Recently, I was talking to a team piloting an AI assistant designed to let sales reps ask questions in natural language using text to SQL conversion right from their Slack app without writing SQL or searching dashboards. They had just released the Slack assistant, but results were underwhelming. Users were frustrated with its limited capabilities, and adoption dropped after only a few tries.
We made it work, but it took too long to get reliable answers. In the end, we had to lower business expectations because our AI Assistant was not consistently producing accurate results.
If you follow Reddit’s r/dataengineering, this story will sound familiar. Many teams spend endless cycles tuning their AI agents to generate reliable and accurate results, often narrowing scope, or even abandoning pilots altogether.
In this blog series, we will break down the problem, highlight the barriers to building AI agents with data, and share a framework that enterprises can use to turn AI investments into real business outcomes.
The AI Assistant for Sales Teams
Consider an AI assistant built for sales teams. Its goal is to help sales reps focus their time on the deals most likely to close. One of the most common questions they ask is:
“What deals are at risk of slipping to next quarter, and what can we do to accelerate them?”
Answering this compound question isn’t as simple as pointing an LLM at a data lake. Even the best models will struggle without the right context.
Breaking down the problem
- Interpret the meaning: The agent must first break the question into smaller parts to identify intent and context. For example: “Which deals?” “When is next quarter?” “How should risk be defined?”
- Data Sources & Relationships: After understanding the intent, the agent must identify relevant data sources—tables, schemas, and documents. It also needs to understand how these sources are related.
- Semantic definitions: Semantics mean standardized definitions of business terms. In data analytics, this usually refers to consistent definitions of metrics, KPIs, and business entities. These definitions directly shape how common terms are defined and are used to create a shared understanding. For example- how pipeline health is assessed and how risk is interpreted for deals in the current or next quarter.
- Governance & Compliance: Once data and metrics are identified, the agent must apply company policies and governance rules. This ensures that results are accurate, compliant, and that sensitive data is masked where required.
- Trusted Insights: After accuracy checks, source verification, and policy enforcement, the output is ready. Analytics has always been about delivering insights, not just dashboards. Here is where AI agents can shine, drawing actionable insights that lead to trusted outcomes.
The Pain Today
Today teams spend endless cycles of prompting & re-prompting the AI agents trying to manually stitch together the “context” needed for the AI agent to accomplish each step described here. Then, they have to manually verify each step and output from the AI Agent. This makes the process slow and painful—often taking weeks or forcing teams to de-scope the original use case just to ship something usable out the door.
Furthermore, this cycle repeats with every new AI Agent. Since the required context is not readily available for reuse, it forces teams to start the process from scratch.
What if there was an automated way to stitch together this context?
What if there was an easy way to provide AI agents the access into this context without the need to build it for each Agent?
What if this Enterprise Context was a living, breathing entity that constantly learns, evolves and adapts with the enterprise and with the AI Agents?
Defining the Enterprise Context
Enterprise context acts as a company’s shared brain, encompassing data, systems, and processes. It enables AI agents to grasp business terms and operations like employees do. It transcends raw data, integrating business taxonomy, rules, and key relationships to transform information into actionable insights.
In short, the enterprise context unifies enterprise knowledge into a structured, standardized form that AI agents can reliably utilize. Where knowledge is fragmented and informal, context organizes and codifies it into a usable layer.
How Organizations Manage Context Today
- Enterprise architecture teams build the foundation with ERwin diagrams and canonical data models that define how business entities relate across systems.
- Analytics teams operationalize definitions through data catalogs, semantic models, and governance frameworks that guide daily usage.
- Business teams document processes and institutional knowledge in BI reports, best practice guides, and platforms like Confluence.
Each group contributes essential pieces, but the result is fragmented. Definitions live in catalogs, logic in semantic models, and processes in governance tools, while some knowledge remains undocumented. Despite investments in data systems, AI agents still lack the unified context needed for performance.
Need for a Unified, Standardized Context
The emergence of AI agents has created an urgent need to eliminate silos and consolidate fragmented knowledge into a single, standardized layer accessible in every interaction. Without this, AI systems risk replicating the confusion that arises from scattered and inconsistent information.
Enterprise context is the bridge that pulls these scattered definitions together into one coherent layer, ensuring AI agents don’t face the same confusion humans do when knowledge is fragmented.
Benefits of Enterprise Context
In an agentic world, humans can’t fill the gaps manually. AI agents need standardized context to operate at scale without constant oversight.
- Consistency: AI agents deliver uniform answers across business functions. Strategic decisions are based on shared understanding rather than conflicting assumptions.
- Trust: Clear lineage shows data sources, applied rules, and reasoning, turning AI from a black box into a trusted advisor.
- Compliance: Policies, classifications, and audit trails are built in automatically.
- Speed: Development accelerates when teams don’t have to keep explaining basic concepts. Enterprise context serves as institutional memory, letting teams build faster without revisiting definitions.
Conclusion
Providing AI agents access to data sources is not enough. They need enterprise context. Current approaches are manual and do not scale. To build AI agents that truly deliver business value, enterprises need a structured, evolving context that reduces ambiguity, enforces governance, and accelerates development. Enterprise context is not optional, but it is the critical layer that makes enterprise AI real.
In Part 2 of this series, we’ll dive into the framework for building enterprise context, exploring its layers—technical metadata, business semantics, ontology, governance—and introduce how Trust3 IQ can build and manage this context, unlocking the AI code for enterprises.
