How long will it be until an AI first company dominates your industry? Could that disrupter be your business? 

In this keynote, Gareth Martin (CEO of Manuka AI) and Maria Bines (CEO and co-founder of SynapseDX) will challenge comfortable assumptions about how AI should be used. Maria will share how she turned AI agents into a working scrum team — complete with job descriptions, an org chart, and the messy lessons of managing digital workers who don’t behave like humans. Gareth will explore why today’s obsession with “use cases” is a band-aid, and why process-driven adoption is the only way to avoid being disrupted — or irrelevant. 

This session isn’t about safe platitudes. It’s about what’s really happening in the wild, what technical professionals need to prepare for, and why the future of AI could look like seamless orchestration… or a chaotic swarm.

As organizations increasingly adopt AI and data-driven strategies, ensuring quality and reliability across the entire data + AI estate has never been more critical. This session will explore 2026 as the year of Data + AI Observability, highlighting key trends driving this transformation. Attendees will gain insights into how observability bridges the gap between data and AI systems across your data, system, code, and models, enabling more trustworthy, scalable, and efficient operations. Join us to learn practical approaches and tools that can future-proof your data and AI initiatives to drive real business impact.

This talk explores the disconnect between MLOps fundamental principles and their practical application in designing ML Pipelines.

This talk explores the disconnect between MLOps fundamental principles and their practical application in designing, operating and maintaining machine learning pipelines. We’ll break down these principles, examine their influence on pipeline architecture, and conclude with a straightforward, vendor-agnostic mind-map, offering a roadmap to build resilient MLOps systems for any project or technology stack. Despite the surge in tools and platforms, many teams still struggle with the same underlying issues: brittle data dependencies, poor observability, unclear ownership, and pipelines that silently break once deployed. Architecture alone isn't the answer; systems thinking is.

Topics covered include:

- Modular design: feature, training, inference

- Built-in observability, versioning, reuse

- Orchestration across batch, real-time, LLMs

- Platform-agnostic patterns that scale

Many but not every new AI tool has the potential to increase productivity. From a domain expert view, it is often a challenge to find out which tool supports, which one distracts and eventually how to take an objective decision where and how to apply these.

Is your analytics workflow stuck in fragmented chaos? AlphaSights, the global leader in expert knowledge on demand, used to juggle queries, scripts, spreadsheets, and dashboards across different tools just to get one analysis out the door. Manual updates slowed their teams, stakeholders waited too long for insights, and opportunities slipped through the cracks.With Hex, AlphaSights built a fully integrated Research Hub that unifies data queries, API calls, ML-powered enrichment, and reporting — all in one place. They eliminated manual work, automated updates, and empowered business teams to act faster on opportunities.The result: faster reaction times, broader coverage, and measurable commercial impact. Join this session to see how AlphaSights turned fragmented workflows into a seamless, automated pipeline — and learn how your team can build faster, smarter insights too.

Data platform migrations and modernisations take 18 months. 80% fail or are late. Teams waste 35% of their time rediscovering tribal knowledge. 'Vibe coding' causes 2000% compute spikes. Generic AI tools generate code without understanding your business logic, creating production disasters.

Fortune 500 companies are escaping this $280B crisis. One retail giant cut their SAP-to-Fabric migration from 18 to 6 months. Another achieved 85% error reduction in procurement analytics.

This session reveals how global enterprises capture organisational knowledge permanently, automate the entire data product lifecycle, and deliver production-ready analytics 3x faster. Learn the AI-first approach that transforms months of manual work into weeks of automated delivery.

Ready to move beyond passive data cataloging and unlock true AI-driven value? Join us for an in-depth session on data.world, now fully integrated with ServiceNow’s Workflow Data Fabric. We’ll show how you can unify, govern, and activate your enterprise data—across cloud, hybrid, and on-prem environments—to fuel agentic AI and intelligent automation. See a live demo of data.world’s knowledge graph in action: discover how to connect and contextualize data from any source, automate governance and compliance, and deliver trusted, explainable insights at scale. We’ll walk through real-world use cases, from rapid data discovery to automated policy enforcement and lineage tracking, and show how organizations are accelerating time-to-value and reducing risk. Whether you’re a data leader, architect, or practitioner, you’ll leave with practical strategies and a clear vision for making your data estate truly AI-ready. 

Agentic AI is developing fast. Agents are not just a tool; they are learning, adapting, and making decisions alongside us. Integrating Agents into teams is not just a technical challenge, it’s a cultural one. Teams need the space to experiment, the confidence to trust AI where it helps, and the right conditions to learn together. When adoption is thoughtful and inclusive, agentic AI can become a powerful extension of how teams think, decide, and deliver.

The future of healthcare depends not only on breakthroughs in science, but also on how we harness the power of data, technology, and AI. To realise this future, we must challenge long-held assumptions about how data products are delivered. What once took months of complex engineering now happens in days—or even hours—by re-imagining the way we work. At AstraZeneca, we shifted from a traditional IT-centric model to one where business teams take ownership, rapid prototyping drives innovation, and automation ensures quality, compliance, and trust.

 This change is more than a process improvement; it is a cultural transformation. By aligning every step to business value, embracing bold goals, and learning from failure, we have built a system that empowers people to innovate at speed and at scale. Data products are no longer the end goal but the enablers of something greater: a knowledge fabric ready for AI, where enterprise context unlocks smarter decisions and accelerates the delivery of life-changing medicines.

Our journey proves that when ambition meets courage, and technology meets purpose, we can transform the way data serves science—and, ultimately, transform the lives of patients around the world.

As populations age and healthcare systems strain under growing demand, AI is emerging as a vital force for innovation. From predictive health models to AI-powered caregiver assistants and conversational companions, data-driven tools are increasingly supporting elder care. But the rise of the “AI nurse” presents a profound challenge: How do we innovate responsibly while preserving human dignity and empathy?

In this session, Dr. Serena Huang explores the practical and ethical dimensions of applying AI in elder care. This talk bridges the gap between technical development and compassionate delivery, highlighting the critical role data professionals play in building trustworthy, equitable systems for vulnerable populations.

In this session, you will learn:

- How AI and predictive models can address workforce shortages and rising care needs in aging populations.

- How to design systems where AI handles data-heavy tasks while freeing up human caregivers for high-touch, empathetic care.

- Key principles for developing ethical, inclusive, and transparent AI systems that protect privacy and reduce bias.

We are the last generation to lead human-only organizations. The rise of agentic AI—autonomous systems capable of making decisions, learning independently, and collaborating with other agents—demands a profound shift in how we manage, govern, and grow our workforce. As enterprises accelerate AI adoption, we are entering uncharted territory where humans will no longer be the sole decision-makers, creators, or collaborators. This session explores the critical new skills and organizational capabilities needed to safely deploy, oversee, and scale hybrid human/agentic AI systems. We will examine how emerging regulations are reshaping expectations for transparency, explainability, and ethical alignment. But governance alone is not enough. Human teams must develop new roles—AI risk stewards, model behavior auditors, and cognitive ethicists—to ensure these agents operate without bias, hallucination, or unintended escalation. To meet this challenge, we must also define new career paths that grow the skills needed to lead hybrid teams and rethink early career roles that can serve as feeders into AI governance and oversight disciplines. In a future where machines continuously learn and evolve, leadership must be redefined for the age of intelligent agents.

We know that poor data quality leads to poor performance when it comes to training and serving AI models. But how much garbage is too much?

Stuck in endless AI conversations? Learn real-life tips to break through. Get materials to improve human-in-the-loop AI and automate pipelines that lead to decisions—not just dashboards.

Stuck in endless AI chats? Get real tips to break through, improve human-in-the-loop AI, and automate pipelines that drive decisions.

Whether you’re an ML engineer, data architect, or AI leader, get a proven framework to identify and overcome critical data challenges.

Most enterprise AI initiatives don’t fail because of bad models. They fail because of bad data. As organizations rush to integrate LLMs and advanced analytics into production, they often hit a roadblock: datasets that are messy, constantly evolving, and nearly impossible to manage at scale.

This session reveals why data is the Achilles’ heel of enterprise AI and how data version control can turn that weakness into a strength. You’ll learn how data version control transforms the way teams manage training datasets, track ML experiments, and ensure reproducibility across complex, distributed systems.

We’ll cover the fundamentals of data versioning, its role in modern enterprise AI architecture, and real-world examples of teams using it to build scalable, trustworthy AI systems. 

Whether you’re an ML engineer, data architect, or AI leader, this talk will help you identify critical data challenges before they stall your roadmap, and provide you with a proven framework to overcome them.

What the rest of the world can learn from gaming’s data-first approach to AI adoption.

Gaming pioneered many of the AI foundations we rely on today, from GPUs to reinforcement learning. It continues to drive innovation, but it has also built a strong resistance to hype. This session explores how gaming teams evaluate, deploy, and reject AI solutions with a discipline other industries can learn from.

Learn how Dell Technologies, NVIDIA, and Microsoft deliver secure, scalable AI solutions tailored to your strategic organisational objectives. Empower your workforce with agentic workflows to enhance productivity, simplify operations, and unlock intelligent business-driven automation. Accelerate real-world outcomes with flexible, secure, efficient, open, and extensible ecosystems.