The Agentic AI Maturity Gap: Leaders Capture Value

In 2026, agentic AI is here — but value is distributed unevenly. Box's survey of 1,640 IT decision-makers across the US, UK, France, and Japan (companies of 100+ employees, conducted April 30 to May 8, 2026 by The Harris Poll) sends a clear signal: the leading edge is capturing most of the value, and the gap is not about budget — it is about how you choose to operate.

I. The Leading Edge Captures Most of the Value

The core finding: the organizations seeing the strongest returns aren't simply deploying more AI — they operationalize it differently. They integrate AI into multi-step workflows, connect systems to enterprise knowledge, formalize governance, and deploy agents for new categories of work rather than standalone productivity gains for individuals. At the leading edge, AI is becoming a core operational capability that generates measurable business impact today and is beginning to reshape the nature of work.

II. The Maturity Gap: How You Operate Agents

Content, governance, and flexibility are the three foundations of the agentic enterprise — and the places where the gap between the leading edge and everyone else is widest. Leaders don't treat agents as isolated efficiency tools; they make them a cross-system operational capability, connected to proprietary knowledge, governed formally, and deployed to new categories of work. This difference in operationalization is the mechanism by which value divides.

In other words, the same technology produces radically different outcomes in different hands. What decides the outcome is not "how much AI you use," but "how you embed it into process, knowledge, and management."

III. Token Economics: Who Owns the AI Bill

The question of who owns the AI bill has settled around a hybrid model. 45% of organizations take this approach, where IT owns the core infrastructure and the underlying token or compute budget, while business units own application-level spend; 34% centralize the full token and compute budget in IT; 19% put AI budgets entirely outside IT, in individual teams.

The key is the direction of travel: the hybrid share rises from 28% at early stage to 50% at the leading edge. Leaders converge on the model that gives IT the platform and the business the use cases — preserving architectural consistency and control while giving business units room to innovate quickly.

IV. Governance and Incentives: Formal, Not Centralized

Formal structure is already widespread: 40% have AI policies and usage guidelines, 35% have a dedicated team to deploy and manage agents, 34% have defined standards for how agents access company content, 38% tie AI usage to performance reviews and compensation, 39% track token or compute consumption per team, and only 7% apply no incentives at all.

Yet success does not come from centralization. Only about 18% credit their most successful AI work to a central AI or data-science team; most successes come from central and business-unit collaboration (27%) or the IT function (27%). Formal structure plus distributed execution is the leaders' shared recipe.

V. What It Means for Your Enterprise

For decision-makers, the action is clear: upgrade agents from individual tools to operational capabilities. Design measurable multi-step workflows, connect AI to proprietary enterprise knowledge, establish formal governance and identity controls, and converge on a cost structure where IT provides the platform and the business owns the use cases.

The maturity gap is not a budget gap — it is an operational-choice gap. The dividing line between leaders and everyone else is whether you run agents as a core capability that changes the nature of work. The enterprises acting today are designing everyone else's future.

References:

• Box: Agentic AI Is Here — The Leading Edge Is Capturing Most of the Value (State of AI in the Enterprise 2026)
• Box: The Maturity Gap — Leaders Operationalize Agents Differently
• Box: State of AI in the Enterprise 2026 (Full Report)