managers and transformation leaders guiding AI adoption.
How we work01 / Diagnose 02 / Practice 03 / Redesign 04 / Embed
A focused path from question to action.
Diagnose
Identify leadership decisions, team habits and capability gaps in the current environment.
Practice
Work through scenarios involving uncertain outputs, bias, speed, trust and escalation.
Redesign
Clarify decision rights, review rituals and team operating principles.
Embed
Build a practical playbook and follow-up exercises for continued adoption.
What you receive
Tangible outputs.
Scope and timing are agreed with you; these are the core deliverables we would shape around your context.
- Leadership capability map
- Scenario-based learning sessions
- AI decision and escalation playbook
- Team practice plan
In practice
A concrete leadership exercise
A team receives an AI-generated recommendation that is plausible but incomplete. Leaders must decide what evidence to request, who signs off and how to communicate uncertainty.
See the work
What a working output looks like.
Illustrative sample · fictional scenario · no client dataA leadership decision-rights canvas
| Decision | Accountable role | Human checkpoint |
|---|---|---|
| Choose a use case | Executive sponsor | State the problem, expected value and reasons to stop. |
| Approve AI use with data | Data or security owner | Confirm data boundaries, approved tools and escalation route. |
| Act on an AI output | Named business decision-maker | Check sources, uncertainty and the consequences of being wrong. |
Open the working brief
- Leadership exercise
- A confident AI recommendation conflicts with incomplete operational evidence.
- Discussion
- What should the team verify, who owns the decision, and what would change its mind?
- Team output
- A one-page operating agreement for AI-supported decisions.
- Follow-through
- A 30-day experiment with an owner, a review date and measures agreed at the workshop.
This sample shows the structure and level of specificity of a deliverable. The final output is built around your workflows, people and agreed scope.
Next step
Start a conversation →Bring us the real challenge.
We will frame the right starting engagement around your context and decision.