In Episode 29 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Lee Fulmer, Head of Data at Canada Life Reinsurance, where they discuss why clean, well-governed data still isn’t AI-ready, and why context, a clear business outcome and curation matter more than any blueprint.
Canada Life Reinsurance reported around a billion dollars in profit last year from roughly fifty deals, with a workforce of around 400 people in which almost everyone is an actuary. Lee reports to the CEO as the first non-actuary the business has hired, which makes it a high-stakes setting for questions about data, AI and accountability. He also explains why the “AI will replace the data team” belief has faded, how cost and the general ledger are forcing a rethink, and why agentic AI is not the right answer for most businesses yet.
They also discuss:
- Why a data leader in a business with billion-dollar profits on a handful of deals can’t afford to treat data as raw numbers alone.
- Why the belief that AI makes the data team redundant took hold, and why it has unwound.
- How the shift to small language models and proprietary training data is putting data back at the centre.
- Why companies are hiring heads and directors of data rather than chasing the Chief Data Officer title.
- What matters more than a job title: responsibility, reporting line and empowerment.
- Why high-quality, well-governed data is not the same as being AI ready.
- What data governance has missed: the context behind a piece of information, not just its accuracy.
- How actuaries and reinsurance show the difference between raw data and the context that gives it meaning.
- Why context, not content, is king, and what the knowledge management craze of the 1990s teaches us.
- Why there is no universal blueprint for AI readiness, and why every organisation is a unique case.
- How organisational behaviour and change management matter more than technical skills in data transformation.
- Why this wave of AI is different: it is accessible to everybody, not just a few million specialists.
- How a generation that has grown up digital is approaching AI differently from older colleagues.
- How the general ledger is forcing leaders to question spiralling AI and data platform costs.
- Why many businesses have more AI pilots than they can measure, and what the RPA era should have taught them.
- Why the cost of compute is rising, and why success measures must be defined before AI is deployed.
- Why agentic AI risks repeating the RPA story in organisations with weak IT foundations.
- How fear of missing out drives AI decisions, and why starting with the outcome gives the right answer.
- What leaders should do first if their data sits in a data lake: curate it around the outcome, and keep the context.
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Thanks to our sponsor FOIL AI
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