Julie Nguyen

BP, a global energy company, operates complex, asset-intensive environments where maintenance, reliability, safety and logistics decisions must be made quickly and with confidence. Across refineries and sites, teams rely on data from dozens of systems, including SAP and multiple operational tools, to plan maintenance work, manage materials and ensure equipment safety. Before Databricks AI/BI Genie, accessing this information required deep system knowledge, manual coordination across teams, long request queues and weeks of engineering effort to produce custom reports. With Genie, BP introduced a simple, natural language interface to its maintenance data, giving a large number of business users an intuitive way to access insights without bottlenecking on a small number of colleagues with deep technical expertise.

BP is a 117-year-old global energy company spanning upstream exploration and production, refining, trading, retail, and growing low-carbon businesses. Its data estate evolved across classic enterprise warehouse patterns and cloud stacks split by business unit—upstream on Azure and retail on AWS. Hadoop-era systems became too slow at scale, and Redshift-based serving was costly and hard to right-size. BP standardized on Databricks SQL as its warehouse and BI serving layer across clouds, using Unity Catalog and Delta Sharing to reuse governed data without copying it. The team reduced cloud cost 43% and accelerated key processing from days to ~8 hours, while powering Power BI and other tools through Databricks SQL Serverless warehouses.

Enterprises across industries are racing to modernize legacy data platforms while simultaneously preparing for the GenAI era. However, the path to modernization varies widely, depending on industry, operating model, governance requirements, and AI ambitions. In this cross-industry panel, leaders from multiple Fortune-level organizations share practical insights on modernizing with Infosys and Databricks to build AI-ready data platforms that deliver predictable, measurable value. Panelists will discuss proven techniques, lessons learnt, and what ultimately unlocked AI readiness at enterprise scale. This includes modernization catalysts, architectural decisions, migration patterns, automation strategies, governance uplift, and aligning transformation with business outcomes. Infosys will moderate the conversation using its Infosys DE.ai modernization factory framework, enabling panelists to reflect on common accelerators and industry-specific challenges.

Agentic AI has the power to revolutionize mission-critical domains. Yet this journey is not without its challenges – the inevitable barriers, frustrations, and setbacks that mark all progress. This session dives into how Infosys Topaz helps enterprises to strategically implement AI at scale in as little as two months to personalize customer journeys, optimize operations, and unlock new revenue streams. Learn how different enterprises have architected foundation capabilities such as Agentic AI factory to build & accommodate hundreds or even thousands of intelligent agents, setting up data fingerprinting and data harvesting to make enterprise Data ready for AI and ensuring interoperability among diverse AI systems