Organizations are adopting AI agents to approve requests, resolve incidents, and complete workflows on behalf of employees. But for long-lasting, scalable success, such responsibilities require context that exists only inside the enterprise.
According to Gartner, enterprises that organize AI-ready data around business context can improve the accuracy of agentic AI by up to 80% while reducing costs by as much as 60%.
What is business context?
General-purpose AI models are trained on the world’s data. They can explain a concept or summarize a document, but they can’t access the data that’s unique to your organization, such as reporting structure, approval hierarchy, or active workflows.
Business context can fill that gap, giving AI the understanding it needs to make decisions that reflect how your business operates. It connects enterprise data to the unique relationships, policies, and decision history that impact every decision.
Imagine your team receives a request to update a customer-facing application. The form captures the request, but not everything needed to evaluate it. Business context reveals which business services depend on that application, whether the deployment falls within a scheduled change freeze, and what approvals are required before work can begin. This context helps determine how the update should be handled.
AI uses business context to evaluate the request similar to how an experienced employee would. Every approved request and resolved incident then becomes part of a growing body of operational knowledge that AI can draw on to help inform similar decisions in the future.
Why context matters
Without business context, AI evaluates each request using only the information immediately available to it. This risks treating similar requests the same even when the business circumstances are very different.
For example, an AI agent could approve the wrong request, overlook a policy requirement, or escalate work that could have been resolved automatically. Context helps AI make more accurate decisions faster by reducing the time spent searching for information and piecing together what happened across multiple systems.
Business context also helps organizations apply policies consistently as AI takes on more responsibility. When an auditor asks why an AI agent approved a request or granted access, organizations can trace that decision back to the business information and policies that informed it. This allows teams to provide a clear record of the reasoning behind every action.
Here are three capabilities to look for when making business context available to AI:
- Connect live enterprise data: Bring together the business information AI needs, wherever it lives.
- Provide context in the moment: Turn that data into business understanding before AI acts.
- Make each decision explainable: Create a clear record of how each AI decision was made and what informed it.
The bottom line
AI becomes more valuable when it understands how your business works. You’ve already built the knowledge AI needs. The more AI can learn from your organization’s experience, the more responsibility it can take on.
Learn how ServiceNow’s Workflow Data Fabric and Context Engine connect enterprise data, surface business context, and help AI make more informed decisions.