In this episode of Databricks Diaries, Andy Davis is joined by Sam Worthington, Chief Data Officer at Crux Underwriting, for a practical conversation on how insurance organisations can get real value from AI.

Sam shares his perspective on where AI is most useful in insurance, particularly around data ingestion, extraction, underwriting augmentation, bordereaux processing, fraud detection, operational efficiency and decision support.

The conversation explores why AI is not a shortcut around poor data foundations, and why insurers need to think carefully about trust, confidence scoring, conflicting data sources and when human expertise still needs to sit in the loop.

Sam also explains why London Market insurance presents a different challenge to more standardised personal lines environments. When risks are complex, specialist and often difficult to compare, AI needs to support underwriting expertise rather than simply replace it.

Key topics include:

• Why AI value in insurance often comes down to efficiency and decision support
• The role of AI in underwriting augmentation
• How insurers can use AI for ingestion, extraction and cleaner data capture
• Why speed to quote matters for MGAs
• The importance of data foundations before applying AI
• How to manage conflicting insurance data, such as slips, emails and submissions
• Why human referral and confidence scoring are critical in underwriting workflows
• The difference between top-down AI use cases and bottom-up agent adoption
• Why London Market insurance is “lumpy and bitty” compared with more standardised markets
• How to identify the right AI use cases by starting with real business pain points

Sam’s advice is clear: start with a genuine problem, embed the solution into the way people already work, build trust through controls and guardrails, and avoid trying to solve everything at once.

A valuable listen for anyone working in insurance, data, underwriting, analytics or AI who is trying to separate practical opportunity from AI noise.

Chris Greeno, CTO at Fresha, joins Andy Davis to discuss how Fresha approaches real-time data streaming, engineering productivity, AI adoption, Claude in practice, and what it takes to build technology at serious scale.

Chris shares a practical view of what it takes to scale technology inside a high-growth product business, covering Fresha’s approach to near real-time data streaming, architecture decisions, cost trade-offs, and the move from traditional reporting infrastructure towards more responsive data products.

The conversation also explores how AI is changing software engineering. Chris discusses Fresha’s adoption of tools like Claude, Cursor and other AI coding assistants, why developer adoption is often harder than expected, and how engineering leaders need to rethink productivity, safety, observability and platform tooling in an AI-enabled environment.

A particularly strong theme is mindset. Rather than seeing AI as a threat to engineering craft, Chris argues that engineers need to learn how to express their craft differently moving from writing every line of code themselves to guiding, reviewing and orchestrating AI-enabled systems.

In this episode, Andy Davis is joined by Liam Morris-Ellis, Dan Prideaux and Ankit Rawat to discuss where organisations are actually seeing value from AI today.

The conversation moves beyond hype and explores why the biggest opportunity is not just automating individual tasks, but rethinking workflows, scaling productivity and building the right data foundations. The group also discusses AI adoption, trust, governance, data quality, human-in-the-lead thinking, and why successful AI programmes need to bring business, risk, compliance and technology teams together.

A practical discussion for data, analytics, AI and transformation leaders who want to move from experimentation to measurable business value.