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Building an AI-Ready Organization — People, Processes, and Culture for AI Adoption

Building an AI-Ready Organization focuses on the organizational conditions that allow AI to move from isolated experiments into sustained business capability. The program examines readiness across leadership, people, processes, skills, culture, operating models, and governance, with particular attention to how work and decision-making change when AI is introduced. Participants diagnose adoption barriers, design practical workforce and change interventions, and build the structures, behaviors, and measurement routines needed to scale AI responsibly across the organization.

Rome

5 days

September 28, 2026 – October 2, 2026

Price

EUR 5,500

Initial Enrollment Request

Learning Objectives

Assess organizational AI readiness across leadership, workforce, processes, culture, data, and governance.
Identify people, capability, trust, and workflow barriers that prevent AI adoption from scaling.
Design role-based AI literacy, upskilling, and manager-enablement interventions.
Define operating-model roles, decision rights, and cross-functional structures for AI adoption.
Apply change-management approaches to resistance, uncertainty, and changing job responsibilities.
Establish adoption metrics and create an actionable roadmap for building sustained AI capability.

Detailed Daily Schedule

Day 1 — Diagnosing Organizational AI Readiness

Define what AI readiness means beyond technology and infrastructure.
Assess leadership alignment, strategic clarity, data access, skills, and governance.
Map current AI adoption across teams, roles, and workflows.
Identify organizational friction, trust concerns, and barriers to scale.
Build an AI-readiness baseline for a participant organization.

Day 2 — Workforce, Skills and AI Literacy

Identify how AI changes tasks, roles, decision rights, and capability requirements.
Segment AI literacy and learning needs by workforce role.
Design practical upskilling pathways for users, managers, specialists, and leaders.
Examine reskilling, redeployment, and workforce-planning implications.
Create a role-based AI capability and learning plan.

Day 3 — Culture, Trust and Change Leadership

Diagnose resistance, anxiety, overconfidence, and other adoption behaviors.
Apply change-management principles to AI-enabled ways of working.
Build psychological safety for responsible experimentation and learning.
Develop manager, champion, and peer-support networks for adoption.
Design communication and engagement routines that strengthen trust.

Day 4 — Operating Models and Adoption Governance

Compare centralized, federated, and embedded approaches to AI enablement.
Clarify responsibilities across business, HR, IT, data, risk, and AI teams.
Design governance that enables experimentation while maintaining appropriate control.
Embed human oversight and responsible-use practices into everyday workflows.
Establish adoption support, escalation, and feedback mechanisms.

Day 5 — Scaling and Measuring AI Adoption

Define adoption, proficiency, workflow, and value-realization indicators.
Build feedback loops that reveal where AI is and is not changing work.
Sequence capability-building and change interventions by organizational priority.
Plan how successful pilots transition into repeatable enterprise practices.
Produce an AI-ready organization roadmap with milestones, owners, and measures.