企業面對的不是 AI 智能體問題，而是上下文問題

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By Manish Sood, founder & CEO of Reltio, an SAP company, and Irfan Khan, president & chief product officer of SAP Data & Analytics

Sixty-three percent of organizations either lack or aren't sure they have the data management required for AI, according to Gartner. Worse, the firm predicts that companies will abandon 60% of AI projects by the end of this year due to a lack of AI-ready data.

Not because of model performance, talent, or cost. It's the data they already have but can't use.

Across every industry, enterprises are deploying autonomous AI agents to execute workflows and make decisions at machine speed. Yet the data those agents rely on remains fragmented, duplicated, and inconsistent. The agent isn't the bottleneck — the scattered data layer underneath it is. Before AI agents can deliver meaningful ROI, organizations must bridge the gap between content and context. Otherwise, they risk automating ambiguity at unprecedented speed.

The illusion of the 'single source of truth'

Ask a large organization how many active customers it has, and sales, finance, and support will each cite a different number. None of them is necessarily wrong. They simply never agreed on what "active" means in the first place.

When a human employee spots a data discrepancy, they ask a colleague for context. An AI agent does not. Unless explicitly programmed to flag ambiguity, an agent will instantly select an answer and act on it across enterprise systems.

That speed makes agents valuable, but also dangerous. An agent acting on an incorrect definition of "active customer" doesn't just produce a flawed spreadsheet; it triggers misrouted shipments, duplicate outreach campaigns, or unnecessary compliance flags. The errors compound before human oversight can intervene. The consequences of bad data aren't new, but the speed at which they now play out is.

When this problem surfaces, the typical executive instinct is to consolidate and move everything into a single data lake or platform. But consolidation merely puts conflicting records in the same room. It doesn't resolve which record is accurate, how the records relate, or what "active customer" means in a specific workflow.

Consolidation gathers data in one place. Unification resolves identity, establishes shared definitions, maps relationships across systems, and encodes the business rules governing what an agent is permitted to do.

Without that contextual layer, there is no single source of truth — only a larger warehouse of conflicting data, and an AI agent with the keys to act on it.

What real-time, unified data actually requires

Consider an AI agent tasked with resolving a delayed customer order. To complete the task, it must:

Identify the core data domains, such as Customer, Product, Supplier, Asset, Location, etc., across multiple existing systems.
Determine which contract and service-level agreement applies.
Connect the customer record to the specific distributor, product, and shipment.
Verify what financial or operational remedy it is authorized to offer.

To choose the right action, the agent needs a current, unified view of the customer, order, and related records, along with the business rules that govern its response.

Delivering that context requires a real-time, unified data product. This is an ongoing discipline, not a one-time cleanup: Identities, relationships, and rules must stay current as records change and new information arrives. Giving agents relevant, reconciled context can also reduce token consumption by limiting the need to retrieve and compare conflicting records.

Forrester's research identifies context management and error recovery as sources of AI agent failure, underscoring that fixes focused on prompts or model tuning are insufficient. Enterprises must address the quality and reliability of the context their agents use to act.

They must also break down the wall between analytical and operational data. Autonomous agents need to reason across historical patterns and real-time operational state simultaneously, without sacrificing governance or speed.

Context is the durable competitive moat

The foundational models powering AI agents will continue to commoditize. Within a few years, the raw model capabilities available to competing enterprises will look virtually identical.

The lasting differentiator won't be the model you license — it will be the proprietary business context that enables an agent to reason within your specific organization: your customer relationships, product logic, and operational rules.

Context must be built from the relationships and institutional knowledge already living inside your data. For example, a retailer lets an agent approve refunds for delayed orders. Customer, order, and contract data must first agree; the agent then refunds a set limit and escalates the remainder. Companies investing in that foundation today are building an asset that compounds with every new use case. It's the secret sauce to AI investments that delivers tangible business results.

A four-step context audit for executives

Before assigning critical decisions to autonomous agents, leadership teams should walk through four key steps:

Start with the decision, not the tech. Identify a consequential decision the agent will support. Define the target outcome and explicit limits on its autonomy.

Map the required context. Determine every entity the decision touches — customers, suppliers, products, assets, and locations. Verify whether internal teams agree on what those entities mean.

Resolve the definition gaps. Where identities or business logic diverge across systems, establish a unification layer to reconcile the meaning of the data before letting an agent touch it.

Define authority and escalation. Set clear rules for what the agent can execute independently, when human approval is required, and how decisions will be audited.

Data products require continuous maintenance, like any core operational asset. Treat unified context as a permanent business discipline, not an IT project with an end date. The organizations that win won't necessarily deploy the most agents — they will deploy the agents that understand their business best.

See how Reltio delivers the unified data context AI agents need to succeed.

This sponsored post was supplied by Reltio, an SAP company.