enterprises have raced past pilots into production AI agents<\/a> without building the foundation those agents require. Eighty-six percent of organizations have deployed agents beyond the pilot stage, yet only 34% trust what those agents actually do. Companies in the bottom readiness quartile, meaning weak governance, fragmented integration, and poor API management, are moving to production anyway and absorbing an average of $2.1 million in compliance failures, downtime, and rework costs. Organizations with strong integration and governance report 59% productivity gains.<\/p>\nWhat this means for your business<\/h2>\n
The 52-point gap between deployment rate and trust rate is the number worth sitting with. Most enterprises now have agents running in production, which means the question is no longer whether to deploy but whether the data infrastructure underneath those agents is actually ready to support autonomous decision-making. If your organization is in the majority that deployed first and governed second, the $2.1 million average cost figure is not a warning about future risk. It’s a description of what is likely already accumulating.<\/p>\n
The pattern here has a reliable shape. Organizations treat AI agents like SaaS tools, something you procure, configure, and switch on. But an agent that can take actions, write to systems, trigger workflows, and move money or data is only as trustworthy as the data it can see and the guardrails that constrain it. Integration, the connective tissue linking agents to applications, data stores, and each other, turns out to be the single clearest differentiator between the top and bottom readiness quartiles. That’s not surprising in retrospect, but it is underinvested. Fifty-five percent of the top quartile report high confidence in their agents’ outputs; only 22% of the bottom quartile can say the same. That gap is a governance debt problem, not a model quality problem.<\/p>\n
The decision this reframes is a structural one most CIOs already own. If your AI and integration teams report into separate org charts with separate roadmaps and separate vendor relationships, your agents will keep operating on fragmented, ungoverned data regardless of how capable the underlying models become. Forrester’s recommendation to unify those teams under one operating model is easy to agree with in the abstract and genuinely hard to execute when integration has historically lived under IT ops and AI lives under a CDO or a new transformation office. The organizations closing the trust gap aren’t buying better agents. They’re reorganizing first.<\/p>\n
Concept deep-dive: Agentic control plane<\/h2>\n
A control plane, in this context, is the centralized governance layer that sits above AI agents and manages what they’re allowed to access, what actions they can take, and how their decisions get logged and audited. Think of it as air traffic control for autonomous software. Without it, agents may have the capability to act but no boundary conditions on when or how. The business connection is direct: a control plane is what converts a deployed agent into a trusted one.<\/p>\n
Based on reporting from Study finds 86% of enterprises have deployed AI agents, but just 34% trust them<\/a>, originally published 2026-07-22 21:00:00.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"Share with your CIO A Forrester Consulting study commissioned by Boomi finds that enterprises have raced past pilots into production AI agents without building the foundation those agents require. Eighty-six percent of organizations have deployed agents beyond the pilot stage, yet only 34% trust what those agents actually do. Companies in the bottom readiness quartile, […]<\/p>\n","protected":false},"author":1,"featured_media":6341,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[142],"tags":[185],"tmauthors":[],"class_list":["post-6340","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai-agents","tag-cio"],"_links":{"self":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/6340","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/comments?post=6340"}],"version-history":[{"count":0,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/6340\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media\/6341"}],"wp:attachment":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media?parent=6340"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/categories?post=6340"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tags?post=6340"},{"taxonomy":"tmauthors","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tmauthors?post=6340"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}