主なポイント
- Many organizations are stuck in “agentic chaos,” causing significant financial risk, as they rush to deploy autonomous agents without proper infrastructure or governance.
- A critical “trust gap” and agent sprawl persist because organizations lack a unified platform to connect autonomous agents to core enterprise data and applications.
- Successful AI maturity and ROI require a centralized governance control plane to ensure agents are secure, reliable, and well-managed.
Autonomous AI agents are no longer a theoretical concept, but an operational necessity. As businesses transition from experimentation to the enterprise, racing toward an autonomous future to capture early competitive advantages, a critical operational hurdle emerges between deployment speed and effective enterprise governance. This disconnect leaves organizations operating in silos, struggling to manage autonomous systems and unable to interact meaningfully with core enterprise data or workflows.
This lack of control isn’t merely a technical growing pain; it’s a financial liability for leaders at organizations scaling agentic pilots to production, as surveyed by Forrester Consulting for an in-depth study, “The Agentic AI Readiness Gap,” commissioned by Boomi.
Seven AI Adoption Trends
Forrester identified organizations operating in a state of “agentic chaos,” characterized by low operational readiness, incurring ~USD $2.1M in additional costs. Rushing to scale before establishing the necessary infrastructure often leads to agentic mistakes, which cause operational downtime, customer loss, and severe compliance fines.
To better understand how to navigate this volatile landscape and successfully transition from chaos to control, Forrester examines the specific shifts defining modern AI adoption, covering seven critical trends shaping the industry:
1. Rapid Movement Beyond Pilots Despite Low Trust
86% of leaders surveyed at organizations have moved beyond AI agent pilots. Specifically, 58% are actively transitioning from pilot to production, and 28% are fully in production. However, a significant trust gap exists as only 34% of leaders trust the actions and decisions their agentic systems take. There’s a need to connect agents to reliable, governed data to ensure trust.
2. Increasing Agent Sprawl
Organizations are experiencing rapid expansion of AI agents, with reports of up to 200 agents deployed within a single organization. A primary driver of this sprawl is the difficulty organizations face in connecting these agents to enterprise systems, leaving them unable to execute tasks. Preventing this sprawl requires a unified platform to connect agents to enterprise applications. To effectively curb it, a centralized platform must be established to bridge the gap between agents and enterprise-wide software.
3. Premature Deployment Leading to High Financial Risk
Launching AI agents prematurely can result in heavy financial liabilities. Organizations plagued by “agentic chaos” — a state of low operational readiness — frequently incur millions in additional costs due to compliance penalties, customer loss, and operational downtime. Yet, under intense pressure to demonstrate ROI, 77% of these decision-makers move into full deployment. Establishing an integration foundation mitigates this substantial risk, ensuring that each agent is backed by a properly governed, architected system before scaling.
4. Transition To Integration and API Management
Organizations that achieve “agentic control” prioritize connecting agents to data, apps, and tools through well-managed, reliable APIs rather than focusing solely on enhancing LLM decision-making. Forrester reports that 45% of these advanced leaders won’t even begin an AI agent pilot without dependable APIs in place. Top-tier API management capabilities are essential for enabling agents to take meaningful action.
5. Expanding API Management Into AI Governance
There is a growing trend of expanding traditional API management into a broader, centralized governance control plane. A centralized governance control plane secures connectivity and provides the audit trails necessary for enterprise-grade AI. Leaders with high operational readiness prioritize centralized governance of the Model Context Protocol (MCP) to securely manage and standardize agentic AI connectivity.
6. iPaaS as Core Infrastructure
For leaders with agentic control, using an integration platform as a service (iPaaS) is the primary approach for supporting and orchestrating agentic AI workflows. This critical capability serves as a key technical differentiator, separating organizations in control from those in chaos.
7. Maturity Correlating With Tangible ROI
Successfully accelerating the journey to AI maturity by overcoming the readiness gap unlocks distinct business benefits for organizations that have established effective operational control and infrastructure. In fact, 59% of advanced leaders report substantial improvements in productivity, while 51% note a rise in innovation.
Moving Toward AI-Readiness
Organizations require a unified infrastructure to manage and govern complex AI agents. The Boomi Enterprise Platform brings together applications, data, APIs, and AI agents across on-premises and cloud environments, bridging the AI readiness gap. Boomi provides the orchestration layer and governance to ensure agents are securely integrated, reliable, and capable of driving ROI. With this foundation in place, companies can achieve agentic control and realize the true business value of AI.
Read “The Agentic AI Readiness Gap” for more information and recommendations on how to transition from agentic chaos to control.