Data & Analytics

Chairpersons welcome and opening remarks

Most AI discussions focus on models, prompts, and applications. But the moment AI is expected to do more than just assist, when it has to execute entire processes, the (technical) foundation becomes decisive. This session shows why a clear vision of your future architecture, data, and integrations determines whether organizations stay stuck in isolated experiments or can genuinely scale up to reliable, scalable agentic AI.

The energy transition conversation needs to move from theory to reality, and Schneider Electric’s work in the field proves this is not only possible, but that the energy transition can and is already happening at the level of buildings, factories, and grid infrastructure. Philippe Rambach makes the case that AI’s most significant near-term impact isn’t in consumer applications, but in the unglamorous, high-stakes world of industrial energy management.

In this session, he shares how Schneider Electric is deploying AI across energy ecosystems from predictive maintenance in manufacturing to dynamic load balancing in smart buildings, and quantifies the efficiency gains already being realized. This talk offers a grounded view of where AI investment translates most directly into measurable energy and cost reduction, and what it takes to move from pilot to production at scale.

This panel brings together enterprise leaders to explore the harsh realities of AI deployment. What makes the difference between models that thrive in production and those that fail to gain traction?

• Lessons learned from failed deployments
• Strategies for long-term value delivery
• Cross-functional integration: tech, ops, and business alignment
• Governance and change management

AI tools like Claude are no longer just answering questions. They’re starting to take action: pulling data from your systems, updating records, even making decisions on your behalf. To do that, they connect to your systems through something called an MCP server, which is basically a bridge between the AI and your company’s tools.

Here’s the problem: most companies let that bridge run wide open, with no one checking who’s really on the other side or what they’re allowed to touch.

Think of it this way: If you hired a new employee, you wouldn’t hand them a master key to every system on day one. You’d set up their access properly, know exactly what they can do, and be able to check it later. AI agents deserve the same treatment. That’s where an identity provider comes in: it acts as the gatekeeper, making sure every AI agent’s access is deliberate, limited, and traceable.

In this session, we’ll show what that looks like in practice; how to keep AI agents safely connected to your business systems without giving away the keys to the kingdom.

This use case explores how IKEA is using GenAI-powered assistants and agents to enhance customer experience across digital and physical touchpoints while keeping human judgment at the center. From personalized product recommendations to post-purchase support, AI systems augment co-workers rather than replace them, helping customers navigate complex choices with confidence.

As conversational agents become more autonomous, anticipating needs, resolving issues, and optimizing interactions purposeful leadership ensures transparency, brand alignment, and trust. Human oversight remains critical in defining tone, escalation paths, and ethical boundaries.

Attendees will gain insight into how IKEA balances automation with AI into omnichannel journeys, and empowers co-workers to supervise, refine, and continuously improve AI-driven customer interactions.

Organizations have largely solved the deployment problem. They haven’t solved the alignment problem. Agents execute continuously against local context with no view of whether the aggregate activity is moving the organization toward its funded strategic goals. The result is strategic drift at AI speed – and the existing agenda of implementation, adoption, and ROI dashboards doesn’t address it. What the next generation of enterprise AI architecture needs – not more governance, but the decision intelligence to ensure agents are working on the right things.

Should you build your AI capabilities in-house or buy from a vendor? This panel dives deep into the decision-making process across various sectors.

• Off-the-shelf platforms vs in-house custom solutions
• Vendor lock-in, IP ownership, and long-term costs
• Perspectives from banking, healthcare, and telecom leaders

Organizations approve data & AI budgets worth millions. The costs are visible. The value is not. And the pressure to change that is mounting fast.

Recent Gartner research confirms what many data & AI leaders already feel in practice: fewer than 1 in 10 CFOs can concretely measure ROI from their AI investments, even though most organizations have already built the foundation. The problem is rarely technological. It’s operational: once value is planned, almost nobody has a structured way to prove it actually happened.

This session focuses on Value Management: the piece most organizations are still missing to validate that data & AI investments deliver what they promised.

You’ll learn how to:

· Establish the right foundation before value management can even begin

· Make value defensible enough to hold up in front of stakeholders

· Close the loop with controlling, so value is not just reported but attributed and reasonable

This session is for data & AI leaders who are ready to make value management as rigorous as cost management already is.

Prediction markets have become one of the hardest evaluation benchmarks for frontier AI labs. Forecasting political shifts, economic policy, geopolitical risk, and supply chain disruption is difficult because the underlying information for each question changes by the minute. This talk shares lessons learned at AskNews while building AI forecasting infrastructure for these markets. Attendees will leave understanding where AI forecasters still fail miserably and where they are now beating human superforecasters.

As cyber threats grow in scale and sophistication, traditional security approaches are no longer enough. This session explores how artificial intelligence is being used to strengthen cybersecurity enhancing threat detection, accelerating response times, and reducing human error. Attendees will gain a clear understanding of where AI delivers real value today, where risks and limitations remain, and how organizations can responsibly integrate AI into their security strategy.