To start a context layer pilot or proof of value, pick one AI use case that fails today because the agent lacks business context, and scope the context layer to the data that use case touches. Write down the pass mark before you build: a set of real questions, the agent’s score on them today, and the score that counts as success. Then run a fixed 6 to 10 week cycle that connects sources, curates definitions and owners, exposes the context to one agent, and measures the change.
What is a context layer pilot for enterprise AI, and how is it different from an AI pilot?
An AI pilot tests a model or an agent. A context layer pilot tests the thing underneath it: the governed business context that tells an agent what “active customer” means, which revenue table is certified, who owns it, and what the agent may see. The agent is only the test harness. What you keep at the end is the context layer itself, and it should work for the second agent as well as the first.
The distinction matters because AI pilots tend to die on data and context long before model quality becomes the limit. Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, and it named poor data quality first among the causes. In February 2025, Gartner went further: through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.
“Pilot” and “proof of value” get used as synonyms. They answer different questions, and a good plan answers both in one cycle.
| AI pilot | Context layer pilot | Proof of value | |
|---|---|---|---|
| Question it answers | Can this agent do the task? | Does governed context make the agent correct? | Is the gain worth funding? |
| What you build | A prompt, an agent, a demo | Definitions, owners, lineage, access rules for one domain | A before-and-after measurement |
| Who signs off | The builder | The domain owner | The budget owner |
| What survives | Usually nothing | A reusable slice of context | A business case for the next domain |
How do you choose the first use case for a context layer pilot?
Choose a use case where wrong answers are frequent, visible, and cheap to measure. The best candidate is a question people ask an analyst every week, where someone can say whether an answer is right.
Five tests narrow the list:
- One domain, one owner. A single business domain, such as revenue, claims, or orders, with a person who can rule on disputed definitions.
- A failure you can show. An agent or dashboard that gives wrong or inconsistent answers today, so the baseline is real.
- Connectable sources. The data sits in systems you can connect in days, not quarters.
- A weekly consumer. A team that will use the result after the pilot ends.
- A small blast radius. Read-only answers first. Agents that take actions come later.
Gartner’s advice on AI-ready data starts in the same place: align data sources with specific AI use cases.
| Candidate | Why it works as a first pilot | Watch out for |
|---|---|---|
| Metric questions on one domain (“What was net revenue retention last quarter?”) | Answers are checkable against a certified report | Competing metric definitions across teams |
| Data discovery for agents (“Which table holds active subscriptions?”) | Low risk, fast to measure | Value is harder to put in dollars |
| Operational triage (“Who owns this pipeline, and what breaks downstream?”) | Lineage and ownership pay off quickly | Needs fresh metadata, or answers go stale |
Model only the context that the first consumer needs.
AI Agent Context Readiness Checklist
Check whether the domain you picked has the owners, definitions, and lineage a first pilot needs.
Get the ChecklistWhat are the steps to build and run a context layer proof of value?
Run it as five phases with an exit signal for each. A phase ends when its signal is true, even if the calendar disagrees.
| Phase | Weeks | Work | Exit signal |
|---|---|---|---|
| 0. Baseline | 1 | Collect 50 to 100 real questions with expected answers. Score the agent today. | A baseline accuracy number the domain owner accepts |
| 1. Connect | 1 to 2 | Connect the warehouse, BI, transformation, and orchestration tools for the domain | Cross-system lineage answers “what feeds this table?” |
| 2. Curate | 2 to 3 | Add glossary terms, metric logic, descriptions, owners, and certification. Let AI draft, and have owners review. | Every asset in scope has an owner and a certified meaning |
| 3. Expose | 1 to 2 | Serve the context through an API or the Model Context Protocol, an open standard introduced in November 2024. Enforce access at retrieval. | The agent reads governed context, with the user’s permissions |
| 4. Measure | 1 to 2 | Rerun the question set. Put 10 to 20 real users on it in shadow mode. Write the readout. | A before-and-after score and a scale plan |
Write the question set before anyone builds, so nobody tunes the context to known questions, and add new failures every week. Gartner’s analyst guidance treats AI-ready data as a practice that improves with every new use case.
Which metrics prove a context layer pilot worked?
Prove it on three levels: the agent gives better answers, the context is in better shape, and the business saves time or money. Agree on the pass mark for each before phase 1.
| Level | Metric | How to measure |
|---|---|---|
| Answer quality | Accuracy on the question set | Share of questions answered correctly, before and after |
| Answer quality | Faithfulness and context precision | Open evaluation metrics such as context precision, context recall, and faithfulness |
| Context health | Coverage | Share of in-scope assets with an owner, a description, and a certified definition |
| Context health | Freshness | Time from a schema or definition change to the agent seeing it |
| Business | Time to answer | Analyst hours per week spent on the pilot’s questions |
| Business | Pilot cost | Context spend (people, tooling, compute) against the value returned |
| Business | Adoption | Weekly users in the last two weeks of the pilot |
| Business | Reuse | Effort or time for the second agent or domain against the first |
A typical pass mark reads like this: “the agent answers 80% of the question set correctly, up from the baseline, with every answer traceable to a certified asset.” Pick your own number, but pick it in week 0.
Funding gets decided at the business level. BCG’s 2025 study of more than 1,250 firms found that only 5% achieve AI value at scale, while 60% report minimal revenue and cost gains. Report analyst hours returned and errors avoided.
The AI Context Stack
A brief on the layers an agent needs from enterprise data, from metadata and lineage to definitions and access rules.
Get the AI Context StackWhy do context layer pilots stall, and how do you avoid it?
Most stalls trace back to scope and ownership. The commonest one is starting with the agent: a demo agent answers the questions it was rehearsed on, so build the context first and treat the agent as the test. Close behind is modeling everything, where a team spends quarters on an enterprise ontology before any consumer exists; scope to one domain and one question set instead. Name an owner for each asset in phase 2, because ownership is a people decision that does not get easier after launch. Enforce permissions at the retrieval interface, since access rules applied after the agent retrieves context come too late. And plan for the day after the pilot ends: the readout should name the next two domains and the team that runs the layer.
The cost of skipping these is high. Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. MIT’s NANDA initiative reported that about 95% of enterprise generative AI pilots delivered little to no measurable impact on P&L.
For a video walkthrough of why AI pilots stall between the demo and production, see why AI pilots fail in production.
Before the build begins, put the exit signals (the condition that ends each phase), the question set, and the scale plan in one document, and have the domain owner and the budget owner sign it.
How does Atlan run a context layer pilot?
Atlan runs the pilot as one loop on one domain, starting from the metadata a team already has. Workday’s experience shows why. Joe DosSantos, VP of Enterprise Data and Analytics, described the starting point plainly: “We built a revenue analysis agent and it couldn’t answer one question.” His team realized they were missing a layer that translates human business language into the structure of their data. Today Workday’s shared language reaches AI through Atlan’s MCP server.
Atlan’s single-domain guide starts with one domain, one team, and one AI client, and its enterprise path maps authoritative context sources, unifies them into one governed graph, applies domain ownership and policy, then activates context for agents. Teams connect sources into one metadata graph, link glossary terms to assets, run context agents that draft descriptions at scale, and set domain-scoped access policies for agents. Context Engineering Studio then runs a Build, Simulate, Deploy, Observe loop:
- Build generates a semantic model for the domain from the existing catalog.
- Simulate tests a question set against the model and shows the gaps, which is the pilot’s measurement step.
- Deploy certifies the context repo and publishes it to Snowflake Cortex Analyst, Databricks Genie, or MCP-compatible agents.
- Observe feeds production failures back into the question set. It is available only for Snowflake Cortex deployments today.
CME Group cataloged over 18 million assets and defined more than 1,300 glossary terms in its first year on Atlan. For what that does to rollout time, see why AI context platforms cut agent rollout from months to weeks.
Atlan in Action: Live Context Layer Demos
See how Context Engineering Studio builds, simulates, and deploys context for one domain.
Watch the Live DemoFAQs about context layer pilots
How long should a context layer pilot take?
Plan for 6 to 10 weeks on one domain. One week sets the baseline and question set, connecting and curating the context take 3 to 5 weeks, and exposing it to an agent and measuring the result take 2 to 4 weeks. Without existing lineage or named owners, expect longer phases.
What is the difference between a context layer MVP and a pilot?
An MVP is the smallest context layer that serves one real consumer: one domain, its definitions, owners, and access rules, exposed to one agent. A pilot is the time-boxed test of that MVP with real users and a pass mark. A proof of value adds the business case for the next domain.
How many questions should the evaluation set include?
Start with 50 to 100 real questions taken from tickets, chat threads, and query logs, each with an expected answer the domain owner signs off. Fewer than 50 makes the accuracy score noisy. Add each new failure, so the set becomes a regression test.
Who should own a context layer proof of value?
Give it two owners. A data or AI platform lead owns the build, the schedule, and the measurement. A business domain owner rules on disputed definitions and accepts the baseline and the final score. Without the second owner, nobody can confirm the answers are right.
Should we build or buy the context layer for a pilot?
Build if the pilot covers one domain and you have engineers who will keep maintaining connectors, lineage, and access rules after it ends. Buy if the second domain needs to follow quickly, because most of the cost of a homegrown layer sits in upkeep.
Sources
- Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept by End of 2025, Gartner
- Lack of AI-Ready Data Puts AI Projects at Risk, Gartner
- Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, Gartner
- Introducing the Model Context Protocol, Anthropic
- Available metrics, Ragas documentation
- The Widening AI Value Gap, BCG (October 2025)
- MIT report: 95% of generative AI pilots at companies are failing, Yahoo Finance
- Build a context layer, Atlan documentation
- Build an enterprise context layer, Atlan documentation
- Context Engineering Studio, Atlan documentation
