The important management question is not whether a “context layer” is the next label for semantic technologies, but whether the enterprise is prepared to operationalise shared meaning at production scale. If AI systems are expected to reason across policies, processes, metadata, and live events, then context stops being a data-modeling exercise and becomes an architectural control plane.
For enterprise architects, that raises a hard design choice the source only hints at: should context be concentrated inside a major platform ecosystem, or treated as a federated enterprise capability spanning data, applications, governance, and automation? A platform-centric route may accelerate delivery, but it can also embed business meaning inside one vendor’s assumptions about ontology, policy, and runtime orchestration. A federated approach offers more portability, but usually demands stronger metadata discipline, stewardship, and integration maturity than many organisations currently have.
The governance implications are equally significant. A living enterprise model that captures decisions, actions, and outcomes can improve automation and explainability, but it also creates a new failure mode: institutionalising bad logic at scale. If business definitions, policy mappings, or process relationships are wrong, AI systems will not merely retrieve incorrect information; they may reason incorrectly in a governed-looking way. That makes provenance, versioning, ownership, and change control core requirements rather than administrative extras.
Before investing, IT leaders should test three things: whether critical business concepts already have agreed definitions, whether policy and process knowledge can be maintained as operating assets, and whether runtime context can be captured without excessive architectural coupling. The winners here will be the organisations that treat context as managed enterprise infrastructure, not just as AI enrichment.
The promise of neuro-symbolic AI – which combines neural network pattern recognition with rule-based reasoning – will only be possible when underpinned by trusted, governed business context. Context has become a buzzword; with terms like semantics, ontology, semantic layer, knowledge graph, and context layer being used interchangeably. Enterprises need a clearer definition of what they are trying to build. After months of vendor and enterprise practitioner interviews, Forrester has formed this proposed definition:
A context layer is the next evolution of semantic layers and knowledge graphs, providing the foundation for neuro-symbolic AI context engineering and agentic AI applications. It combines business semantics and governance of semantic layers with the ontological modeling of knowledge graphs. The context layer represents all enterprise knowledge across data, metadata, business concepts, policies and processes through graph-based ontologies and linked context. In addition, it continuously incorporates runtime context such as events, decisions, actions, and outcomes, creating a living model of the enterprise that enables AI reasoning, automation, and decision intelligence.
Coverage of Context Layer Platforms
Forrester has a well-developed view of how to evaluate semantic layer platforms. We recently initiated coverage of this market with a Forrester Landscape, planned for publication at the end of Q4 2026, which will be followed by a Forrester Wave.
Context layer platforms will be harder to compare because the market does not yet map to a single architectural pattern. The vendors Forrester recently interviewed address very similar use cases but come from different technology heritages and base their platforms on different assumptions about data, metadata, semantics, governance, and AI runtime context. We currently see the market forming around two subcategories:
- Platform- and business-application-focused context layer capabilities from business application providers and hyperscalers
- General-purpose context layer capabilities within platforms coming from graph database, data catalog, metadata, semantic layer, and data lakehouse platform vendors
We would welcome your perspective. Please comment on social media, schedule a briefing, or request a research inquiry with me or Indranil.
Enjoyed this article? Sign up for our newsletter to receive regular insights and stay connected.