About this session

Data is the backbone of AI innovation, but is your strategy keeping pace? As AI capabilities evolve, simply having algorithms isn't enough. Organizations need a robust, scalable data architecture that delivers real-time insights and tangible business value. Yet, many tech leaders are grappling with fragmented data ecosystems, integration challenges, and the pressure to make data-driven innovation both efficient and sustainable.

This group discussion is not about being handed the answers, it is about bringing your experience to the table, challenging assumptions, and exchanging practical strategies with other senior tech leaders from across Europe. Join us for a dynamic dialogue where we will unpack the building blocks of an AI-ready data strategy, discuss what’s working, and debate the future of data-driven decision-making.

We highly encourage you to prepare any questions you may have on this topic beforehand and use this forum as a valuable opportunity to learn how others in similar positions at different companies approach this topic.

Key Discussion Themes:

1. AI-Ready Data Architecture: Building for Scale & Speed

• What are the essential principles for building a data architecture that supports AI-driven analytics and automation?
• How do you balance agility, governance, and security in an environment that demands both speed and precision?
• What architectural models have proven effective in real-world implementations?

2. From Data Silos to AI-Driven Insights

• How can organizations dismantle data silos and create a unified, AI-ready data ecosystem?
• What are the most effective strategies for data integration, real-time processing, and governance?
• How do you foster collaboration between IT and business units to drive unified data strategies?

3. Monetizing AI & Data: From Strategy to Execution

• Moving beyond experimentation—how can organizations operationalize AI to deliver measurable business outcomes?
• What KPIs and success metrics should leaders track to ensure ROI from AI initiatives?
• How do you identify and prioritize AI use cases that align with your organization’s long-term goals?