From Kevin Keenan, Vice President of Communications, Reltio
Imagine this. AI sales agents approve discounts to retain dissatisfied customers. At the same time, financial agents report an increase in credit risk. Meanwhile, supply chain agents reprioritize inventory based on expected profit impact. Each decision is rational. But when they come together, a conflict arises.
This is the reality of agent AI. Companies are moving beyond chatbots to autonomous systems that run workflows across finance, supply chain, marketing, and customer operations. These agents not only generate insights, but also act.
The economic upward effects are significant. Analysts estimate that agent AI will lead to a trillion-dollar shift in productivity.
But autonomy without coordination creates massive friction. The problem is not intelligence. It’s the context.
reliability gap
Almost every company is investing in AI. According to a recent Harvard Business Review Analytic Services survey sponsored by Reltio, 94% of organizations are considering AI initiatives. Only 15% believe their data infrastructure is truly ready for agent AI.
This gap helps explain the heterogeneity of returns. While leading companies are reporting significant revenue increases, 60% of companies have seen minimal impact despite significant investments. The difference is not the model, but the operating environment.
Most agents are deployed in fragmented ecosystems where customer records, product data, business history, and financial systems are not coordinated in real-time. Each agent only sees part of the picture. Irregularities occur when autonomous systems operate in inconsistent conditions.
Reltio. This image was created with the help of artificial intelligence.
Look beyond “dirty data”
For years, companies have viewed the problem as data quality. They are working to solve problems, including removing duplicates, improving governance, and cleaning up fields. While these steps are certainly important, the deeper problem for agent-driven companies is the lack of context. Records systems record what happened, but rarely record why.
If your manager approves a 20% discount after an outage, the reason is often in Slack or email. After a few months, that decision becomes a data point without a narrative where the AI agent recognizes a discount but not an outage that would justify it. Thus, even a well-constructed system can make bad decisions.
Agents don’t just need cleaner data. You need a shared, living map of relationships and decision tracking that connects customers and products, transactions and events, and actions and intent. In other words, we need a context layer.
Reltio
Weight of data debt
Decades of application and system sprawl have left enterprises with significant data debt. Support systems don’t match marketing databases, and supplier information is duplicated between departments. Financial data lags behind operational reality.
Data silos remain the biggest barrier to AI progress, cited by 46% of survey leaders. Adding agents to a fragmented system will not solve the problem, but will accelerate it. Each new agent constructs its own partial version of the truth.
New rules for intelligent data
The companies that win in the next decade won’t have the most agents, but they will have agents operating in the same trusted context.
This change requires an infrastructure designed for integration, including a platform that connects core data and metadata in real time. This creates a disciplined 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 be combined or competitive.
Explore new rules for intelligent data. See how industry leaders are doing it We’re integrating trusted data to lead in the AI era.
This post was created by Reltio. Insider Studio.