Boomi: Data Activation, Key to AI in Business

Boomi and Data Activation: The Missing Key for Enterprise AI

In 2026, the main challenge facing businesses with artificial intelligence (AI) is not what one might expect. Contrary to popular belief, it is neither model errors nor agents' inability to reason that pose the problem. The real difficulty lies in data fragmentation. This data, essential for the functioning of AI systems, is often poorly labeled and scattered across a multitude of applications that were not designed to share a common context.

Boomi, a company specializing in data integration, refers to this challenge as the data activation problem. After observing the operation of 75,000 AI agents among its clients, Boomi emphasizes that resolving this issue must be a top priority. In February, Boomi announced it had reached a record with over 30,000 clients worldwide, more than a quarter of which are part of the Fortune 500. According to Steve Lucas, President and CEO of Boomi, the value of AI only materializes when data is properly activated, reliable, and governed. This statement was made during the announcement of the platform's new capabilities on March 9.

The Fragmentation Problem

Businesses are not lacking in data. It is abundant and exists in various systems such as ERPs, CRMs, data lakes, SaaS platforms, and other legacy applications accumulated over the years. However, what is missing is a shared context that would allow an AI agent to process this data consistently across different systems.

For example, an agent extracting customer records from a CRM and pricing data from an ERP may encounter conflicting definitions of what constitutes a customer or a product. The results produced by the agent will only be consistent if the underlying data standards are as well.

To address this issue, Boomi has introduced Meta Hub, a central reference system announced during the platform update on March 9. This system is designed to standardize business definitions within the organization and extend this context to every AI agent. The goal is to ensure that agents reason from a uniform understanding of business logic, rather than generating results based on fragmented interpretations from disconnected systems.

During the same announcement, Boomi also introduced real-time SAP data extraction through change data capture. This addresses one of the main integration bottlenecks in large enterprises, where SAP data is often inaccessible due to slow manual export processes, rendering it unavailable for real-time AI workflows.

Additionally, new governance capabilities for Snowflake Cortex agents within Boomi's Agent Control Tower have been added. These include audit trails and session logs, addressing a growing concern among businesses: AI agents operating as black boxes, making decisions without a visible reasoning chain.

What Analyst Recognition Signals

In March, two independent assessments provided Boomi with external validation of its market positioning. On March 16, Gartner named Boomi a Leader in its Magic Quadrant 2026 for integration platforms as a service, marking the twelfth consecutive time. Boomi was recognized for its execution capability.

On March 31, the IDC MarketScape for global API management also named Boomi a Leader. This report highlighted Boomi's AI-centered strategy, which views APIs as both the fuel and the control plan for AI workloads. According to Gartner, AI-ready integration is a strategic capability that aligns architecture, integration, and governance to enable AI agents to effectively access enterprise data and operate within business processes. This framework validates the problem Boomi is addressing and indicates that iPaaS platforms are now being evaluated on their AI readiness, beyond their traditional integration capabilities.

The Bigger Picture

It is becoming clear that the transition from pilot to production in enterprise AI is stalling at a predictable point. Organizations have models and agents, but many lack the necessary data infrastructure to make these agents reliable enough for use in real business processes.

Data activation, which involves transforming static data into dynamic, governed, and context-rich flows, is essential for agents to truly reason. This approach could become the industry standard or be integrated into a broader category, a question that 2026 will begin to answer.

What is certain is that the companies that succeed in achieving a return on investment with agent-based AI are those that have first resolved the data layer issue.