Boomi says 75,000 AI agents in production still struggle to deliver value due to fragmented enterprise data. The finding shifts attention from model performance to infrastructure readiness as the primary bottleneck in scaling AI systems.
The company, which serves over 30,000 customers including more than a quarter of the Fortune 500, identified what it calls the “data activation” problem. According to Boomi, enterprise data is widely distributed across ERP systems, CRMs, and legacy platforms, but lacks consistent labeling and shared context. Without alignment, AI agents generate outputs based on conflicting definitions and incomplete information.
Can Data Activation Unlock Enterprise AI At Scale?
The issue reflects a broader pattern in enterprise AI adoption, where deployments stall between pilot and production. While organizations have invested heavily in models and agents, fewer have standardized the underlying data layer required for reliable outputs. By comparison, traditional integration platforms focused on connectivity, not semantic consistency across systems.
Boomi’s response centers on building a unified data context for AI operations.
“AI only delivers value when data is properly activated, trusted and governed first,” said Steve Lucas, chairman and CEO of Boomi, during the company’s March platform update.
The firm introduced Meta Hub as a central system of record to standardize business definitions and ensure consistent reasoning across AI agents.
Additional updates target real-time data access and governance visibility. The platform now includes change data capture for SAP systems, reducing latency in data availability, and expanded audit capabilities for Snowflake Cortex agents. These features aim to address concerns around opaque AI decision-making and delayed data pipelines.
External validation has reinforced the company’s positioning. Gartner named Boomi a Leader in its 2026 Magic Quadrant for Integration Platform as a Service for the twelfth consecutive year, while IDC MarketScape also ranked it as a Leader in API management, citing its AI-centric approach.
Attention is now shifting toward how enterprises restructure data architecture to support agent-driven workflows. The next catalyst will be whether standardized data layers become a prerequisite for AI investment, shaping how organizations measure return on AI deployments.
