Unlocking AI’s full potential: Why context is everything

AI is ubiquitous in both the consumer and enterprise sectors. Yet few organizations are realizing AI’s full potential. Why? AI agents must make decisions and take actions based on a limited subset of overall data. Result: too much guesswork, the occasional hallucination, and failure to extract full value from AI.

The downfall of enterprise AI, then, is agents that falter without a comprehensive understanding of data, both customer- and business-derived. Companies need to be able to pivot from simple data ingestion to sophisticated content collection, integration, and curation that enable AI agents to respond accurately and take appropriate actions.

This can only be accomplished by advancing from traditional prompt engineering to context engineering, which combines a 360-degree view of the customer and a complete enterprise view of a dynamically changing business.

Why enterprise AI is data-rich but context-poor

Many companies implementing AI are data-rich. They use large language models (LLMs) that pull data from all over the internet. They have in-house models that access data from customer databases and product documentation libraries.

Their agents access these pools of information and attempt to guide their decisions. Sometimes they get it right. But too often, they take the wrong action or recommend an incorrect response. What is missing is end-to-end context.

Here is a common example: A person wants to buy a car, so before finalizing their purchase, they go on the manufacturer’s website to research the various options. This data is captured in the car maker’s systems, and over the following weeks, AI directs a series of marketing actions to generate interest in the car model. Without full context, the marketing agent doesn’t recognize that the person has already purchased the car.

This breakdown occurs when one system contains the details of a car purchase, another has records on the individual buyer, and a separate application tracks customer engagement details (such as website visits). Robbed of the rich context of data locked inside information silos, AI digital engagement agents only know that someone researched a car. They’ve missed the opportunity to promote extended warranties and maintenance plans.

Far from rare, such examples are all too common in agentic AI. Enterprises may be data-rich but are context-poor.

Key elements for achieving fluid, unified data

For AI to respond contextually, data needs to be fluid, harmonized, and unified. The walls between silos must be removed.

Achieving this requires several key elements:

Data catalog: The data catalog provides a single view of data across systems. This gives apps and AI agents a map of all assets residing in on-premises systems, the cloud, data lakes, and legacy infrastructure.

Data lineage: Consider this a data verification layer. It traces the full journey of data from origin to consumption, showing every change or transformation along the way. Data lineage enables AI agents to know where any piece of data came from, how it was produced, whether it aligns with organizational governance and regulatory compliance policies, whether it is secure and trustworthy, and whether it reflects the most current knowledge.

Connected signals and actions: Apps and AI agents rely on signals from every system to interpret what’s happening and trigger secure, meaningful actions.
Unified data context: There must be a central repository within an agentic AI architecture that collects, synthesizes, harmonizes, and unifies all information. This context interface for apps and AI agents must operate in real time without requiring file copying or data movement. Whether an AI agent is analyzing a trend or processing a product return, it must provide a single, shared, up-to-the-second view of the customer and the business, aligned with all relevant policies.

Enterprise understanding: Apps and AI agents should not have to relearn the business from scratch. They must act in accordance with the definitions, rules, and principles that underlie each portion of the business. If they don’t, they may appear “AI smart” but “corporate stupid.” Why? Deep metadata intelligence in the enterprise is unavailable to customer-facing systems.

Building enterprise understanding for smarter AI

Enterprise context is vital in defining core business entities and their interrelationships. This context encompasses historical records, master data management (of products, suppliers, assets, and more), business rules, regulatory compliance, and organizational workflows. Comprehensive customer and enterprise records must be unified to supply AI agents with a shared data vocabulary that helps them infer the right context for the right situation at the right time.

Case in point: Large enterprises typically include numerous accounts and corporate entities. The names of various entities may be similar, but there are hierarchies, as well as specific rules and tax schemes that apply by geography and industry. In such a complex organizational structure, if names are entered incorrectly or data is assigned to the wrong corporate entity, AI-based errors are practically inevitable.

Why complete context is key to preventing AI errors

Only the complete unification of customer and enterprise metadata and systems can prevent costly errors and keep AI agents and apps supplied with the applicable context. This way, organizations can consolidate all enterprise and customer data and connect related data from multiple sources to transform trusted context into a meaningful story.

Learn more about Data 360 from Salesforce and how it transforms scattered, fragmented enterprise data into one complete view of your business to fuel real-time workflows, better decision making, and more intelligent agents.

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