In this episode, Andy Davis is joined by Reno Daigle, CIO and Head of Underwriting Operations & Transformation at CNA Hardy, to explore what creating genuine business value from AI actually looks like inside a large insurance organisation.

Reno shares his perspective from more than 25 years across financial services, technology, operations and transformation, and explains why AI should be viewed as much more than another automation tool.

They discuss how AI can improve both effectiveness and efficiency, helping insurers make better decisions, improve customer outcomes, increase throughput and extract value from decades of historical data.

A key theme is the need to move beyond isolated AI use cases. Reno argues that the real opportunity comes when organisations use AI to rethink their end-to-end operating model, rather than simply adding AI into individual steps of existing processes.

Andy and Reno also explore the challenges that come with adoption, including security, governance, rapidly increasing AI costs and the need to prove genuine return on investment.

They discuss:

🔹 Where AI is already delivering value across insurance

🔹 The difference between using AI for efficiency versus effectiveness

🔹 Why AI has the potential to fundamentally reshape insurance operating models

🔹 Using decades of insurance data to improve underwriting and decision-making

🔹 The opportunities AI creates for entirely new insurance products and risk categories

🔹 Balancing AI adoption with security, governance and cost

🔹 Why organisations shouldn't discourage experimentation too early

🔹 The importance of choosing the right AI use cases rather than adopting technology for its own sake

🔹 How AI could reshape jobs and the skills organisations need

🔹 Why leadership needs a clear view of how AI investment will ultimately generate value

Reno also reflects on the wider impact AI could have on the workforce, drawing parallels with previous technology shifts such as computers, spreadsheets, automation and outsourcing.

His message for leaders is clear: organisations cannot afford to ignore AI, but successful adoption requires strong governance, clear use cases and a deliberate balance between innovation, cost and risk.

A thoughtful conversation for technology, data and insurance leaders trying to move beyond AI experimentation and understand how it can create sustainable business value.

What does it take to build AI responsibly when the technology is evolving faster than the rulebook?

In this episode of The Databricks Diaries, Daniel Thornton sits down with Anna Gressel, Partner and Global Co-Head of AI at Freshfields, to explore the legal, governance and strategic challenges shaping the next generation of artificial intelligence.

From advising some of the world's leading AI developers and global enterprises to helping boards navigate AI risk, Anna offers a unique perspective from the intersection of technology, law and business strategy.

Together, they discuss:

• Why AI governance is no longer just a compliance exercise
• The shift from chatbots to autonomous AI agents—and why it changes everything
• How organisations can balance rapid AI experimentation with responsible governance
• The biggest mistakes companies make when implementing AI
• What leaders should be asking themselves to assess their AI readiness
• Why today's junior employees may soon become managers of teams of AI agents
• The emerging importance of AI incident response and cybersecurity
• How legal teams are becoming strategic partners in AI transformation
• Why "world models" could be the next major breakthrough in artificial intelligence

Whether you're a CIO, CTO, CDO, Head of Data, AI leader or technology executive, this conversation provides practical insights into preparing your organisation for an AI-driven future while staying ahead of regulatory, operational and competitive risks.

If you're thinking about AI adoption, agentic AI, governance, or the future of intelligent systems, this is an episode you won't want to miss.

How do organisations move beyond deploying AI tools and begin creating measurable, sustainable value?

In this episode of Databricks Diaries, Andy Davis speaks with Alexandra Diem, Tribe Lead and SVP for Data, Analytics and AI at Gjensidige, about the practical realities of implementing AI within a large, regulated organisation.

Alexandra explains how her team began by using generative AI to remove tedious and repetitive work for analysts. However, the project quickly exposed a wider challenge: AI is only as useful as the data, metadata and documentation supporting it.

They discuss why data governance, join logic, business definitions and semantic layers are becoming increasingly important as organisations introduce natural-language access to data.

The conversation also explores the changing economics of AI. As developers become more productive with tools such as GitHub Copilot, organisations must consider model selection, token consumption and FinOps practices to ensure that increased productivity does not create uncontrolled costs.

Andy and Alexandra also examine the difficulty of proving the value of internally focused AI initiatives, where the connection between investment and revenue is often less visible than it is with customer-facing products.

Topics covered include:

• Identifying genuine organisational problems before selecting an AI solution

• Using AI to automate repetitive analytical work

• Why metadata and documentation are critical for trustworthy AI

• Building automated governance into the data platform

• Managing AI consumption, model selection and token costs

• Balancing short-term business cases with longer-term AI investment

• Creating effective semantic layers for people and AI

• Why back-office processes may offer the greatest immediate AI opportunity

• Helping developers use AI productively without becoming overly dependent on it

Alexandra’s central advice is simple: do not begin by asking what you can automate with AI. Begin by identifying where users are getting stuck, where processes slow down and where handovers create friction. Those bottlenecks are often where AI can generate the most meaningful value.

Databricks Diaries is hosted by Andy Davis and explores how organisations are using data, analytics and AI to deliver practical business outcomes.

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.

In this episode of Databricks Diaries, Daniel Thornton sits down with Andreea Moldovan, Associate Director of Data Science at Sage Publishing, for a candid and wide-ranging conversation about what it actually takes to lead data science and AI in the real world.

Andreea's journey is anything but conventional — starting as a social scientist in Romania, she went on to complete a PhD funded by the Wellcome Trust, before moving through the Financial Conduct Authority, a specialist insurance company, and a price reporting agency, to ultimately find her home in academic publishing.

In this episode, they explore:

• Why a background in social science is one of the most underrated assets in data science and AI
• How to quantify and communicate uncertainty to non-technical stakeholders without losing their trust
• The mindset shift from individual contributor to data science leader — and why it's harder than it looks
• What "outcome-first" thinking really means in practice when building AI solutions
• How generative AI is changing the shape of data science roles — and why the fundamentals haven't changed as much as people think
• The one constant in a decade of working in data science: change

Whether you're an aspiring data leader, a seasoned practitioner, or simply curious about the human side of AI, this is a conversation packed with honest reflections, hard-won lessons, and a refreshing dose of realism about what the next few years hold.

In this episode of The Databricks Diaries, Andy Davis speaks with Sindy Yick, Head of Data Science and Machine Learning at Markerstudy Group, as part of our ongoing AI readiness series.

Sindy brings a valuable data science perspective to the conversation, exploring why AI readiness is not just about adopting the latest tools or building agents. Instead, it starts with strong data foundations, good system design and a clear understanding of where AI can genuinely add value.

The conversation also tackles one of the more difficult topics in AI adoption: the anxiety technical teams feel around AI agents and automation. Sindy shares her view on why AI should be treated as an assistant rather than a replacement, why junior talent still matters, and how organisations may need to rethink how they train and develop early-career data professionals.

In this episode, we cover:

• Why data quality and system foundations come before AI adoption
• The “garbage in, garbage out” risk when applying AI to poor-quality data
• How data science and machine learning teams are reacting to rapid AI change
• The impact of coding agents on junior data science and engineering roles
• Why AI is more likely to assist technical teams than fully replace them in the near term
• The importance of stakeholder engagement, business knowledge and industry context
• How organisations may need to rethink graduate and junior training pathways
• Why human judgement, communication and strategic thinking are becoming more valuable
• Potential AI use cases across insurance, including operations and underwriting
• The importance of guardrails when moving AI closer to customer-facing or front-end applications
• Sindy’s advice for data science leaders: stay open-minded and keep adapting

AI readiness is not just about adopting the latest model or building the next agent.

In this episode, Andy Davis speaks with Sindy Yick, Head of Data Science and Machine Learning at Markerstudy Group, about why strong data foundations, guardrails and human skills are critical to making AI work in practice.

They also explore the future of junior technical roles, the rise of coding agents, and why business knowledge, stakeholder engagement and adaptability may become the most important skills for data science teams in the years ahead.

In this episode of the Databricks Diaries, Daniel is joined by Kieran Pointon, Director of Data & Analytics at Bromford Flagship LiveWest — one of the largest housing providers in the UK with around 120,000 homes following a recent three-way merger.

Kieran brings 20+ years of experience across data engineering, BI, and enterprise architecture, including leadership roles at Halfords and Adidas. In this conversation, he opens up about the realities of merging three data teams, why he's betting on Microsoft Fabric over Databricks for his current platform, and how data science is helping real people get into work, education, and safer homes.

What's covered:

• Kieran's journey from headphones-on software engineer to data leader — and how being an introvert shaped his leadership style
• Why "listen before you speak" is his number one piece of leadership advice
• Building a high-performing data team at Halfords that could run without him
• Merging Bromford, Flagship, and LiveWest — three teams, three landscapes, three ERPs
• Why he chose Microsoft Fabric over Databricks for the new platform (and the five-year bet behind that decision)
• The "left brain vs right brain" model: where the technology approach and business approach meet in the middle
• Real data science use cases changing tenants' lives — condensation/damp/mould prediction, customer complaint modelling, and the "Pipeline of Talent" NLP model that's helped 450+ customers into work and education
• How to get exec and board buy-in for data and AI initiatives
• What makes the best analytics team — and why it's not always about the best tools or the best engineers
• Honest advice for any data leader facing a merger or acquisition

In this episode of the Databricks Diaries, Daniel is joined by James Blagg, a seasoned Chief Data Officer with over 25 years of experience leading data strategy, architecture, and modernisation programmes across financial services.

James shares hard-won lessons from a career that spans the 2008 financial crisis, the big data hype cycle, the move from Teradata appliances to cloud-native platforms, and now the AI wave. It's a candid conversation about what actually drives value in data — and the gap between vendor promises and operational reality.

What's covered:

• James's career journey from Oracle PL/SQL developer to CDO
• Leading BI teams at Lloyds through the financial crisis and the HBOS integration
• Nationwide's data simplification programme and the "almost cloud" Teradata investment
• How platform evaluation has changed — and why cost, skills, and relationships now matter more than features
• Why "it's on the roadmap" is the most dangerous phrase in vendor selection
• The MVP hypothesis approach to picking analytics use cases
• Why so many data warehouse projects are perceived as failures (and why metadata investment is finally fixing it)
• Where AI really sits on the maturity curve — and why financial services is 10–15 years away from full back-office adoption
• A practical playbook for governance, cost monitoring, and security guardrails before scaling AI on Databricks
• The truth about cloud consumption pricing and reserved pricing deals