Getting AI Transformation Right: Humans at the Center

Many organizations are accelerating their artificial intelligence agendas, yet the EY team’s latest Work Reimagined research shows that results vary widely. The difference is not the speed of technology adoption but whether an organization has established the human and organizational foundations required to turn AI investment into performance. Boards are increasingly seeing that the strongest outcomes come from companies that are deliberate and invest early in leadership alignment, workforce capability, and trust so that AI enables performance rather than creating disruption.

This panel discussion will examine what boards should look for when assessing whether AI transformation is truly positioned for value creation. Grounded in the University of Oxford’s Saïd Business School–EY Humans@Centre approach, as well as the most recent Work Reimagined research, the discussion will focus on workforce expectations, leadership behaviors, and organizational conditions that consistently determine whether AI scales effectively or stalls.

The panel will explore:

• Mindset as the multiplier: why leadership alignment, psychological safety and a shared narrative about AI (and agentic ways of working) matter as much as the technology itself, and how boards can assess whether leaders are prepared to redesign work, not just deploy tools

• Sequencing operating model change: how leading organizations intentionally phase workflow redesign, role and skill evolution, decision rights, and enabling technology, including where to introduce agents; the panel will also cover what questions boards should ask so that efforts are coordinated, measurable, and not prematurely scaled

• Practical readiness signals: how boards can gain visibility into where confidence exists, where friction is emerging, and where leadership intervention may be required to unlock adoption and manage risk

11:30 a.m. - 12:00 p.m.: Network lunch

12:00 p.m. - 1:15 p.m.: Panel discussion

1:15 p.m. - 1:30 p.m.: Closing network