Agentic AI for Data & AI Leaders

As AI becomes more embedded into business strategy, leaders are tackling the complexity of deploying AI agents that drive value without derailing product alignment, compromising security, or overcomplicating decisions.

AI and data have been one of the hottest topics of 2025, so we convened an exceptional group of C-Suite AI, Data & Engineering leaders for an afternoon of open discussion, challenge and idea-sharing.

Lina Mikolajczyk - Director of Data Science & Engineering @ Bumble
Mike Terrall - Head of Data Science @ BBC
Graeme Hitchman - Head of Data Science & Product @ Quantium
Russell Johnson - Chief Data and AI Officer
Simon Proffitt - MD of Tech & Applied AI @ Faculty AI
Dominic Watson - Business Development Manager @ Faculty AI
Nick Masca - Data & AI Leader @ Marks and Spencer
Guy Simon - Investment Director @ Livingbridge
Dima Zborovskiy – Science / ML Engineering Director @ Deliveroo
Leanne Pienaar – Data & Analytics Director @ Indurent
Alessio Ricco – Data Engineering Lead @ Sainsbury’s
Pedro Varela - Chief AI Officer @ BJSS 
Ted Truscott - Director of Product Analytics

The topics:

The buy vs. build dilemma: When to invest in off-the-shelf AI solutions vs. building in-house capabilities.

Driving business value: How to ensure AI agents deliver tangible impact while aligning with product vision.

Security & compliance: Navigating the evolving risk landscape in the age of AI agents.

Key takeaways:

💡Align AI with business strategy: Integrate AI into core objectives for executive buy-in and measurable outcomes. There was a big discussion around whether AI or business strategy should come first and they all agreed business strategy.

💡Focus on business impact: Measure ROI through revenue, conversion rates, and cost savings not just technical metrics relating to performance of engineers / scientists.

💡Build vs. Buy: Use APIs for quick wins, reserve in-house builds for strategic assets (e.g. proprietary data). This was something where there was no right outcome and agreed it will depend on the size and the capability of the Data org in place.

💡Encourage experimentation: Encourage small-scale pilots, balancing top-down strategy with bottom-up innovation. Not just to aid innovation but also to keep top talent happy, they want to be exploring and investigating.

💡Choose your champion: It became apparent that outside of Data & AI, the success of AI needed a sponsor, there was a lot of discussion with regards to who that champion should be and that should change depending on people / company but the consensus was that typically the CFO was the one to get on board.

💡Transforming models: Aim for radical shifts in decision-making, not just productivity gains. If you don’t know what this looks like, seek external examples and take external council.

💡Continuous learning: Stay curious and adapt quickly to rapid advancements in AI capabilities.

If you’re navigating these themes and would like to hear more about our leadership events, or attend one of our upcoming sessions, register your interest below, or reach out to us with more info here.