How far do you want to take AI in your organisation? Improving a task, redesigning a service and changing how departments work together require different commitments from leadership.

A team proves that AI can make its workflow faster. Then the next improvement needs another department to change a process, share a system or accept a different responsibility. The people who built the pilot may have no mandate to make that decision. This is where I keep seeing a familiar problem: a misalignment of timing and incentives.

Using an illustrative request moving through HR, service operations and finance, I will follow the transition from conventional automation to AI-assisted work and bounded agents. We will examine how domain experts contribute as business engineers, how engineering supports their changes, and who remains accountable for quality and operation. Somewhere between the major systems, there may still be a spreadsheet that everything depends on.

The session connects those delivery decisions to leadership: cultivating capability, maintaining shared context and reusable tools, and making cross-department change worth supporting. As similar AI capabilities become widely available, domain knowledge and the ability to redesign work deserve more attention.

You will leave with a practical way to frame one business change: an outcome to improve, the authority to act, and the conditions for accepting and operating the result.

Takeaways:
1. Match the ambition for AI with the investment and authority needed to deliver the change across its affected owners.
2. Cultivate domain and engineering capability together, with shared context, reusable tools and a supported route into operation.
3. Align incentives with the completed business outcome, including quality, rework and the cost of keeping the service running.

30 minutes (25 + 5 Q&A). Intermediate. For technology and engineering leaders, enterprise architects, transformation sponsors and service owners. Standalone leadership session in the five-talk series.