AI, Agents, and Analytics: Key Highlights from the Business Intelligence and Analytics Summit

The ConfX Business Intelligence and Analytics Summit successfully concluded in London, bringing together industry leaders, executives, and analytics professionals to explore the next phase of enterprise data strategy. The event, chaired & moderated by Saloni Thanki of Parinama Tech & Lan Nguyen of LinkedIn, moved beyond the initial hype of AI to focus on the practical challenges of building scalable, secure, and valuable AI-powered systems.

The summit's sessions centred on key pillars of building foundational trust through governance, embedding a human-centric data culture, and preparing for the next frontier of "Agentic AI."

1. Building the Foundation: Governance, Trust, and Semantics

A primary focus of the summit was the critical infrastructure required for mature AI adoption. Speakers emphasised that governance and trust are not optional add-ons, but the very foundation of scalability.

Elena Uzhegova of Publicis Media delivered a clear call for organisation-wide accountability, noting that in the new landscape, "Privacy is not the job of only one team. It's a job for everyone in the company." This need for a comprehensive trust framework was echoed by Chen Salomon of Monday.com, who emphasised the necessity of "activity logging and visible reasoning"—AI must be able to transparently explain how and why it arrived at a decision.

Beyond logging, Masood Alam of the Scottish Government argued that AI models often fail because they lack context. He detailed the solution: a semantic layer, or Knowledge Graph, that maps data relationships. This allows AI to understand nuances, such as knowing that doctors might record "cardiac arrest" when a user asks about "heart disease."

Highlighting the practical need for transparency, Deepak Damodarr (Neos Networks) shared compelling case studies on data lineage (the "map") and traceability (the detailed "logbook"). He demonstrated how these tools enabled rapid diagnosis of critical errors, like an incorrect data join causing a 127% churn prediction, proving essential for managing complex AI pipelines.

2. The Human Element: Strategy, Culture, and Storytelling

If governance is the "how," the summit's speakers argued the "why" remains fundamentally human. Technology is secondary to the strategy and culture required to wield it effectively.

Subhadip Roy of ERAM Systems stressed that AI implementation is primarily a "business transformation," not a technology one. He urged leaders to start with "what is our business problem?" rather than "how do we use AI?"

This human-centric view was central to a fireside chat with Manou Campbell (MediaK8) and Kalina Tomova (WomenWise), who argued that data teams must evolve from reactive "service desks" to become strategic partners to foster a true data-driven culture.

Ultimately, data requires effective communication. As Julian Hoffmann Anton, a geospatial & visualisation consultant, reminded the audience, "No one ever made a decision because of a number. They need a story." This need for persuasive storytelling was echoed by Lan Nguyen of LinkedIn, who discussed using sophisticated analytics like Marketing Mix Modelling (MMM) and Incrementality Testing to build executive confidence in marketing ROI, overcoming inherent scepticism.

Empowering the broader workforce was also key. Speakers like Peter Rogan (Multiverse) and Mukul Madhav (DHSC) discussed the rise of "citizen data scientists"—domain experts equipped with low-code tools—as crucial for scaling analytics across the organisation.

3. The Next Frontier: Agentic AI

The summit concluded with a clear vision of what's next: the shift from generative AI (chatbots that respond) to agentic AI (autonomous agents that act).

Christian Schroder of Bain & Company explained that this evolution will move companies from isolated "use case implementation" to "AI-driven end-to-end process redesign." He described scenarios where agents handle complex workflows like loan processing autonomously, leaving only key decisions to humans.

Jayna Devani of OpenAI provided a live glimpse of this future, demonstrating an agent tasked to autonomously research the "biotech funding landscape." The agent browsed multiple sources, analysed data, and compiled a detailed report, showcasing a significant leap in potential productivity.

The final message synthesised all sessions: the future involves powerful Agentic AI. However, realising its potential hinges on mastering the foundational elements of governance, semantics, data traceability, and human-centric strategy. The focus now shifts from mere adoption to building robust, trustworthy systems ready for the complexities ahead. Stay tuned for details on the next edition of the Business Intelligence and Analytics Summit! See the summit in action: Browse our photo gallery below for highlights from the event!