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Boomi commissioned Forrester Consulting to survey 409 IT and technology decision-makers across North America, Europe, and Asia-Pacific, and the findings draw a sharp line between deployment speed and operational readiness. Eighty-six percent of organizations have moved AI agents past the pilot stage, yet only 34 percent trust what those agents actually do. The gap between adoption and confidence carries a price: companies in the lowest readiness quartile face an average of $2.1 million in additional costs from compliance penalties, disruptions, and rework.
What this means for your business
The study splits the enterprise AI field into two camps, and which one you’re in is decided less by your AI model choices than by whether you built integration and governance infrastructure before deploying agents. Companies with strong readiness are nearly twice as likely to use integration platforms to support agentic workflows. If your organization shipped agents into production to hit a board-level deadline, and the data foundation work came second, you’re almost certainly sitting in the “agentic chaos” quartile whether or not anyone has named it that internally.
Boomi, whose core product is an integration platform, has an obvious interest in framing the trust deficit as an integration deficit rather than, say, an AI model quality problem or a change management problem, and that framing is worth stress-testing. But the underlying logic holds up independently of the vendor. AI agents operate by pulling data from enterprise systems, scoring options, and taking actions autonomously. If the data they pull is incomplete, siloed, or ungoverned, the agent’s autonomy becomes a liability amplifier rather than a productivity multiplier. The 46-versus-25 percent split in iPaaS adoption between high-readiness and low-readiness organizations is the study’s sharpest signal, and it points at architecture, not attitude.
The organizations reporting real returns, 59 percent citing productivity gains and 51 percent citing increased innovation, share one structural trait: they aligned AI and integration teams under a common operating model before scaling. That’s not a vendor recommendation, it’s an org design choice. CIOs who still have AI strategy sitting inside one team and data integration inside another are carrying an invisible coordination tax that compounds every time a new agent goes into production. The budget question worth revisiting isn’t whether to buy more AI capability, it’s whether the teams responsible for the data those agents consume are staffed and empowered to govern it.
Concept deep-dive: Model Context Protocol
Model Context Protocol, or MCP, is an emerging standard for how AI agents request and receive context from external systems, think of it as a structured handshake that tells an agent what data it’s allowed to see and act on in a given situation. Without centralized MCP management, different agents inside the same enterprise can operate with inconsistent data permissions, creating compliance exposure and unpredictable behavior. High-readiness organizations in the Boomi study are significantly more likely to govern MCP centrally, which is what makes their agents trustworthy enough to scale.
Based on reporting from Boomi study finds AI agent trust lags enterprise adoption, originally published 2026-07-21 02:53:00.
