What makes a good analyst when AI can write the SQL for you?
In this episode of AI in Action, Dan Lewis talks to Matthew Jennings about his career in data analytics and what he’s learned about leading data teams. Matthew started with a master’s degree in geographical information systems (GIS), which gave him a strong grounding in customer data and demographics. 
Matthew explains why data teams work best as trusted partners rather than a support function. He also explains why keeping analysis simple often matters more than being clever. Marketers need clear insight they can act on quickly, so analysts should tell the story rather than bury people in technical detail.
The conversation then turns to AI. Matthew talks about how it’s taking on the manual work that used to eat up analysts’ time, from writing code to preparing data, so they can focus on interpretation and storytelling. Dan and Matthew also discuss a real concern: what happens to junior analysts when the entry-level tasks they learned on are automated?
They also cover:
Why human judgement, context and relationships still can’t be replaced
How to set a data strategy when the technology keeps changing
Why adopting new technology is as much a people challenge as a technical one
What to look for when building a team: analytical minds who also enjoy explaining their work across the business

How do you use AI in a business built around energy, and where should people still make the calls?
In this episode we speak to Jack Fielder, Chief Commercial Officer at myenergi. Jack explains how the company's products help households see, manage and optimise the energy they produce and use, saving both money and carbon. He also talks about Grid Pay, a new service that pays customers when their home devices help balance the national grid.
Jack splits the company's approach to AI into two parts: AI in the business and AI in the product. He covers how teams experiment safely with general AI tools, how they share what works across the company, and why he sees this as a data transformation rather than an AI transformation.
We also cover:
Why high UK energy prices make smart, automated optimisation more important for households
Why automation matters more than behaviour change when markets, weather and tariffs are too complex to manage by hand
Where autonomous data pipelines work, and why customer-facing AI still needs a human in the loop
Jack's view of AI adoption two years from now: used in most workflows, with people still accountable for decisions
A practical conversation for anyone working out how to get real value from data and AI, without losing sight of the people involved.

What does successful AI adoption look like inside a business?
That's the question we're setting out to answer in AI in Action.
We'll be speaking to senior leaders about the reality of adopting AI at scale, from experimentation and governance to business value and leadership.
Want to know what's working, what's challenging them, and where they see AI heading next? Listen now. 
Rockborne bridge the talent gap to bring together skilled data consultants with the companies where they can make an impact. We started Rockborne to change who gets to build the future of data and to prove there’s a better way to grow capability. We saw talented people being overlooked and teams finding it hard to grow capability, so we built a path that does both: open doors for diverse talent and support data leaders to build teams that last.

What does successful AI adoption look like inside a business?
That's the question we're setting out to answer in AI in Action.
We'll be speaking to senior leaders about the reality of adopting AI at scale, from experimentation and governance to business value and leadership.
Want to know what's working, what's challenging them, and where they see AI heading next? Listen now. 
Rockborne bridge the talent gap to bring together skilled data consultants with the companies where they can make an impact. We started Rockborne to change who gets to build the future of data and to prove there’s a better way to grow capability. We saw talented people being overlooked and teams finding it hard to grow capability, so we built a path that does both: open doors for diverse talent and support data leaders to build teams that last.