Most teams don’t need more AI hype; they need better decisions. We sit down with data expert and founder of Head for Data, Colin Parry, to strip AI back to what actually moves the needle: clean inputs, clear processes, and tools that match the job. Colin’s path from renewables and wind turbine analytics to leading data science teams gives him a rare field‑to‑boardroom perspective on how to build systems that work in the real world.

We start with the foundations: why data only exists to improve decisions, and how a centralised platform plus solid governance turns scattered spreadsheets into a reliable source of truth. Colin breaks down the difference between deterministic tasks that deserve automation and ambiguous work where AI’s probabilistic strengths shine. He explains why ChatGPT is just one tool in a larger AI family, and how to pick the lightest‑weight solution that solves the real problem instead of forcing everything through a language model.

The episode’s centrepiece is a practical case study with a major property factoring firm. By defining a true unit of work, cleaning their data, and building an optimisation algorithm that accounts for geography, travel time, and seniority, Colin’s team rebalanced workloads across 40,000 properties. Then they layered fees over effort to expose profitability by development, empowering leaders to adjust pricing, retain the right clients, and drop the wrong ones. The result: fairer teams, sharper unit economics, and faster, more confident decisions.

If you’re wondering where to start, we share a simple path: run a focused gap analysis, centralise your data, automate the deterministic steps, and apply AI where intent or inputs are genuinely messy. Want a partner for that first step? Colin offers a free half‑day Intelligent Futures workshop to identify quick wins. Subscribe, share with a colleague who’s drowning in spreadsheets, and leave a review to tell us which process you’d automate first.