AI & Big Data Expo 2026 marks the moment enterprises stop experimenting and start shipping.
The models have been selected. The use cases have been defined. And yet, most AI programmers stall at the same point, not because of the technology, but because the data powering it isn't ready, and the engineering teams building it can't move fast enough.
Getting AI into production requires two things most enterprises underestimate: data their agents can trust, and engineering teams that can build and ship at the speed AI demands. Xebia works on both.
At Xebia, we believe enterprise AI only delivers value when it's grounded in governed data and built with AI-native engineering discipline. That means migration-ready platforms, unified data foundations, and AI applied across the full software delivery lifecycle — not just in isolated tasks.
At AI & Big Data Expo, our experts will be on-site to discuss how organizations can:
- Build a governed, agent-ready data platform that replaces fragmented pipelines with a single source of truth
- Accelerate data migrations by up to 65% using purpose-built agentic pipelines
- AI deployed on enterprise processes
- Modernize legacy systems by mapping codebases and planning transformation based on what the code actually does
Whether you're building the foundation for your first AI agent or scaling an enterprise-wide programme, we look forward to meeting you at Booth #246 at AI & Big Data Expo 2026.
Join Us On Stage
Real-World ROI — How to Measure the Impact of Intelligent Automation Projects.
📆 Monday, May 18 | 9:45am – 10:25am
As organizations continue to invest in intelligent automation, the focus is shifting from experimentation to measuring real business value. In this panel, a Xebia customer joins an esteemed group of industry leaders to explore how organizations can effectively quantify the impact of automation initiatives — from cost savings and efficiency gains to revenue growth and risk reduction.
Through real-world examples, the panel will unpack common challenges in tracking ROI and share practical frameworks, KPIs, and strategies to demonstrate measurable outcomes to stakeholders. Attendees will leave with actionable insights to better assess, justify, and optimize the value of their automation investments.
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Kevin Hearn
SVP, Head of Consumer Bank Development at Axos Bank with over two decades of experience across operations, technology, and enterprise transformation. Kevin drives scalable system design and leads cross-functional initiatives, turning complex operations into high-performing, execution-ready solutions.
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From Fragmented Data to Agent-Ready: How Xebia Helped an Enterprise Unify a Complex Data Estate and Build a Foundation for AI
📆 Tuesday, May 19 | 11:05am – 11:35am
Most organizations reach the same inflection point with AI: the models are selected, platforms are in place, and use cases are defined — but progress slows. The root cause is rarely the AI itself. It’s the data. Siloed, inconsistently defined, and lacking governance, data becomes a bottleneck. While humans can work around these gaps, AI agents cannot — they act on what’s available, making data quality and structure critical.
In this session, we’ll share how Xebia helped a large enterprise unify a complex, fragmented data landscape into a governed, scalable foundation for AI. By implementing a Medallion architecture, enforcing data contracts, and automating migration with built-in quality controls, we enabled faster, reliable data access without a full re-architecture. You’ll learn the key architectural decisions, delivery approach, and how combining AI agents with expert engineering accelerated a transformation that typically takes 12–24 months.
Speaker: Niels Zeilemaker
Global CTO at Xebia with broad experience across startups and global enterprises. Niels defines data tech stacks and drives architecture strategies, including data mesh implementations, to enable scalable and future-proof solutions.
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Book a Meeting
Meet our experts at Booth #246. Whether you're assessing data readiness, exploring AI-native engineering, or ready to build — we'd love to connect.