Why AI agents still outrun the reality of enterprise ambition – News

Measuring the maturity curve

AI agent innovation cycles are accelerating faster than most enterprises can track. Leaders need more than anecdotal benchmarks to understand their position and the gaps they can’t yet see. The five dimensions measured in the “Agentic AI Futures Index” include:

1. Digital Labor Transformation — Tracks how organizations are integrating digital coworkers and reshaping workforce strategies.
2. Adoption Maturity — Charts the journey from pilots to enterprise-wide agentic workflows.
3. Trust and Governance — Assesses explainability and accountability frameworks.
4. Reasoning and Decision Intelligence — Measures the shift from task execution to judgment-based work.
5. Causal AI Innovation — Explores whether systems can move beyond pattern recognition to understand cause and effect.

Each dimension is scored on a 0-5 maturity scale based on responses from 61 questions submitted by 625 qualified AI professionals across 13 industries. Respondents were screened for direct involvement in AI strategy, development or governance, ensuring the data reflects practitioners actively shaping the field rather than observers speculating about it.

“Together, these indices define a strategic maturity curve that helps organizations craft AI strategies, showcase solution leadership, benchmark progress against peers and anticipate the technologies that will shape the next decade of innovation,” Hebner explained. 

The Digital Labor Transformation Futures Index — theCUBE Research

The execution deficit behind agentic AI

Enterprises aren’t lacking conviction. More than 90% of leaders surveyed for the “Agentic AI Futures Index” see digital labor — and agentic AI more broadly — as inevitable, and conviction is highest among those with the most direct AI experience, according to the Index. Digital coworkers are essential solutions to talent shortages, rising costs and competitive pressure, not optional innovations.

This pattern shows up consistently across the Index: Leaders express strong conviction in agentic AI, but organizations struggle to translate that vision into real-world execution. The “Digital Labor Transformation Index”, part of the overall “Agentic AI Futures Index,” illustrates the point. Its overall maturity score sits at 3.1 — evidence that organizations have begun to move past experimentation, but not enough to signal readiness for scaled digital coworkers. Collaboration between human resources and IT is forming, and early strategies are taking shape. Targeted use cases are emerging, but execution continues to fall short.

“Right now, it’s still considered a technical implementation, but this is really a business play,” said Christophe Bertrand, principal analyst at theCUBE Research. “Without cross-organization collaboration, things can head to the wall very quickly because there’s only so much you can ask IT to do for you.”

Even with that conviction, operational readiness remains out of reach. Within the “Digital Labor Transformation Index,” aspirations score 4.1 on the maturity scale, strategy drops to 3.1, and execution falls to just 1.8. That pattern signals a structural gap between what organizations envision for digital labor and what they can actually deliver today, echoing the broader vision-to-value gap seen across the “Agentic AI Futures Index.”

No single failure drives that gap. Many organizations still treat agentic AI and digital coworkers as technical implementations owned by IT rather than as workforce transformation initiatives that demand shared accountability. Strategy scores show that investment and planning are underway, but execution scores reveal that the collaborative structures, governance and change management required to operationalize digital labor at scale remain immature.

“These are the signs of initiatives in their infancy,” Bertrand said. “We’re still putting the foundations in place, and we’re years from where we want to be.”

The cultural dimension is equally critical. Human resources is increasingly involved in shaping how enterprises integrate AI agents as digital coworkers rather than as replacements for human workers. This shift reframes agentic AI as workforce evolution rather than pure automation.

“If you’re not at the cadence of speed, culture is the game,” said John Furrier, co-founder and co-chief executive officer of News Media Inc., during the AI Agent Builder Summit. “It’s the only game in town, because if you don’t have the speed, you don’t win. If you’re not in the game, on the field with AI, you’re going to lose to somebody else who’s going to be faster.”

AI agents: Where investment meets skepticism

The “Agentic AI Futures Index” highlights the accelerating investment in AI reasoning and decision intelligence. Seventy-three percent of enterprises are making significant or strategic commitments to capabilities that move beyond automation into judgment-based work, and investment maturity scores 3.8 out of 5 – the highest of any dimension measured.

Agentic AI Trust and Governance — theCUBE Research

But confidence hasn’t kept pace with spending. Only 49% of leaders interviewed express high confidence that AI agents can make trustworthy, accurate decisions. The Index’s trust scores, 2.4 out of 5, are the lowest across all measured dimensions. The gap between investment and confidence is where ROI stalls, according to Paul Chada, chief executive officer of Doozer.ai. 

“A prediction is not a decision,” Chada said. “You can only trust an agent when it shows that it understands the goal, the context and the consequences of its actions.”

The tension between belief and doubt plays out in real deployments. Enterprises range from expecting agentic AI to solve everything to deep skepticism that it’s ready for production, according to Michael Garas, AI partnerships leader at IBM Corp.

“I think the top-of-mind concern for enterprises is how to make sure AI agents have the context that they need,” Garas said during the AI Agent Builder Summit.

Generative AI identifies patterns and correlations, but decision intelligence requires systems to understand cause and effect, Chada noted. As enterprises move from prediction into judgment-based work, the trust mechanisms that supported automation no longer apply. Explainability, human collaboration and the ability to evaluate trade-offs become essential to validating an agent’s decisions.

“Organizations are just wanting to run [AI agents] in parallel right now,” Chada said. “As they see it, make the correct decision repeatedly [and] trust builds naturally; that’s when autonomy becomes acceptable.”

These tensions are why trust is emerging as the real gatekeeper of enterprise adoption. The “Agentic AI Futures Index” revealed that organizations are willing to invest and eager to experiment, but autonomy only advances at the speed of confidence. As AI agents take on work with real consequences, the ability to understand and verify their decisions will shape how quickly enterprises move into the next phase of this shift.

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