---
title: There Won't Be a Single Context Layer
description: AI context now lives at every layer of the data stack. EMA’s Herb Blecher explains why there won’t be a single context layer, and what should hold it together.
image: https://blog.enterprisemanagement.com/hubfs/there-wont-be-a-single-context-layer.jpg
---

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# EMA Research Blog: Navigating IT and Security Horizons

## [There Won't Be a Single Context Layer](https://blog.enterprisemanagement.com/there-wont-be-a-single-context-layer)

 Oct 21, 2026, 9:27:19 AM

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The industry has talked a lot about what context is and why it’s important, but not as much about where it should sit. Now, across IT, it’s appearing at every step, from the gateway to the data platform to consumption. Context is both a standalone product and a feature.

And it's not hype.

In EMA’s September 2026 Data and Analytics Research, 61% of organizations reported that missing, incomplete, or inconsistent context has caused an AI system to misinterpret data at least occasionally.

Context needs to be available everywhere we allow AI, which is increasingly…everywhere.

So what does that mean for how enterprises apply it? If an analytics tool has a context feature, does that mean time or money needs to be spent on context at the platform or gateway layer? Is an independent context product necessary? The answer starts with the problem you're trying to solve. \[See [*Skip the Definitions: What Problem Are You Trying to Solve*](https://blog.enterprisemanagement.com/skip-the-definitions.-what-problem-are-you-trying-to-solve)*\]*

My water gets treated before it reaches my house, and I run it through another filter before I drink it. The treatment at the plant and the treatment at my tap do different things. Water for irrigation doesn't need the same treatment as water for drinking.

The same idea applies to context. Different points in the data stack contribute and consume different information in different ways. The challenge is to make those contributions work together without serving non-potable water to guests or sprinkling the lawn with purified water.

#### Context now includes control

We've traditionally (i.e., six months ago) thought about context as the information AI needs to understand enterprise data: definitions, relationships, lineage, and business meaning. As AI moves from answering questions to taking actions, context increasingly needs to include who's asking, what they're trying to accomplish, and what they're authorized to do. The context needed to answer a question may be different from what's needed to act on the answer.

Our research shows the gap between priorities and practice. Sixty-four percent of organizations consider purpose and 62% consider identity when deciding whether an AI system or agent may access enterprise data. Yet only 33% evaluate policy at runtime on every request. Context has to carry enough information for controls to make the right decision when the request occurs, while enforcement still has to happen at the appropriate point.

#### The pieces already exist

We don’t need to invent the concepts required to solve this. Formal categorization predates computers by a few thousand years. Many of the underlying technologies aren't new either. Ontologies, semantic models, knowledge graphs, catalogs, metadata systems, policy engines, lineage systems, and business glossaries all solve pieces of the problem. And there's no shortage of acronym-salad standards and open specifications: SKOS and OWL for semantics and ontologies, Cedar and ODRL for authorization and control, Fides for privacy and purpose, and newer initiatives such as Open Semantic Interchange (OSI) for sharing business meaning.

The issue isn’t a lack of components. It’s how we assemble and reconcile them well enough for an AI system to use the right data, from the right place, with the right meaning, in the right way for the task at hand.

No single mechanism can carry all enterprise meaning. Some context should exist ahead of time. Some should be retrieved when needed. Some should come from people. Some can be derived. Some should be governed centrally. Some belongs close to the application.

In our research, organizations that combine context-assembly methods report frequent context failures at 11%, compared with 31% among organizations relying on a single method. They also report material incorrect actions at 20%, compared with 48%.

But the layers may disagree. Making context available everywhere is of limited use if each system applies a different answer to the same question. That makes authority as important as distribution. Context needs to be available at many points without every point independently maintaining its own version of enterprise meaning.

#### What holds it together?

If context exists at every step, the question becomes how information passes between those steps. Open standards help, but enterprises still need to determine where definitions and policies are authoritative, how context is assembled and refined, and how conflicts get resolved.

There is a market consequence as well. Context will tend to accumulate where data and work already live, giving established vendors an advantage. Independent context vendors need a clear answer to what they provide across those platforms that embedded capabilities do not.

For enterprises, the priority is to avoid building competing sources of truth simply because context features are everywhere. Every tap can add a filter. None of them should redefine what's safe to drink.

I'll be exploring how enterprises assign that authority, and keep meaning, identity, and purpose intact from the gateway to the point of use, in upcoming EMA research, *Engineering Knowledge for AI: Defining and Unifying Business Meaning.*

 Topics: [Semantic layer](https://blog.enterprisemanagement.com/topic/semantic-layer) [ai context layer](https://blog.enterprisemanagement.com/topic/ai-context-layer) [enterprise AI governance](https://blog.enterprisemanagement.com/topic/enterprise-ai-governance) [AI data context](https://blog.enterprisemanagement.com/topic/ai-data-context)

![Herb Blecher](https://blog.enterprisemanagement.com/hs-fs/hubfs/ppt-herb-blecher.jpg?width=100&height=100&name=ppt-herb-blecher.jpg)

#### Written by [Herb Blecher](https://blog.enterprisemanagement.com/author/herb-blecher)

Herb leads the Data and Analytics practice at Enterprise Management Associates (EMA). His research examines how enterprises architect, govern, and operationalize data platforms to drive business outcomes, with particular focus on AI/ML platforms, data observability, analytics modernization, and data integration. Herb has been a data and analytics practitioner in financial services and data product development for more than two decades. He directed national data programs for the U.S. Treasury and Office of the Comptroller of the Currency, built and managed large-scale data platforms covering 80% of U.S. mortgages, and developed analyses relied on by federal regulators and cited in the Wall Street Journal, Bloomberg, and CNBC.

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- [network data (1)](https://blog.enterprisemanagement.com/tag/network-data)
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- [open networking (1)](https://blog.enterprisemanagement.com/tag/open-networking)
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- [optics (1)](https://blog.enterprisemanagement.com/tag/optics)
- [ownCloud (1)](https://blog.enterprisemanagement.com/tag/owncloud)
- [passwordless (1)](https://blog.enterprisemanagement.com/tag/passwordless)
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- [process mining (1)](https://blog.enterprisemanagement.com/tag/process-mining)
- [radar (1)](https://blog.enterprisemanagement.com/tag/radar)
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- [routers (1)](https://blog.enterprisemanagement.com/tag/routers)
- [rsac 2022 (1)](https://blog.enterprisemanagement.com/tag/rsac-2022)
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- [spear phishing (1)](https://blog.enterprisemanagement.com/tag/spear-phishing)
- [splunkconf24 (1)](https://blog.enterprisemanagement.com/tag/splunkconf24)
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- [tariffs (1)](https://blog.enterprisemanagement.com/tag/tariffs)
- [techatthegap (1)](https://blog.enterprisemanagement.com/tag/techatthegap)
- [techlayoffs (1)](https://blog.enterprisemanagement.com/tag/techlayoffs)
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- [threat detection (1)](https://blog.enterprisemanagement.com/tag/threat-detection)
- [threat detection solutions (1)](https://blog.enterprisemanagement.com/tag/threat-detection-solutions)
- [trends (1)](https://blog.enterprisemanagement.com/tag/trends)
- [vendor to watch (1)](https://blog.enterprisemanagement.com/tag/vendor-to-watch)
- [vibe coding (1)](https://blog.enterprisemanagement.com/tag/vibe-coding)
- [video conferencing (1)](https://blog.enterprisemanagement.com/tag/video-conferencing)
- [virtualization (1)](https://blog.enterprisemanagement.com/tag/virtualization)
- [wide-area networking (1)](https://blog.enterprisemanagement.com/tag/wide-area-networking)
- [workforce mobility (1)](https://blog.enterprisemanagement.com/tag/workforce-mobility)
- [xdr (1)](https://blog.enterprisemanagement.com/tag/xdr)
- [zero day (1)](https://blog.enterprisemanagement.com/tag/zero-day)

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