By Kevin Keenan, vice president, communications at Reltio
Imagine this. An AI sales agent approves a discount to retain a frustrated customer. At the same time, a finance agent flags accounts for elevated credit risk. Meanwhile, a supply chain agent reprioritizes inventory based on projected margin impact. Each decision is rational. Together, however, they create conflict.
This is the reality of agentic AI. Enterprises are moving beyond chatbots and toward autonomous systems that execute workflows across finance, supply chain, marketing, and customer operations. These agents won't just generate insights — they will act.
The economic upside is significant. Analysts estimate that agentic AI will create a trillion-dollar shift in productivity.
Autonomy without alignment, however, creates friction at scale. The issue isn't intelligence. It's context.
A reliability gap
Nearly every enterprise is investing in AI. According to a recent Harvard Business Review Analytic Services survey sponsored by Reltio, 94% of organizations are exploring AI initiatives. Only 15% believe their data foundation is truly ready for agentic AI.
That gap can help explain uneven returns. While leading companies report meaningful revenue gains, 60% of enterprises see minimal impact despite heavy investment. The difference isn't the model but the environment it operates in.
Most agents are being deployed into fragmented ecosystems where customer records, product data, operational history, and financial systems don't reconcile in real time. Each agent sees only part of the picture. When autonomous systems operate in an inconsistent context, they create irregularities.
Reltio; This image was created with the assistance of Artificial Intelligence.
Looking beyond "dirty data"
For years, enterprises have framed the problem as data quality. They go through the motions trying to fix the issue: removing duplicates, improving governance, and cleaning up fields. While these steps certainly matter, the deeper issue for agent-driven enterprises is missing context. Systems of record capture what happened, but rarely capture the why.
When a manager approves a 20% discount after a service outage, the reasoning often lives in Slack or email. Months later, that decision becomes a data point without a narrative in which an AI agent sees the discount, but not the outage that justified it. This is how well-built systems can make flawed decisions.
Agents need more than just cleaner data. They need a shared, living map of relationships and decision traces that connect customers to products, transactions to events, and actions to intent. In short: they need a context layer.
Reltio
The weight of data debt
Decades of application and system sprawl have left enterprises carrying significant data debt. Support systems don't match marketing databases, and supplier information is duplicated across departments. Financial data lags operational reality.
Data silos remain the top barrier to AI progress, cited by 46% of leaders in the survey. Adding more agents to fragmented systems doesn't solve the problem but accelerates it. Each new agent builds its own partial version of the truth.
The new rules of intelligent data
The companies that win the next decade won't have the most agents but will have ones that operate from the same trusted context.
That shift requires infrastructure designed for unification, such as platforms that connect core data and metadata in real time. This creates a governed understanding of entities and relationships across the enterprise.
Autonomous systems are coming, and it's up to leaders to decide whether their organizations' intelligence will compound or conflict.
Explore the new rules of intelligent data. See how industry leaders are unifying trusted data to stay ahead in the AI era.
This post was created by Reltio with Insider Studios.
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