AI Success Starts with AI-Ready Data

Since 2022, generative and agentic AI (GenAI, Agentic AI) have moved from research labs into everyday business. These technologies are designed for everyone in the enterprise without requiring deep technical skills or lengthy onboarding, which makes them powerful accelerators of productivity and automation. Their broad accessibility also introduces new risks.

This keynote highlights two critical areas that have become especially important as AI adoption accelerates:

1. Data Access: The Universal Semantic Layer

AI agents are highly capable. Without proper control, they can autonomously access, link, and move data across the enterprise, automatically and without human oversight. This creates tangible risks of data leakage, regulatory breaches, and unexpected costs.

The answer is a universal semantic layer: a controlled environment that centrally governs meaning, access, policies, and reuse of data objects for people, applications, and agents. It should support modeling and analytics with built-in AI capabilities while acting as a firewall that ensures AI agents only access what is permitted, traceable, and compliant.

Beyond risk mitigation, the universal semantic layer addresses the complexity of existing BI infrastructures. Large enterprises typically run multiple data sources and several BI tools, each with its own limited semantic layer. This leads to duplicated business logic and inconsistent definitions. Adding GenAI access multiplies entry points and increases chaos. A universal semantic layer unifies business models and access across tools, allowing organizations to retain and extend current BI investments at significantly lower cost.

2. Data Location: Data Repatriation

AI requires powerful servers, and enterprises must ensure secure access and compliance. With rising cloud costs, fluctuating capacity at hyperscalers, and tightening regulations around privacy and security, many organizations are moving workloads back on-premises or to local providers. This trend, known as data repatriation, aims to balance cloud flexibility with the control provided by private infrastructure.

This keynote sets the stage for the day: trusted AI outcomes require not only powerful algorithms but also a robust data architecture built on semantic consistency, sovereignty, and governance.

Learn how Strategy Mosaic delivers a Universal Intelligence Layer that unifies data from multiple sources into a single semantic governance framework, ensuring a single version of truth for enterprise analytics.

The session will showcase:

• AI-Optimized Data Access: secure, tool-agnostic access to both structured and unstructured data.
• Mosaic Studio: AI-powered modeling that builds rich semantic models in minutes.
• Flexibility & Scalability: seamless movement across databases, BI tools, and cloud providers without costly transformations.
• Enhanced Decision-Making: consistent, real-time insights that eliminate silos and conflicting metrics.

A live demo will highlight how Strategy Mosaic simplifies data preparation and empowers faster, more reliable decisions across the enterprise.

AI initiatives require a data foundation that can keep up with speed and complexity. Exasol’s analytical engine provides the performance backbone for an AI-ready architecture, processing large data volumes at in-memory speed while maintaining governance, consistency, and predictable cost structures.

In this session, you will see how Exasol enables:

• High-Performance AI Analytics: accelerate complex queries and models with in-memory technology optimized for demanding AI workloads.
• Mission-Critical Scalability: scale across hybrid or on-premise environments without compromising speed or control.
• AI-Ready Data Architecture: provide a stable foundation to run advanced analytics and machine learning securely and reliably.
• Cost Optimization: reduce cloud dependency and maintain predictable performance at lower operational costs.

Exasol’s approach shows why AI success is not just about smarter models but also about smarter data architecture. This session highlights how enterprises can strengthen their analytical backbone to make AI initiatives faster and more trustworthy.

In this session, HPE Ezmeral Data Fabric together with HPE GreenLake will demonstrate how modern architectures enable enterprises to unify data management, secure workloads, and scale resources on-premises and in the cloud. Participants will learn how to establish governance and flexibility across the entire lifecycle of data, from edge to hybrid environments.

• Unified Data Management: integrate and control data across sources in hybrid and multi-cloud landscapes.
• Governance at Scale: ensure security, access policies, and auditability for enterprise-wide data.
• Edge-to-Cloud Flexibility: handle data from IoT, operational systems, and cloud-native applications in one governed framework.
• On-Premise Scaling Enabler: leverage HPE GreenLake and Ezmeral to dynamically scale resources while retaining control over sensitive workloads.
• AI-Ready Foundations: deliver governed, secure, and scalable pipelines that feed advanced analytics and AI models.

This session will show why governance and control at the data lake level are essential for trusted AI outcomes and how Ezmeral with GreenLake enables enterprises to achieve both flexibility and compliance.