Why Intelligence Without Authority Cannot Deliver Enterprise Value

At the end of 2025, AI was everywhere. McKinsey reported that nearly 90 percent of organizations now use AI in at least one business function, and a majority are experimenting with agentic systems. Yet the same research shows that fewer than one-third have scaled AI beyond pilots, and fewer than 40 percent report any measurable enterprise-level financial impact. MIT’s research paints an even starker picture. Despite tens of billions of dollars invested in generative AI across large enterprises, the overwhelming majority of organizations are seeing little to no return.

This disconnect is not about model quality, data availability, or compute power. It is about authority.

Enterprises have built intelligence layers that can observe, predict, and recommend, but they have stopped short of allowing AI to execute decisions end-to-end. As a result, AI accelerates fragments of work while leaving the system itself unchanged. Task-level productivity improves, but enterprise-level value remains elusive.

See also: 5 Defining AI and Real-Time Intelligence Shifts of 2025

How AI Became an Observer Instead of an Actor

Most enterprise AI deployments today follow the same pattern. Models analyze data, surface insights, generate recommendations, and present them to humans. From there, the work slows down. Humans review outputs, seek approvals, reconcile conflicts, and manually push actions through legacy systems.

This design made sense when AI capabilities were immature. It makes far less sense now.

The MIT Sloan Management Review’s 2025 research highlights that AI initiatives stall not because organizations lack insight, but because decision-making structures remain rigid, sequential, and human-centric. AI is often bolted onto workflows designed decades ago, workflows optimized for manual intervention and layered approvals. In these environments, intelligence becomes ornamental rather than transformational.

Enterprises have effectively turned AI into a highly sophisticated observer. It can see everything, but it cannot act without permission.

See also: Adaptive Edge Intelligence: Real-Time Insights Where Data Is Born

Why Pilots Plateau

This structural constraint explains why so many AI initiatives look promising in pilots but fail at scale.

In pilot environments, the volume of decisions is manageable. Humans can review recommendations and intervene without creating bottlenecks. At enterprise scale, that model collapses. Decision queues grow. Approval chains slow execution. The very humans meant to be “in the loop” become the limiting factor.

Gartner captures this tension clearly. In a January 2025 poll, only 19 percent of organizations reported making significant investments in agentic AI, while 31 percent said they were waiting or unsure. Gartner predicts that more than 40 percent of agentic AI projects will be canceled by the end of 2027, not because autonomy lacks promise, but because organizations underestimate the operational complexity of deploying it without redesigning workflows.

AI pilots stall because enterprises treat execution as a human responsibility even when the decision is repeatable, low risk, and time sensitive. Intelligence without authority cannot compound.

Decisions Are Not All the Same

The autonomy debate often fails because it is framed as all or nothing. Either AI acts independently, or humans remain fully in control. That framing is flawed.

Not all decisions deserve the same execution model. High-impact, irreversible decisions require human judgment and accountability. High-frequency, low-risk decisions do not. Yet many enterprises force both into the same approval-heavy process.

MIT researchers emphasize that decision design matters more than algorithmic accuracy. When organizations fail to distinguish between decision types, they either over-automate sensitive judgments or under-automate routine work. Both outcomes destroy value.

The goal is not blanket autonomy. The goal is calibrated authority. AI should execute where speed, scale, and consistency matter most, while escalating decisions that require context, ethics, or strategic trade-offs.

What End-to-End Execution Actually Looks Like

When AI is trusted to execute within defined boundaries, workflows change fundamentally.

Instead of generating insights that wait for action, AI systems sense conditions in real time, make decisions based on predefined rules and objectives, execute actions across systems, and learn from outcomes. Humans shift from execution to orchestration.

This is not theoretical. Gartner predicts that by 2028, 15 percent of day-to-day work decisions will be made autonomously, up from virtually zero in 2024. They also forecast that one-third of enterprise software applications will embed agentic AI by that time.

The critical distinction is not autonomy versus control. It is closed-loop versus open-loop systems. Open-loop systems inform humans and wait. Closed-loop systems act, measure, and adapt. Enterprise value emerges when AI closes the loop.

Trust Is the Wrong Starting Point

Much of the conversation around AI autonomy centers on trust. Can we trust AI to make decisions? Should humans always remain in the loop?

These questions are important, but they are misplaced. Trust is not a prerequisite for autonomy. It is an outcome of good system design.

Research from Stanford and MIT shows that human-AI systems perform best when roles are clearly defined, and execution authority is explicit. When AI is confined to advisory roles in environments where speed matters, humans either over-rely on recommendations or ignore them entirely. Both behaviors reduce effectiveness.

Effective systems do not rely on blind trust. They rely on constraints, auditability, reversibility, and escalation rules. Autonomy becomes safer when it is bounded.

Legacy Systems Are Not the Real Barrier

One of the most persistent myths in enterprise AI is that legacy systems must be replaced before autonomy is possible. Rimini Street’s research on enterprise IT investment challenges this assumption.

Their 2025 survey shows that most large organizations continue to run core operations on long-standing ERP and industry platforms, and that wholesale replacement is neither economically viable nor operationally prudent. Yet these same organizations are under pressure to modernize.

The path forward is not ripping and replacing systems of record. It is introducing systems of action that sit above them. Agentic AI can orchestrate workflows across legacy environments without rewriting the underlying platforms. What must change is not the core system, but the execution model layered on top of it.

This distinction matters heading into 2026. Organizations that wait for perfect architectures will wait indefinitely. Organizations that redesign decision authority will move faster with what they already have.

From Insight Economies to Execution Economies

Enterprises are entering a new phase of competition. Insight is no longer scarce. Execution is.

McKinsey’s research shows that AI high performers are not distinguished by better models, but by their willingness to redesign operating models, assign clear ownership, and embed AI into core processes. These organizations pursue growth and innovation objectives alongside efficiency, and they invest accordingly.

The next generation of enterprise advantage will not come from knowing more. It will come from acting faster and more consistently than competitors. That requires letting intelligence cross the boundary into execution.

Intelligence Is Cheap. Authority Is Scarce.

AI will not fail because it cannot think. It will fail because enterprises refuse to let it act.

Pilots, copilots, and dashboards will continue to proliferate. They will continue to impress. But without execution authority, they will not transform businesses.

The question for leaders in 2026 is not whether AI works. That debate is over. The question is where AI is allowed to decide, where it is allowed to act, and where humans add the most value.

Until enterprises answer those questions honestly, AI will remain powerful, expensive, and operationally sidelined.