Do Enterprises Need a Context Layer Between Data and AI?

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by Emily Winks, Data governance expert at Atlan.Last Updated on: January 28th, 2026 | 16 min read

Quick answer: Do enterprises need a context layer between data and AI?

A context layer transforms raw data into actionable intelligence by providing AI systems with the meaning, relationships, and business rules they need to make accurate decisions.

The need for a context layer between data and AI is becoming critical as enterprises scaling their AI operations struggle with the "AI context gap": where models lack organizational knowledge that humans intuitively possess.

Critical characteristics of effective context layers:

  • Bridges interpretation gap: Connects what data says to what it means for your specific business.
  • Reduces hallucinations: Context-aware systems can achieve greater AI accuracy versus those without proper context.
  • Enables governance at scale: Provides audit trails, access controls, and policy enforcement for millions of AI decisions daily.
  • Operates at inference speed: Retrieves relevant context in milliseconds to support real-time AI operations.
  • Evolves continuously: Captures human feedback and organizational changes to stay current with business reality.

Below: why enterprises face the AI context gap, how context layers work, key components and architecture, implementation strategies, measuring success, how modern platforms address context.

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Why are enterprises facing the AI context gap?

As AI operations scale, the fundamental challenge that enterprises face isn’t model intelligence, but organizational knowledge. Anthropic research points out that enterprises struggling to gather and operationalize contextual data will fail to deploy sophisticated AI systems.

AI systems trained on public data understand general concepts but lack three critical context layers:

  1. Structural context defines what terms mean in your organization (is “customer” someone who bought once or maintains an active subscription?).
  2. Operational context captures decision rules and procedures (when Sales calls an account “Mid-Market” but Finance categorizes them as “Enterprise,” which definition should AI use?).
  3. Behavioral context encodes patterns and historical lessons (why certain product configurations always require executive approval).

When this context is missing, three problems emerge.

  1. First, inconsistent definitions mean the same question yields different answers depending on which system AI queries.
  2. Second, missing business rules cause AI to suggest actions that violate unwritten organizational policies.
  3. Third, lack of relationship understanding prevents AI from connecting dots across disparate information sources that humans intuitively link.

Without this organizational context, AI behaves like new employees with no onboarding–capable and eager but fundamentally uninformed about how your business operates.

The consequences compound at scale. As teams build isolated context layers for individual AI use cases, enterprises recreate the data silos of the early 2000s. The difference now is impact. AI does not just analyze fragmented context, it acts on it, triggering workflows, making recommendations, and automating decisions.

As Forrester’s Indranil Bandyopadhyay puts it, “the rise of agentic AI is forcing enterprises to confront a hard truth: traditional data architectures weren’t built for this moment. Stitching together relational databases, warehouses, and lakes might have worked for yesterday’s analytics, but it’s a brittle foundation for AI systems that demand real-time, multimodal context.”

Real-time, multimodal context is only possible when enterprises stop treating context as scattered byproducts of individual systems and instead manage it as a unified, governed layer. This layer captures structural definitions, business rules, relationships, and unstructured knowledge just once, and keeps them continuously updated, and available at inference time across all AI agents and workflows.

How do context layers work as enterprise infrastructure?

A context layer functions as living infrastructure rather than another application layer. Think of how data warehouses defined business intelligence in the 1990s. The context layer will similarly define AI by becoming a shared foundation for every intelligent system.

Modern context architectures operate on four foundational components working together.

1. Context extraction and bootstrapping


Context layers connect across fragmented organizational landscapes—knowledge bases, CRMs, data warehouses, BI tools—to intelligently mine existing systems and create a bootstrapped version of collective organizational context.

This process infers rules from behavior by analyzing decision logs, communication patterns, and historical actions–scattered traces of human tribal knowledge. By combining data with domain-specific meaning, the context layer enables AI to think and act intelligently about complex business scenarios.

2. Context products as minimum viable units


Drawing inspiration from how data products became foundational in modern data stacks, context products serve as reusable, reproducible, governed units of contextual value.

A context product packages structured data representations with the shared understanding that gives that data meaning. They actively encode how your organization thinks about specific domains, complete with definitions, relationships, business rules, and decision frameworks.

Teams can compose complex AI capabilities by combining multiple context products rather than rebuilding context from scratch for each use case.

3. Human-in-the-loop feedback mechanisms


The strength of context layers lies in continuous improvement, rather than completeness. Every interaction between humans and AI agents refines organizational understanding through feedback loops.

When an AI provides an answer, a human adjusts it, and that correction becomes part of institutional memory. This simple mechanism—question, clarification, confirmation—builds increasingly precise and human-aligned systems over time.

4. Context store and retrieval architecture


The context store and retrieval architecture are two complementary systems that lay the foundation for a context layer.

The context store maintains information in forms both humans and machines can access, including specialized storage for different context types:

  • Graph databases for relationships
  • Vector stores for semantic search
  • Document stores for unstructured knowledge
  • Relational databases for structured rules

The retrieval layer serves as connective tissue making disparate stores feel like one unified system. This interface layer handles context retrieval, reasoning about which context applies, and governance enforcement.

Operating at inference speed—agents retrieving context millions of times daily, often in milliseconds—and interoperable across tools, this architecture ensures AI systems reason and act using the same shared context as humans.

What are the key components of production-grade context layers?

Enterprise context layers require five technical capabilities working together.

1. Domain knowledge graphs capture relationships


Knowledge graphs structure how entities connect across your business, moving beyond flat data representations to capture meaningful relationships. In financial services, this means understanding how risk assessment, customer onboarding, sanctions screening, and investigations relate—instead of treating each as an isolated silo.

Knowledge graphs explicitly document who reports to whom, which products belong to which categories, how processes flow across departments, and which metrics depend on which data sources.

This relationship mapping enables AI to perform multi-hop reasoning: connecting facts across several degrees of separation to synthesize new insights.

2. Semantic layers translate technical to business


Semantic layers map technical database structures to business concepts, ensuring AI understands organizational language. When an analyst requests “quarterly revenue by region,” the semantic layer automatically translates this into correct SQL joins across customer, transaction, and geography tables without requiring SQL expertise.

Organizations using an average of 3.8 different BI tools need unified semantic layers to prevent each tool from having its own metric definitions. The layer becomes a single source of truth for business logic that works everywhere—from dashboards to AI agents to executive reports.

3. Active metadata provides operational intelligence


Beyond static definitions, active metadata captures how data is actually used, who accesses it, what quality issues exist, and how assets relate through lineage. This operational intelligence helps AI understand not just what data exists but which data is trustworthy and relevant for specific decisions.

When a table’s schema changes or quality scores drop, active metadata automatically notifies downstream users and triggers appropriate workflows. So, context stays current everywhere teams work, turning metadata from passive record into living operational layer.

4. Governance and policy enforcement


Context layers must treat governance as the control plane for AI, defining how data, permissions, and policies flow into AI systems. This ensures outputs remain compliant, traceable, and auditable.

In private banking, an assistant recommending investment products must consider regulatory suitability rules, investor profiles, and portfolio objectives. The context layer provides this governed context, ensuring every recommendation is properly grounded within appropriate constraints.

5. Multi-modal integration and virtualization


Enterprise context spans structured databases, unstructured documents, images, audio, and video. Context layers must link these modalities together, enabling AI to connect and blend multiple data types.

A law enforcement system might need to connect a person tracked in CCTV footage with an audio clip exchange and an associated crime report document—something pure vector search struggles with but graphs handle naturally.

How to implement context layers: Build versus platform approaches

Organizations face a fundamental architecture decision: build isolated context stores for specific AI applications or invest in shared context infrastructure.

1. The isolated approach: Faster start, higher long-term cost


Building context stores per AI use case delivers quick initial wins. Teams can move fast, customize for specific needs, and avoid coordination overhead. This path works well for pilot projects and proof-of-concepts.

However, fragmentation costs compound at scale. Each team rebuilds a similar context, definitions drift across projects, AI systems give inconsistent answers to the same questions, and organizations struggle to enforce governance policies uniformly.

Maintenance burden also grows linearly with each new use case.

2. The infrastructure approach: Upfront investment, compounding returns


Treating context as shared infrastructure requires initial coordination and investment but delivers compounding benefits. Consistent definitions mean AI provides reliable answers regardless of access point. Centralized governance scales naturally. New use cases leverage existing context rather than starting from scratch.

This mirrors how organizations evolved from departmental databases to enterprise data warehouses. The integration effort paid dividends through consistency, governance, and reduced redundancy.

3. Hybrid strategies for pragmatic deployment


Many enterprises adopt phased approaches. Start with high-value, risk-averse domains like customer support or financial reporting where accuracy matters most. Build context layer foundations in these areas while proving business value. Gradually expand to additional domains, leveraging patterns and infrastructure already established.

Organizations achieve 94-99% AI accuracy with proper context engineering versus 10-20% with fragmented approaches. The performance gap justifies infrastructure investment for any organization deploying AI at production scale.

Key implementation considerations include selecting appropriate technology stack (graph databases, semantic layer platforms, metadata management tools), establishing governance processes for context quality, defining ownership models for context maintenance, and creating integration patterns that work across your specific tool ecosystem.

How can you measure ROI and success metrics for context layers?

Context layer investments require quantifiable returns to justify ongoing support.

  • AI accuracy and reliability improvements provide the clearest metric. Measure hallucination rates before and after context layer implementation, comparing answer accuracy across different AI use cases. Track confidence scores and monitor how often AI systems correctly handle edge cases versus making incorrect assumptions.
  • Operational efficiency gains manifest in reduced time to deploy new AI use cases (from months to weeks when context infrastructure exists), decreased effort resolving conflicting AI outputs, and lower maintenance burden.
  • Governance and compliance metrics include audit trail completeness (can you explain every AI decision?), policy violation reduction, and time to respond to regulatory inquiries.
  • Adoption and trust indicators track how many teams leverage shared context infrastructure, user satisfaction with AI-generated insights, and business stakeholder confidence in AI-driven decisions. When trust increases, AI usage naturally expands.
  • Cost avoidance measurements capture savings from preventing errors (like avoiding wrong decisions from hallucinated outputs), reducing redundant context development across teams, and minimizing regulatory penalties from ungoverned AI.

Set baseline measurements before implementation. Track improvements quarterly. Most organizations see meaningful impact within 6-12 months, with returns accelerating as more use cases leverage shared infrastructure.

How do modern platforms streamline context layer operations?

Building context infrastructure from scratch requires significant engineering investment. Modern data platforms have evolved to address this challenge by providing integrated context management as core capability rather than add-on feature.

Enterprise-grade solutions combine several key elements.

  • Automated context extraction discovers and catalogs organizational knowledge across systems without manual documentation overhead.
  • Active metadata management keeps context current as data and business logic evolve.
  • Built-in semantic layer support standardizes business definitions and metrics across consumption tools.
  • Native AI governance embeds policy enforcement, access controls, and audit trails directly into AI workflows.

Atlan’s approach treats context as foundational infrastructure with three integrated layers.

The unification layer provides automated connectors extracting context from 100+ data sources, column-level lineage tracking how information flows, and intelligent automation enriching metadata continuously.

The collaboration layer enables teams to create data products with embedded context, implement governance policies that scale, and establish quality frameworks ensuring AI works with trusted data.

The activation layer brings context directly into AI experiences through Model Context Protocol (MCP) integration with ChatGPT, Claude, and Cursor. These are native AI assistants that understand organizational context, and extensible app framework for building custom AI-powered workflows. This eliminates the gap between where context lives and where AI systems operate.

Organizations using integrated approaches report faster time-to-value, broader adoption across teams, and more consistent AI outcomes compared to building point solutions. The platform handles infrastructure complexity so teams can focus on applying AI to business problems rather than solving technical plumbing challenges.

See how context layer infrastructure accelerates enterprise AI deployment

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Real stories from real customers: Context layers driving AI success

How Workday is building context as culture to power trustworthy AI

“As part of Atlan’s AI Labs, we’re co-building the semantic layers that AI needs with new constructs like context products that can start with an end user’s prompt and include them in the development process. All of the work that we did to get to a shared language amongst people at Workday can be leveraged by AI via Atlan’s MCP server.” - Joe DosSantos, Vice President of Enterprise Data & Analytics, Workday

Learn how Workday turned context as culture

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How Mastercard is engineering context into the fabric of its data with Atlan

“When you’re working with AI, you need contextual data to interpret transactional data at the speed of transaction (within milliseconds). So we have moved from privacy by design to data by design to now context by design. We needed a tool that could scale with us. We chose Atlan, a platform that’s configurable, intuitive, and able to scale with our 100M+ data assets. Atlan’s metadata lakehouse is configurable across all tools and flexible enough to get us to a future state where we keep up with AI, unlock innovation responsibly.” - Andrew Reiskind, Chief Data Officer at Mastercard

Mastercard's building context from the start

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Ready to move forward with a unified context layer for your enterprise?

The question isn’t whether enterprises need context layers—it’s whether they can afford to deploy production AI without them. Context infrastructure bridges the gap between what AI models know and what your organization needs them to understand. As AI agents move from pilot projects to mission-critical operations, the context layer becomes as essential as the data warehouse was to business intelligence.

Start with high-value domains where accuracy matters most, build infrastructure that scales across use cases, and create governance frameworks that grow with your AI ambitions. Organizations that invest in context engineering now will build competitive advantages that compound as AI adoption accelerates.

Atlan’s unified context layer enables trusted AI deployment at enterprise scale.

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FAQs about do enterprises need a context layer between data and AI

1. What is a context layer for AI?


A context layer for AI is a unified, governed system that captures and delivers organizational knowledge to AI models and agents in real time. It provides shared definitions, business rules, relationships, and historical context across structured and unstructured data.

By ensuring consistent meaning and policy enforcement at inference time, a context layer enables AI systems to reason and act in alignment with how the business actually operates.

2. What’s the difference between a context layer and a semantic layer?


A semantic layer translates technical database structures into business concepts, defining metrics and relationships for consistent reporting across BI tools.

A context layer encompasses semantic definitions but extends further to include operational rules, behavioral patterns, access policies, and dynamic organizational knowledge.

Think of the semantic layer as one component within the broader context layer infrastructure. Both work together: semantic layers provide the “what” (business definitions), while context layers add the “how” and “why” (decision rules, relationships, constraints).

3. How do context layers reduce AI hallucinations?


Hallucinations occur when AI systems generate plausible but incorrect outputs based on incomplete or missing information. Context layers provide verified, relevant data that grounds AI responses in organizational truth rather than statistical patterns from training data.

By feeding AI the specific business rules, current system state, and relevant relationships for each query, context layers dramatically reduce the model’s tendency to fabricate answers. Organizations report achieving 94-99% accuracy with proper context grounding versus 10-31% without it.

4. Can we use vector databases instead of building a full context layer?


Vector databases solve part of the challenge by enabling semantic search across unstructured content, but they don’t capture structured relationships, business rules, or operational context.

A production context layer typically uses vector databases alongside graph databases (for relationships), semantic layers (for business logic), and active metadata management (for operational intelligence). Vector databases are one tool in the context layer toolkit, not a replacement for the entire infrastructure.

5. How long does it take to implement an enterprise context layer?


Implementation timelines vary based on organizational complexity and approach. Organizations using modern platforms with pre-built connectors and automation typically achieve initial deployment in 60-90 days for priority domains. Building custom context infrastructure from scratch can take 6-12 months.

The phased approach works best: start with one high-value domain, prove ROI, then expand to additional areas. Most organizations see meaningful impact within the first quarter and full production deployment within a year.

6. What happens to context layers as our data and business rules change?


As our data and business rules change, continuous update mechanisms become critical. The goal isn’t a static snapshot but a living system that evolves as your organization does, similar to how code repositories maintain currency through continuous integration.

That’s where modern context layers can help. They incorporate several capabilities, such as automated metadata extraction that syncs with source system changes, human-in-the-loop feedback that captures corrections and refinements, version control for tracking how definitions evolve, and governance workflows for reviewing and approving context changes.

7. Do context layers work with AI agents and agentic workflows?


Yes, context layers are especially critical for agentic AI where systems act autonomously rather than just responding to queries. Agents need to understand not only what data exists but which data is relevant, what actions are permitted, which policies apply, and how to handle exceptions.

Context layers provide the scaffolding that enables agents to reason about multi-step workflows, coordinate across systems, and make decisions that align with organizational rules even when facing novel scenarios.

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