According to new research commissioned by Boomi and conducted by Omdia, a global analyst and advisory firm under Informa TechTarget, Inc, agentic AI has crossed a decisive threshold. In this blog, we’ll examine just what that means and highlight several key metrics the research uncovers.
AI Is No Longer A Pilot Project
AI and agentic transformation are now board-level mandates, reshaping how enterprises operate. 94% of organizations rate agentic AI as a high or critical priority, and 34% have elevated it above every other technology initiative on their list. C-suite executives are personally involved in identifying use cases at 75% of organizations. IT leadership participates at 80%. This is not just grassroots experimentation.
But there is a growing gap between ambition and execution. The research surveyed 300 IT and AI leaders across the US, UK, Germany, Australia, and Singapore — and the findings are not ambiguous. Organizations are not struggling to build AI agents. They’re struggling to scale them. And the barriers are not where most people expect.
The Deployment Surge Is Already Underway
Enterprise AI adoption has matured rapidly. Nearly half of surveyed organizations (47%) are already scaling and optimizing AI solutions across their business — well past the prototype stage. That maturity has created the conditions for agentic AI to take hold. These are not organizations experimenting with chatbots. They are deploying autonomous systems that approve transactions, trigger workflows, and make real-time decisions across enterprise functions.
Business use cases span the entire organization. Operational efficiency leads the list of strategic drivers at 59%, but digital transformation (56%), revenue growth (54%), and customer experience enhancement (51%) are all close behind. Unlike earlier AI deployments — typically a single model serving a single function — a single-agent workflow today might span CRM, ERP, billing, and knowledge management in a single execution. That cross-functional reach is precisely what makes agentic AI so powerful. It’s also what makes it so difficult to scale.
Early results are encouraging. Among organizations running agents in production, cybersecurity and personalization use cases are delivering results that exceed initial expectations. But there is an outlier: data-driven decision-making, where only 35% of organizations achieved their goals against a 50% expectation. And as the research makes clear, the culprit isn’t model failure — it’s infrastructure failure.
Integration: The Hidden Bottleneck
The structural barrier to agentic AI scaling is that to do their jobs, agents need live access to enterprise systems. A predictive model can run on a batch export of data. An agent cannot. It needs to act in real time across the system, which requires deeper integration that most enterprise infrastructures were never designed to provide.
These numbers make the challenge concrete:
- 96% of organizations say their agentic AI use cases require connectivity to enterprise systems, databases, or third-party services
- ~30% of agentic AI use cases are currently stalled due to integration bottlenecks
- 97% expect API usage to increase significantly over the next 6 to 12 months as a direct result of agentic AI adoption
When organizations were asked what capabilities are most essential for scaling agentic AI, the top answers were not about model sophistication. They were secure API access (53%), enterprise system integration (52%), and robust API management (47%). The connective tissue between agents and enterprise systems — not the intelligence layer — is the primary constraint.
The data quality dimension compounds this further. AI agents are only as reliable as the data they act on, especially when they act autonomously. Data quality and consistency issues top the list of barriers at 29%, followed by legacy infrastructure challenges (25%), security concerns that limit data sharing (24%), and data silos (21%). As the research points out, an agent that decides across CRM, ERP, and billing inherits every data inconsistency from every system it touches — and acts on all of them at machine speed.
Governance Is Losing the Race Against Adoption
The second structural challenge is governance — and it is arguably the more urgent. Agents are being deployed across enterprises faster than the frameworks to oversee them can be built. More than half of organizations (56%) acknowledge this directly, saying agents are deployed faster than they can establish proper governance frameworks. The same share reports compliance gaps due to rapid adoption.
Shadow AI — autonomous agents operating outside formal IT governance — is not a fringe problem. It’s the norm. Nearly all enterprises (90%) already have agents running outside formal IT governance structures, and 28% have agents operating with no governance framework at all. The average estimated financial exposure from this gap is approximately $5 million per enterprise, driven by compliance failures, security breaches, and operational disruptions.
There’s also a significant and telling perception gap within organizations. Executives are roughly twice as likely as management to strongly agree that governance is a serious problem — whether on the pace of deployment (32% vs. 18%), security vulnerabilities (32% vs. 19%), or compliance gaps (30% vs. 15%). Builders trust the technology they can see working. Executives are accountable for the consequences when it doesn’t. Bridging that gap requires governance built into the orchestration platform from day one, not retrofitted after agents are already in production.
What Enterprises Need: A Single Unified Platform Built for Orchestration
The research is clear about what organizations want from their AI infrastructure — and equally clear about what they’re not willing to accept. Two-thirds of organizations (68%) prefer a single integrated platform that combines AI agent development, governance, API management, and data integration. Among those who prefer a unified platform, 98% say that unification is important or extremely important.
The top worries about a single-vendor platform are limited flexibility (38%), vendor lock-in risk (37%), and gaps in specialized functionality (35%). This concern is more acute for agentic AI than for most enterprise technology decisions because the underlying technology — large language models, agent frameworks, and orchestration standards — is evolving rapidly. Organizations building agent systems today may be running entirely different models and tooling twelve months from now.
What they need, and what the research consistently points to, is orchestration: the ability to coordinate agents, APIs, data, and governance into reliable business outcomes across a heterogeneous, evolving enterprise IT landscape. Organizations already running agents in production understand this viscerally. They prioritize unified visibility into every agent-to-system interaction at nearly twice the rate of organizations still in the planning stage (49% vs. 27%).
The Takeaway: Infrastructure Is the Competitive Differentiator
The Omdia research arrives at a conclusion that should reframe how enterprises approach their agentic AI investments. The intelligence layer — the models, the reasoning capabilities, the AI itself — is ready. What is holding organizations back is everything beneath it: integration, data connectivity, API management, and governance.
Organizations that solve the infrastructure problem now will scale faster, govern more effectively, and adapt as the technology evolves. Those that do not will find their agents stalled, their data unreliable, and their governance exposure growing. The race to agentic AI scale is not won by building smarter agents. It’s won by building orchestration capabilities that let those agents act — reliably, securely, and at enterprise scale.
Boomi Can Help
Boomi, as the leading integration, automation, and data activation platform, is well-positioned to address the challenges identified in Omdia’s research regarding the transition to agentic AI.
- Boomi provides a comprehensive integration platform that enables seamless connectivity between various systems, applications, and data sources. This helps organizations quickly integrate AI agents with existing enterprise data.
- Boomi’s API management capabilities allow organizations to create, manage, and secure APIs easily, facilitating the integration of AI solutions with enterprise systems.
- Boomi includes governance features that help organizations maintain control over their integrations and data flows. This includes monitoring, auditing, and compliance capabilities.
- Boomi Agentstudio, part of the Boomi platform, is a full agent lifecycle management solution that empowers organizations to simply build, govern, and orchestrate all AI agents at scale.
To learn more, download the full report, Why Scaling Agentic AI Demands a New Approach to Integration and Orchestration.