Project Lighthouse

Leadership_Brief

Individuals are saving time or improving their work, but those gains still depend largely on personal initiative.

AI activity is increasing, but progress is still described mainly through usage, pilots, or completed use cases rather than measurable business value.

Some teams can demonstrate meaningful results, but the learning is not consistently adapted or reused elsewhere.

AI is changing parts of the work, but roles, processes, incentives, governance, or decision rights still reinforce the old ways of working.

We have invested in AI, but meaningful improvement remains difficult to demonstrate.

It is still too early for us to know what pattern is emerging.

Other teams become interested, but each team figures things out in its own way.

Leaders encourage broader adoption, but progress is inconsistent.

Successful approaches are documented, but they rarely change how the organization operates.

The organization deliberately adapts processes, governance, leadership practices, or ways of working so the improvement can create broader value.

Success depends largely on who is leading the initiative.

How many people are using AI tools or how frequently they are being used.

How many AI initiatives, pilots, or use cases are underway.

Productivity improvements within individual teams or functions.

Business outcomes such as customer experience, operational performance, or financial results.

Whether successful improvements consistently become part of how the organization works.

We are still determining what success should look like.

Individual employees are expected to adapt how they work.

Managers determine how the team's work should evolve.

Each team develops its own approach with limited coordination.

Leaders deliberately adapt roles, processes, decision-making, and ways of working as the work changes.

Very little changes beyond introducing the technology.

Project Lighthouse

Before You Begin