Mike Lovelace, Vice President – Strategic Growth
Most organizations have plenty of data. What is lacking to drive true business value from this data is actionable intelligence.
Key information that can drive action often already exists, spread across operational systems, customer platforms, financial applications and analytics tools. The challenge is bringing that information together, understanding what it means for the business, and turning it into better decisions and recommended actions.
Businesses have invested heavily in systems designed to solve specific problems. CRM platforms manage customers. ERP systems manage financial and operational processes. Operational environments add another layer with point-of-sale and order management, inventory and supply chain, labor scheduling, e-commerce, loyalty, and performance-management platforms. Each system serves an important purpose and generates valuable data. Increasingly, many of these systems now include their own analytics and AI capabilities.
The problem is that the business does not operate one system at a time.
A business experiencing declining margins, for example, may need to understand the relationship between sales, product mix, inventory, labor, promotions, customer behavior, and channel performance. Each system may provide a piece of the answer. The real value comes from connecting those pieces to understand what they mean together.
Business intelligence platforms changed how organizations consume information. Dashboards made data visible. Leaders could see performance, identify trends, and drill into results without waiting for a manually assembled monthly report. While valuable, most dashboards still answer variations of the same question: What happened?
· What were sales last week?
· Which locations missed their targets?
· Where did expenses increase?
· What happened to conversion?
· Which products or channels underperformed?
Answers to these questions are primarily retrospective. A dashboard can tell us that something has changed. It does not necessarily tell us why it changed, what else contributed to it, what is likely to happen next, or what action we should take.
Imagine driving 80 miles per hour while navigating primarily through the rearview mirror. The mirror contains accurate information. It tells you where you have been, but it prevents you from properly seeing the available paths forward.
For many organizations, that is still how data is being used.
The rapid adoption of AI can reinforce this fragmentation when intelligence remains confined within individual applications.
An organization may have AI inside many of its technology platforms and analytics tools with each becoming smarter within its own boundaries. As examples, the CRM can surface propensities of customers; the workforce platform can identify trends in labor; the financial system shows cost patterns; and the inventory platform may flag something else entirely. You now have more intelligence but still fragmented. It is the enterprise equivalent of having several experts sitting in separate rooms who never speak to one another.
That challenge becomes more important as AI proliferates. MuleSoft's2026 Connectivity Benchmark found that half of AI agents currently operate in silos, while 86% of IT leaders expressed concern that agents could add more complexity than value without proper integration.
Each system provides a piece of the picture but individually do not address what those signals mean together. Why did margin decline? What factors contributed most? Is the issue isolated or systemic? And, ultimately, what should the business do next?
The same issue appears in the member experience. A member may contact their health insurer with what seems like a simple question: “Why wasn’t this claim paid the way I expected?”
Answering that question may require information from several different systems: member eligibility, plan benefits, claims history, provider information, prior authorizations, explanation-of-benefits data, billing records and previous service interactions.
Each system may contain part of the answer. The challenge is bringing those pieces together quickly enough to give the member a clear, accurate response without forcing them, or the service representative, to navigate multiple disconnected systems.
The customer experiences fragmentation as friction. They wait. They repeat information. They get transferred. A service representative moves between applications and manually assembles the answer.
The problem is not necessarily that the information does not exist. The information exists in pieces.
The health plan example illustrates a challenge impacting many industries. Information needed to answer an important question or make an important decision already exists but is too dispersed to be effectively useful. Important business decisions rarely fit neatly within one perspective.
This leads to a different question: How many systems does someone have to access—and how many different views do they have to reconcile—before they have enough context to make an important decision?
The answer exposes the real opportunity. It is not simply more data, more reporting, or more AI, but rather connecting what the organization already knows so people can understand what is happening in context.
At Covalent, we call this Operational Intelligence: bringing information across systems and functions into context so the organization can understand what is happening, why it matters, and what action should come next.
The results:
· Better decisions
· Faster action
· Improved customer experience
· Stronger operational/financial performance
Next in the series: From Data to Action — Building a Connected Intelligence Layer
Partners in your digital transformation.
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