Adam Sroka

Every organisation is investing in AI, data platforms and analytics. Yet many of the decisions that determine commercial performance, operational resilience and competitive advantage are still fragmented across disconnected systems, spreadsheets and individual expertise. As businesses become more complex, the challenge shifts from accessing data to turning it into coordinated, repeatable decisions. In this interactive roundtable, Adam Sroka, CEO and Technical Co-Founder of Hypercube, will facilitate a discussion on what it takes to build Decision Intelligence into the enterprise. Together, participants will explore how organisations can connect data, AI and human expertise to support better decisions at every level of the business. The session will examine why many AI initiatives struggle to deliver lasting business value, how leading organisations are creating a shared decision layer across teams, and what practical steps leaders can take to embed AI into real operational workflows while maintaining governance, transparency and human oversight. Participants will leave with a clearer understanding of: Why Decision Intelligence is emerging as the next stage of enterprise AI adoption The organisational barriers preventing faster, more consistent decision-making How AI, data and domain expertise can work together to augment human judgement The role of agentic AI, orchestration and enterprise knowledge in supporting complex decisions Practical approaches to embedding Decision…

Forget polite panel discussions and canned PowerPoint slides. The Duel of the Data Experts is a high-stakes clash of ideas where you, the audience, hold the power. Six of the industry's sharpest thinkers will take the stage to debate the most pressing controversies in modern analytics. Operating under strict time limits, our debaters are split into two opposing camps: Team Optimist (visionaries betting on the AI-driven future) and Team Pragmatist (battle-hardened veterans who’ve lived enterprise software reality). They debate, YOU vote. Fast, unfiltered, and deeply provocative, this interactive session pits champion against challenger across three explosive thematic rounds: ROUND 1 — THE INTERFACE: AI Analytics means dashboards, and even data stories, are a thing of the past. Can conversational AI agents truly replace human-designed BI reporting, or are we abandoning visual clarity for hallucinated chaos? ROUND 2 — THE ARCHITECTURE: Modern semantic architecture finally makes 'Single Source of Truth' a reality. Do modern semantic layers finally solve corporate metric alignment, or are we just building the next unmanageable tech swamp? ROUND 3 — THE HUMAN: Data teams and analysts are the primary drivers of modern business value. Does AI elevate analysts into strategic powerhouse decision-makers, or are data teams dazed and drowning in an automated ticket queue? Expect sharp banter, zero corporate diplomacy, and direct audience voting to decide who wins each round. Whether…

Energy flexibility is playing an increasingly fundamental role in the UK energy market. With the adoption of renewable energy sources such as EVs, solar panels and domestic and commercial batteries, the number of flexible assets is soaring - making aggregation and flexibility trading infinitely more complex and requiring vast amounts of data modelling and forecasting. To address this challenge, Flexitricity adopted MLOps best practices to tackle this complex real-world challenge and meet the needs of the scaling energy demand in the UK. The session will cover: - The complex technical challenge of energy flexibility in 2025. - The critical requirement to invest in technology and skillsets. - A real-life view of how machine learning operations (MLOps) scaled Flexitricity’s data science model development. - How innovations in technology can support and optimise delivering on energy flexibility. The audience will gain insight into: - The challenge of building data science models to keep up with scaling demand. - How MLOps best practices can be adopted to drive efficiency and increase data science experiments to 10000+ per year. - Lessons learned from adopting MLOps pipelines.

Modern enterprises can’t manage data they don’t understand – uncovering the code-to-data relationship is the missing link. As data ecosystems grow more complex, traditional approaches to tracking data lineage can’t keep up. This talk explores how AI-driven code analysis can automatically build end-to-end lineage graphs, giving engineers clear visibility into hidden dependencies across large, legacy, and regulated systems. We’ll show how AI enhances data catalogues and introduce Gable - a tool that helps teams map, validate, and monitor data flows at scale. A live demo on a large energy data codebase will highlight how AI transforms lineage tracking from a manual headache into an automated, scalable solution.

As the energy sector transitions to new technologies and hardware, the data requirements are undergoing significant changes. At the same time, the markets in which energy systems operate are also evolving - giving traders and energy teams a vastly more complex set of options against which they need to make decisions.