AI is already part of how work gets delivered.
It’s in the tools your teams are using, the platforms you’re recommending, and the solutions you’re putting in front of clients. It’s being introduced quietly often without much scrutiny. That raises real concerns around data privacy, sovereignty, and who actually controls the systems your work depends on. A lot of AI-enabled workflows depend on sending data to systems you don’t control, running on models you can’t inspect, in environments you didn’t choose. That creates a gap between what organisations believe they are responsible for, and what is actually happening.
We can close that gap.
We’ll look at how AI is really showing up in client work today in delivery pipelines, vendor decisions, and everyday tooling. Let's sort out where risk is introduced, how sovereignty is affected by common integration patterns, and what it takes to stay in control.
We'll review:
• Where AI is entering projects without clear oversight
• How data moves through typical AI-enabled workflows
• The implications of relying on closed vs open ecosystems
• How vendor choices shape long-term control and flexibility
• What transparency looks like when you actually need to explain a system
• Approaches to governance that can be applied in active projects
We don't need to slow teams down, but let's make sure the decisions being made are intentional, understood, and aligned with the responsibilities you carry.
Learning Objectives
1: Identify where AI is entering their delivery workflows and assess the risks to data privacy and sovereignty
2: Evaluate AI vendors and tools more critically, understanding how data is handled, where it is processed, and what control is retained
3: Recognise integration patterns that introduce hidden risk, including impacts on transparency, auditability, and long-term flexibility
4: Apply practical approaches to governing AI in active projects, including defining ownership, setting boundaries for data use, and making deliberate architectural decisions
This session is aimed at people involved in delivering digital products or client work. No prior AI experience is required.