Guest: Gavin Goodland, Chief Data Officer at National Grid

AI is creating extraordinary opportunities for businesses, but it is also creating an enormous new demand for energy.

In this episode of Databricks Diaries, Andy Davis sits down with Gavin Goodland, Chief Data Officer at National Grid, to explore the relationship between AI, data centres and the infrastructure required to power an increasingly digital economy.

As hyperscale data centres add significant new loads to the electricity network, National Grid is simultaneously using AI, real-time data and advanced optimisation to help manage a grid that is becoming more complex, distributed and dynamic.

Gavin explains how AI is being applied across National Grid — from predicting equipment performance and optimising network capacity to physical AI, drones, workforce training and intelligently managing demand from data centres.

The conversation also goes beyond individual use cases into a bigger question facing every enterprise: how do you turn rapid AI innovation into sustainable business value?

They discuss:

• Why AI and data-centre growth are becoming a major consideration for the electricity grid
• How AI can help balance demand, generation and network capacity
• Using real-time operational data to get more value from existing infrastructure
• Physical AI, robotics and drones across critical infrastructure
• Why National Grid starts with the business problem rather than the technology
• The importance of keeping the “human in the lead”
• Moving employees from curious, to comfortable, to confident with AI
• Where organisations should build AI themselves versus buy from technology providers
• Why legacy technology shouldn’t prevent organisations from innovating
• The emerging importance of an AI control plane for integration, observability, lineage and governance
• Managing the rapidly changing economics of AI and consumption-based technology
• Why Gavin believes organisations should establish an AI Value Office to evaluate value and risk consistently

A fascinating look at AI from an organisation sitting at the intersection of two of the biggest technology and infrastructure shifts happening today: the growth of AI and the transformation of the energy system.

Is AI making technical skills less important?

Or is it actually making experience, judgement and technical understanding more valuable?

In this episode of Databricks Diaries, Dan Thornton is joined by Sham and his son Milo for a conversation about AI, data engineering, careers and what companies should really be looking for when hiring data talent.

They get into:

• What being “AI-ready” actually means

• Why strong data foundations matter before AI can give you useful answers

• How AI helped turn work that could have taken 2–3 months into a couple of weeks

• Why you still need to understand and review AI-generated work

• How AI is changing the day-to-day role of data professionals

• What Milo looks for when hiring data analysts

• Why curiosity and problem-solving matter when assessing candidates

• What makes a strong modern data engineer

• Whether companies should prioritise experienced AI-savvy engineers over junior “vibe coders”

There’s also a more personal side to the conversation, with Sham and Milo talking about their different routes into technology, career advice and what they’ve learnt from each other.

The big question:

Has AI actually made technical experience less valuable — or more valuable?

What do you think?

Connect with Dan Thornton and Primus Connect for specialist Databricks recruitment.

#Databricks #AI #DataEngineering #DataAnalytics

AI experimentation is easy. Turning those experiments into something that delivers genuine business value is much harder.

In this episode of The Databricks Diaries, Daniel Thornton speaks with Natalia about the realities of taking AI from experimentation into production.

Natalia has spent around a decade working across data and AI, including building AI capability from scratch in a regulated banking environment. She shares practical lessons on what organisations need to get right before they can start delivering real AI impact.

We discuss:

✔️ Why organisations need to start with the business problem, not the technology

✔️ Building the right platform and environments for AI

✔️ How to prioritise AI use cases

✔️ Why early wins can help build confidence and secure further investment

✔️ The importance of measuring real business impact

✔️ Where organisations are currently seeing value from AI

✔️ What the future of AI and self-service could look like

One of the biggest takeaways: don't ask what you can do with AI. Start by asking what problem is worth solving.

Connect with Daniel on LinkedIn:

https://www.linkedin.com/in/databricksdan/

Learn more about Primus Connect:

https://www.primus-connect.com/

#Databricks #AI #ArtificialIntelligence #DataEngineering #DataLeadership

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.