Why Your AI Isn’t Scaling and How a Context Layer Fixes That
Discover how your AI agents can deliver reliable outcomes and business value at enterprise scale.
Across many sectors, organizations are increasing AI investment faster than AI outcomes are materializing. Gartner® predicts over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value or inadequate risk controls. For Data and IT leaders, the constraint is rarely the model alone. It is whether the underlying data foundation is trusted, connected, governed, and ready to support AI at enterprise scale.
Agents cannot deliver reliable outcomes when they reason from fragmented, unresolved records rather than a connected view of the business. Without that foundational readiness, organizations struggle to achieve business impact at scale from AI efforts. Real-World Context closes the gap by giving agents a trusted, governed understanding of how people, accounts, and transactions relate to one another, so they can reason from reality rather than isolated records.
Fill out the form to see how to build Real-World Context into your existing stack and deliver it through reusable data products.
In the guide, you’ll learn how to:
- Recognize the four data gaps that keep enterprise AI from scaling: fragmented data, inaccurate data, unresolved entities, and a lack of Real-World Context.
- Understand how entity resolution, knowledge graphs, and a semantic layer combine to build the context layer that sits above your existing data platform.
- Use data products as the primary mechanism to make trusted data and Real-World Context accessible and reusable across the organization.
- Apply a three-part framework, covering infrastructure, ownership, and standardization, to assess where your own data foundation stands today.
Download now to identify the data gaps holding your AI initiatives back and build the trusted, reusable context layer needed to move from isolated use cases to enterprise scale.
