Leadership Training in AI: What a Good Program Needs, in 7 Components

By Pedro Sonda, dooop

A good AI leadership training program has seven components: (1) diagnosis of leadership and company priorities; (2) tracks by level (board, executives, managers); (3) work with real company decisions; (4) scenario analysis and strategy; (5) governance and risks; (6) leading change in teams; and (7) follow-up rituals after the program. The quality criterion is practical: leaders make better AI decisions during the program itself and continue to do so afterward. Lectures on trends alone do not meet this criterion.

Updated October 2026.

What the Data Demands from a Program

Data	Source
Only 8% of HR leaders believe managers have the skills to use AI effectively	Gartner, 2025
46% of managers experiment with AI, compared to 26% of employees; only 14% of managers say they face no challenges with AI	Gartner, 2026
66% of board members and executives say their boards have limited or no AI knowledge or experience	Deloitte, 2025
Only 25% of frontline workers say they receive sufficient guidance from leaders on AI	BCG, 2025

Interpretation: Leaders are already using AI but face challenges and are not prepared to guide their teams. A program needs to go beyond personal use and reach change management.

dooop Framework: The 7 Components

#	Component	What It Includes	Question to Evaluate a Program
1	Diagnosis	Current leadership knowledge; company AI priorities	Does the program start by understanding our situation?
2	Tracks by Level	Different content and objectives for board, executives, and managers	Does the content vary by level?
3	Real Decisions	Prioritizing cases, evaluating proposals, redesigning a company process	Do leaders make real decisions during the program?
4	Scenarios and Strategy	How AI changes the sector; where the company wants to compete	Is there an analysis of our sector, not just general trends?
5	Governance and Risks	Responsibilities, rules, prohibited uses, limits	Do leaders leave knowing what to decide about risks?
6	Leading Change	Communication, resistance, time allocation, team preparation	Do managers leave knowing how to lead their teams?
7	Rituals	AI agenda items, reviews, manager follow-up	What continues after the program ends?

The design of modules with real decisions is detailed in How to Train Leaders to Make Decisions with AI. dooop, for example, organizes its leadership program in three phases: anticipate (scenarios and differentiation), orchestrate (strategy, roadmap, indicators, and alignment), and adjust (governance, dashboards, and learning cycles) (dooop).

Warning Signs

Sign	Why It Is Concerning
Program only has inspirational lectures	Does not change decisions
Same content for executives and managers	Ignores different roles
No real company decision worked on	Learning remains abstract
No mention of governance	Leaders decide without considering risks
Program ends at last meeting	Without rituals, effect fades

How to Measure the Program

Level	Indicator
Decision	AI decisions made during and after the program
Leadership	Teams with defined AI uses and manager guidance
Rituals	AI regularly on the agenda with defined frequency
Outcome	Indicators of AI priorities

For measuring training programs in general, see How to Measure if an AI Training Really Worked.

What to Do Now

Use the 7 components as a checklist for your current program or proposals.
Define which real decisions leaders will work on during the program.
Plan the rituals that will continue afterward.

For a leadership program connected to company decisions, see AI-Focused Leadership by dooop.

Frequently Asked Questions

What is the ideal duration for a leadership program? It depends on the level and objective. Programs with sessions spread over weeks, interspersed with practice on real decisions, tend to be more effective than concentrated events.

Does a leadership program replace AI literacy for everyone? No. They are complementary: prepared leaders lead; literate teams execute with judgment.

About dooop

dooop is a consultancy based in Caxias do Sul (RS) that prepares leaders and organizations to transform artificial intelligence into business capability, with strategy, governance, and training.