Peter Brown

‘I see a future where thousands of agents support our customers and colleagues.’

Part of the AI Leadership Index – a curated list of the most inspirational figures shaping the AI revolution.

Peter Brown is Head of Engineering for Smarter Journeys & Hive Lab at Lloyds Banking Group. Accountable for platforms including Agentic Operations, Complaints, Knowledge Management, Moments of Truth, and the Appointment Booking System.

Leading the adoption of cutting-edge technologies and AI solutions at scale, contributing significantly to Lloyds Banking Group’s £50M GenAI benefits realised in 2025.

Winner of IT Leader of the Year at the UK IT Awards 2025. Based in Manchester and passionate about putting people at the heart of technology while delivering meaningful outcomes for customers.

Please share a case study of how you have you innovated with AI over the last year

Peter has delivered three large-scale GenAI solutions in the past 18 months, benefiting 40,000 colleagues and 30 million customers. He implemented Athena, a GenAI knowledge-based system, and Merlin, an award-winning complaints solution that reduced resolution times from 19 to 5 days. Peter also implemented a system to convert millions of calls from speech to text via an in-house built solution (at scale).

Peter excels in bridging business and technical teams, fostering innovation, and leveraging data for decision-making. His recent 2025 achievements include GenAI cost savings of approximately £20M, a 60% reduction in customer journey times, and the promotion of diversity & inclusion across the Labs he leads.

Peter's ability to deliver at scale sets him apart as a leader in AI and data. If 2025 wasn't busy, Lloyds have put Peter in pole position to deliver an agentic operations solution across the consumer division, in a safe and responsible way. This is a new challenge for 2026 and beyond whereby Pete has mobilised a new Lab, looking to re-imagine the operations space plus deliver various AI solutions at scale.

What is the biggest challenge you have faced with AI innovation and how you have overcome it?

The three biggest challenges are pace, scale and responsible AI.

Pace refers to the speed of delivery in a large enterprise like Lloyds. While we are improving, traditional processes combined with new governance requirements can slow progress. Delivery is becoming more complex as new AI controls are introduced alongside legacy processes that still apply to traditional solutions. The way we build, and test AI products is fundamentally different from how we develop digital services today, and our frameworks continue to evolve.

Scale has always been a challenge in an organisation like Lloyds. Moving from a proven concept in a controlled environment to something that can serve 30 million customers or 30,000 colleagues is difficult. With AI, this challenge increases because delivery models and compute requirements can differ significantly from traditional solutions, creating additional infrastructure and performance considerations.

Responsible AI is also critical. AI is not always the right solution; sometimes simplifying a process or removing a feature is more effective. When AI is appropriate, selecting the right approach matters—an LLM is not always the answer. Once chosen, the solution must be delivered responsibly: traceable, auditable, explainable and resilient. Ultimately, progress depends on the organisation’s willingness to adopt AI. Without that foundation, innovation can stall before it begins.

How do you balance innovation with ethical safeguards?

Through balancing business value and delivery discipline Lloyds has built an enterprise GenAI capability called CorteX, which provides a unified and secure way for engineers, data scientists and business users to access and work with LLMs. It acts as an internal LBG gateway that all GenAI solutions should use, providing enterprise-wide guardrails.

We also introduce additional local guardrails tailored to the specific business problem, helping protect both customers and colleagues. These controls are embedded in our change delivery framework and increasingly integrated into our internal engineering pipelines.

Alongside this, we work closely with the business and feature teams to explore ideas, innovate and identify the solutions we want to scale into the future product roadmap.

It isn’t easy, but we are not prepared to deliver solutions without the right guardrails in place. We are also training colleagues across the bank to adopt this mindset so that responsible innovation becomes part of how everyone works.

How do you believe the artificial intelligence space will change over the next 12-24 months?

Firstly, I cannot predict what will happen in the next six months, let alone 24, but I see a world where thousands of agents support our customers and colleagues. From an engineering perspective, developers may have multiple agents generating code, with the Software Engineer providing oversight through PR reviews and ensuring the quality of output while reinforcing good practices.

I also expect a blurring of roles. Business Analysts may become highly skilled at prompting, while Software Engineers develop a stronger understanding of how LLMs operate. Not at the same depth as a Data Scientist, but enough to select the right solution for the right use case.

At the same time, the software delivery lifecycle will evolve, helping address some of the pace and governance challenges mentioned earlier. Finally, I expect the business itself to adapt to the speed of change and adopt AI solutions at scale, which will require curiosity and a shift in mindset.