Membres
APGAR unveils its point of view: AI Needs Context, Not Just Data
AI agents need more than data. Discover how Enterprise Context Management makes business context trusted and reusable.
An AI agent may have access to your enterprise data. But can it understand which information to trust, how it is connected and what it means for the business?
As organizations move from analytics dashboards to AI agents capable of interpreting information and taking action, business knowledge can no longer remain implicit. People may know which customer record to trust, which hierarchy to apply or which business rule matters. AI agents need that same understanding to be made explicit, governed and reusable. Without clear meaning, trusted relationships and governance, enterprise data cannot be interpreted consistently. This context gap can limit the reliability and scalability of AI initiatives.
From Context Intelligence to Enterprise Context Management
This challenge was recently explored during an APGAR On Air conversation between Kash Mehdi, VP and Field CTO at Reltio, and Alaa Hoblos, CEO and Co-Founder of APGAR.
The discussion highlighted why Context Intelligence is becoming essential as organizations move from AI experimentation towards agentic AI. It also underlined an important reality: most companies are not starting from zero. Many have already invested in data platforms, Master Data Management, metadata and governance. The next challenge is to connect and evolve these foundations so that they can support not only human decision-making, but also AI-driven action.
APGAR’s Point of View
In “AI Needs Context, Not Just Data,” Frédéric Robert, Innovation and Data Advisory Director at APGAR, presents APGAR’s vision of Enterprise Context Management as a strategic capability for making business context trusted, reusable and machine-ready. Enterprise Context Management defines, governs, connects, translates and activates business context so that people, analytics products, AI systems and autonomous agents can interpret and use information consistently.
The objective is not to create another repository, catalog or technology layer. It is to transform existing business knowledge, including definitions, rules, relationships, policies and governance decisions, into context that can be understood and activated by machines.
Building on existing data foundations
Enterprise Context Management does not replace Data Management, MDM, metadata management, governance or DataOps. It builds upon these capabilities to make business meaning reusable and operational across AI, analytics and business processes.
Organizations with strong data foundations therefore have a valuable starting point. However, trusted data alone does not automatically provide AI agents with the context required to understand how the business works.
APGAR’s Point of View introduces a structured Enterprise Context Layer and a context maturity model to help organizations assess whether their business context is governed, reusable and ready to support AI and agentic workflows.
Is your enterprise context ready for AI?
Giving AI access to enterprise data is only the beginning. To operate reliably, AI agents need trusted context: a consistent understanding of business entities, relationships, rules and knowledge. Organizations must now determine whether this context is sufficiently governed, connected and reusable to support AI at scale.
APGAR’s Point of View explores: why AI readiness is fundamentally a context challenge; how business knowledge can be translated into machine-ready context; how existing data management capabilities contribute to an Enterprise Context Layer; how organizations can assess and strengthen the maturity of their context.
Read the Point of View to explore APGAR’s Enterprise Context Layer, understand the different maturity stages of enterprise context and identify where weak context may be limiting your AI and analytics initiatives.
