What’s in this podcast?
In this episode, we look at the real story behind transformation in data and AI, and why the classic big bang approach often fails to deliver lasting impact.
Jason explores when large-scale transformation programmes do make sense, like when you’re starting from a fundamentally broken place, or when disruption is the goal. But he also digs into the messy reality of what usually happens: slow delivery, rigid plans, lost trust, and a disconnect between activity and real outcomes.
He then makes the case for iterative change. A more human, responsive, and sustainable way to build meaningful transformation over time. With real-world examples and sharp reflections, Jason shares how small, focused steps can create big shifts, and how to blend bold vision with practical delivery.
This episode is full of insight for business and data leaders navigating change, delivering transformation, or just trying to make something actually stick.
