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IT leaders everywhere feel that AI adoption is incredibly wide, but critically shallow. A staggering 88% of organizations are using AI, yet nearly two-thirds of them have not even begun to scale. They are stuck in an endless “experimentation or piloting” phase.

This has created a frustrating AI value gap. We all see exciting cost and revenue benefits at the individual use-case level, but only 39% of organizations report any measurable impact on their enterprise-level EBIT.

Why? Because experimenting with an AI tool is easy. Scaling a trusted, enterprise-wide AI capability is hard. It’s a problem of integration, governance, and strategy.

Now, AI agents – the next great leap – are amplifying this crisis. Sixty-two percent of organizations are already experimenting with agents. But they are bolting them onto the same old, fragmented workflows, creating uncontrolled agent sprawl that mirrors the API sprawl of the last decade. The result is more complexity, more security risks, and more “black box” processes that cannot be scaled.

What AI high performers do differently

The McKinsey report not only diagnoses the problem but also points toward the cure. A small group of “AI high performers” (those seeing EBIT impact of more than 5%) is successfully breaking out of the pilot trap.

Their secret appears to lie not just in using more AI, but in adopting a different approach.

  • They aim for transformation in addition to efficiency: While 80% of companies use AI for efficiency, high performers also set goals for growth and innovation.
  • They fundamentally redesign workflows: This is the key. High performers are 2.8 times more likely to fundamentally redesign their workflows. Their focus moves beyond automating a single task to building dynamic, multi-step agent networks that transform an entire business process.
  • They invest in the foundation: These organizations are 4.9 times more likely to spend over 20% of their digital budget on AI. They are building the plumbing to make AI work at scale.
  • They are actively, personally engaged: High performers are 3.0 times more likely to have senior leaders who demonstrate strong ownership and commitment to their AI initiatives. They are driving the strategy from the top, not just delegating it.

    The “actionability gap” problem

    That plumbing high performers are building is what we at MuleSoft have championed for years: a composable network of APIs.

    Without the ability to take action, an AI agent functions more like a fancy chatbot. An agent’s value is derived not just from talking; its true potential is realized by doing – by checking a student’s enrollment status, updating a citizen’s service request, or processing a credit application. To do anything, they need secure, reliable access to your core business systems. This is the actionability gap: the divide between an agent’s intent and its ability to securely execute a task through a governed API.

    This is where agent sprawl becomes so dangerous. Ungoverned agents trying to access ungoverned APIs is a recipe for disaster. This is how you get massive data breaches, compliance failures, and costly hallucinations that break real-world processes.

    To fundamentally redesign workflows like the high performers, you need a platform that can manage, coordinate, and secure both the agents and the APIs they consume.

    From agent sprawl to a trusted agentic enterprise

    This is precisely why we built MuleSoft Agent Fabric. It is the foundational layer designed to close the AI value gap by turning your collection of uncontrolled agents into a governed, intelligent, and coordinated digital workforce.

    It’s the only solution that manages the entire agentic workflow, from the agent’s intent to the API’s action, with a single, unified platform.

    Discover (Agent Registry): Stops duplicated effort and investment

      • The Problem: Multiple teams are building the same agent to “check shipping status” because they can’t see what’s already been built.
      • The Solution: The Registry provides a central catalog for all agents and APIs. It makes all agentic assets discoverable and reusable, turning duplicated effort into a composable network that accelerates innovation.

      Orchestrate (Agent Broker): Automates complex workflows, moving beyond simple tasks

        • The Problem: Your agents can only perform one simple task, like a password reset. You can’t automate a complex 10-step process, such as “new employee onboarding” or “student financial aid packaging”.
        • The Solution: The Agent Broker is the engine for “redesigning workflows.” It’s an intelligent coordinator that can manage a network of specialized agents to execute complex, multi-step processes – seamlessly connecting HR systems, IT provisioning, and finance.

        Govern (Agent Governance): Provides the enterprise trust layer

          • The Problem: McKinsey identified “inaccuracy” and “cybersecurity” as some of the top risks organizations experience and are working to mitigate. An ungoverned agent could expose PII, be hijacked to launch an attack, or share sensitive data in violation of PCI, HIPAA, or FERPA.
          • The Solution: Agent Governance is your trust layer and the mechanism that enables the human validation process, which high performers rely on. It applies consistent, enterprise-grade policies to every single agent interaction, ensuring they are secure by default. This is how you scale with confidence, not risk.

          Observe (Agent Visualizer): Delivers accountability and performance

            • The Problem: When an AI-driven workflow fails, you have no idea why. This black box problem is exactly what McKinsey calls out as “explainability” – one of the top three risks organizations experience, yet one of the least mitigated. It’s an untraceable, unauditable liability.
            • The Solution: Agent Visualizer provides end-to-end visibility into how your agents make decisions and interact. It’s the accountability layer that allows you to trace workflows, identify bottlenecks, and prove the performance of your AI investments.

            Your first steps to becoming an AI high performer

            The journey to a true agentic enterprise requires a strategic shift, moving beyond the scope of a single project. To break out of pilot mode, you must shift your focus from launching agents to managing them.

            Here are three strategic directives for any leader:

            • Demonstrate leadership commitment: The data is clear: high performers exhibit 3 times more leadership engagement. You can start by mandating a “Governed by Default” strategy for all new AI projects. This is far more than an IT-level rule; it’s a C-suite directive that prioritizes trust as the foundation for scale.
            • Prioritize one transformative workflow: Stop automating simple tasks. Pick one high-value, complex process (like “procure-to-pay,” “student admissions,” or “constituent case management”) and empower a team to fundamentally redesign it with a multi-agent network. This is the shift from efficiency to transformation.
            • Measure outcomes, not just activity: Instead of measuring how many agents you’ve built, measure their impact on your core mission KPIs. Are you reducing application processing time? Are you increasing student retention or citizen satisfaction scores? This is how you prove mission impact and close the AI value gap for good.

              The age of AI agents is here. The only question is whether you will manage the chaos or harness the opportunity.