Context Maturity Model
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How mature is your context infrastructure?

AI agents are only as good as the context they operate on. Assess your organization across 4 dimensions and 5 maturity stages -- and get a clear picture of what to build next.

Start Assessment How It Works
73%
of enterprises are stuck at Stage 2 or below
4x
faster AI deployment at Stage 4 vs Stage 2
Stage 3
is the critical leap -- where AI goes from pilot to production
How It Works

Two minutes. Four dimensions. Total clarity.

No forms, no sales calls. Click the cells that describe your organization and get an instant read on your context maturity.

1

Read each row

Four dimensions of context: Data, Meaning, Knowledge, and User. Each row describes what organizations look like at each of 5 stages.

2

Click where you are

For each dimension, click the cell that best describes your organization today. Be honest -- the value is in the accuracy.

3

Get your playbook

Once all 4 dimensions are scored, you'll get a personalized breakdown with your biggest gap, your strongest asset, and what to do next.

The Model

4 dimensions. 5 stages. 20 behaviors.

Click the cell in each row that best describes your organization today.

Stage 1
Invisible
No usable context
Stage 2
Tribal
In people's heads
Stage 3
Documented
Written, not machine-readable
Stage 4
Governed
Systematic & shared
Stage 5
Compounding
Self-improving
Data Context
Which data to use & where it lives
No one knows which systems hold what data. Finding the right table requires asking around.
AI can: Nothing. Every project starts with months of data discovery.
Key people know where data lives. New team members learn by asking, not by looking things up.
AI can: Work with 1-2 pre-configured sources. Breaks on cross-system questions.
Data sources are cataloged. Documentation exists but may be stale. No automated lineage.
AI can: Answer scoped questions within documented domains. Still guesses on edge cases.
Enterprise data graph maps all sources with automated lineage. Certified sources are discoverable by AI and humans.
AI can: Discover and access data across systems automatically. Traces answers back to source.
Data graph self-updates. New sources are auto-discovered, profiled, and connected. Lineage is real-time.
AI can: Operate on the full data estate. New data sources enrich all agents automatically.
Meaning Context
What terms & metrics actually mean
Business terms are undefined. "Revenue," "pipeline," and "customer" mean different things to different teams.
AI can: Generate answers that sound right but use wrong definitions. The danger zone.
Teams know definitions differ. Conflicts are resolved ad hoc -- usually by the most senior person in the room.
AI can: Work for a single team with hand-configured definitions. Cross-team use breaks.
Key terms are documented in a glossary or wiki. Definitions exist but AI tools don't consume them automatically.
AI can: Reference human-readable docs. But can't enforce consistency across tools or teams.
Governed semantic layer with machine-readable definitions. All AI tools consume the same definitions.
AI can: Ensure every answer uses the governed definition. Cross-team consistency is automatic.
Semantic layer evolves with AI-suggested refinements, usage-based conflict detection, and automated governance.
AI can: Flag definition conflicts proactively. New metrics are auto-proposed from usage patterns.
Knowledge Context
Business rules, exceptions, tribal knowledge
Business rules live in the heads of 2-3 long-tenured employees. You learn them by making mistakes.
AI can: Nothing safely. Every edge case is a landmine waiting to produce wrong answers.
Some rules are scattered across Slack threads, wikis, and email chains. Finding the right rule requires knowing who to ask.
AI can: Handle the 80% case. The 20% of exceptions -- which matter most -- are invisible.
Key rules are documented in runbooks and SOPs. Mostly current, but not in a format AI agents can query programmatically.
AI can: Be guided by humans who know the rules. But can't apply them independently.
Business rules are in a governed knowledge base. AI agents query rules directly when making decisions. Exceptions are codified.
AI can: Apply business rules automatically. Handles exceptions that would have tripped up earlier stages.
Knowledge base is living -- AI proposes new rules from observed patterns, humans validate, the system compounds.
AI can: Learn from edge cases. Every interaction makes institutional knowledge more complete.
User Context
Who's asking changes the answer
AI gives the same answer to everyone regardless of role, permission level, or decision context.
AI can: Serve one audience. Governance and personalization are nonexistent.
Basic access controls exist. AI is filtered by permissions, but framing doesn't adapt to the user's role.
AI can: Hide data users shouldn't see. But a CFO gets the same view as an analyst.
Role-based views are configured per tool. Access policies exist but are maintained separately, creating drift.
AI can: Serve different views per role within a single tool. Cross-tool governance is manual.
Unified governance layer. AI understands each user's role, typical decisions, and access level across all tools.
AI can: Tailor answers: financial framing for CFO, operational detail for managers, strategic summary for CEO.
AI learns from interaction patterns. User preferences compound. The system anticipates needs before they ask.
AI can: Proactively surface insights. Each interaction makes the system more personalized for everyone.

Your Context Maturity Score

Data
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Meaning
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Knowledge
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User
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Overall Context Maturity Select all 4 dimensions

Your Context Maturity Playbook

What Each Stage Unlocks

Context maturity directly predicts AI capability

Each stage unlocks specific AI capabilities that were impossible at the prior stage.

Stage 1: Invisible
AI can demo. Not deploy.
Pilots work in controlled settings because a human hand-curated all the context. Production is impossible.
Toy demos with synthetic data
Internal chatbots nobody trusts
POCs that never graduate
Stage 2: Tribal
Single-team pilots that work in the room.
One team with one expert can make AI work for one use case. Breaks when that person is unavailable.
Department-level analytics bots
Pre-configured dashboards with AI
Assisted search within one domain
Stage 3: Documented
First use cases reach production.
AI handles the happy path. Each use case is hard-won -- months of work to get context right.
Production AI for well-scoped use cases
Cross-system queries (with caveats)
Basic AI-assisted reporting
Stage 4: Governed
AI scales across the organization.
New use cases build on shared context infrastructure. Weeks to deploy, not months.
Multi-agent systems with shared context
Enterprise-wide AI assistants
Governed, auditable AI decisions
Stage 5: Compounding
AI makes the whole org smarter.
Every agent interaction enriches the shared context. New capabilities emerge that weren't designed.
Self-improving knowledge systems
Proactive AI that anticipates needs
AI-native decision infrastructure
The Transitions

What it takes to move up one stage

Each transition requires a different type of investment. Focus on what matters for your current stage -- not what's aspirational.

Invisible → Tribal

Find the knowledge holders

You can't build what you can't find. The first investment is discovery -- mapping where data lives, who knows what, and which definitions differ.

Key investments

  • Data source inventory across all systems
  • Identify the 3-5 people who hold critical tribal knowledge
  • Document the top 10 business terms that cause confusion
Tribal → Documented

Get it out of their heads

The knowledge exists -- it's just not accessible. This transition is about making implicit knowledge explicit and findable.

Key investments

  • Data catalog with searchable source documentation
  • Business glossary with agreed-upon definitions
  • Runbooks and SOPs for critical business rules
  • Basic access control policies documented
Documented → Governed

Make it machine-readable and shared

Documentation that only humans read isn't enough. Context must be consumable by AI agents, governed by policy, and shared across use cases.

Key investments

  • Semantic layer with machine-readable metric definitions
  • Enterprise data graph with automated lineage
  • Structured knowledge base for business rules
  • Unified access governance across AI tools
  • Change management for definition updates
Governed → Compounding

Make every interaction smarter

The rarest transition. Context becomes a living asset that improves itself. Less than 5% of enterprises reach this stage.

Key investments

  • AI-assisted context curation (suggested definitions, rules)
  • Feedback loops from AI usage to context refinement
  • Conflict detection and resolution workflows
  • Cross-agent learning -- insights from one agent enrich all

Trusted by data leaders at

Mastercard Workday General Motors Ralph Lauren Virgin Media O2 HubSpot

Know where you stand.
Know what to build next.

Context maturity isn't just a framework -- it's the difference between AI pilots that stall and AI programs that scale.

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