Why most teams stop short of autonomous automation — and what it’s costing them

Finding and implementing automation solutions is no longer the challenge most enterprises face. Data from Redwood Software’s “Enterprise automation index 2026” makes this clear. Investment in automation continues to rise, and the majority view it as mission-critical. Yet, fewer than 6% of organizations have achieved autonomous automation in any core business process. That’s a substantial gap between intent and outcome.

This points to a deeper issue: Many organizations have automated tasks and implemented point solutions, but they haven’t fundamentally changed how work flows across their ecosystems.

Understanding why so many teams stop short of autonomous automation requires looking behind the technology curtain to examine how automation is governed and embedded into the operating model. It’s the accumulation of structural constraints that can quietly but consistently slow progress. These constraints show up less in tooling decisions and more in people and process issues.

Automation advances faster than operating models

If you introduce automation into environments that weren’t designed to support it at scale, your processes will be automated without being restructured. The risk is that ownership stays distributed and decision-making feels unclear.

There’s a practical ceiling you’ll reach in this scenario. Dependencies and exceptions will multiply, because what worked for a handful of workflows is difficult to extend across end-to-end processes. At this stage, automation won’t be slowed by technical limits, but by uncertainty around who can change what, when and under what conditions.

Autonomous automation is driven by shared accountability across IT, operations and the business. That doesn’t mean everyone owns everything, but it does mean no critical process lives entirely within one function’s control. Decisions about logic, exceptions, risk and change management have to be made in the open with a clear operating model behind them. Without that, automation can move quickly in pockets but will always stall when it reaches the seams between teams.

Complexity becomes institutionalized

The report shows that workflow complexity is the most commonly cited barrier to automation adoption. Such complexity is generally unplanned or accidental — the result of years of layered systems and incremental fixes.

Rather than being addressed directly, complexity is often worked around. Teams automate what they can without disturbing upstream or downstream dependencies. Over time, automations inherit the same structural complexity as the environment they operate in. This increases costs and makes change progressively harder to justify.

It also creates a troublesome paradox. You’re introducing automation to simplify execution, but it becomes embedded in architectures that are stuck in the proverbial mud. Autonomous automation depends on the opposite condition: predictable, observable systems designed to adapt without constant intervention.

Governance keeps automation in a holding pattern

As automation’s surface area expands, governance typically becomes more restrictive. Controls are added to reduce risk, but many times without a corresponding increase in transparency or coordination.

In practice, you end up performing cautious automation. Your teams avoid automating processes that cross organizational boundaries because changes require lengthy approvals. The automations you do have may be reliable, but they’re static and siloed.

The research shows that only 10% of organizations prioritize automation adoption at the enterprise level. This can manifest as a focus on preventing failure instead of enabling evolution. Your governance framework should support change in addition to stability.

Utilization plateaus before autonomy emerges

Most organizations own capable automation platforms, but only 27.5% fully utilize them, according to the same study. Underutilization isn’t simply a matter of missing features. It reflects how automation is positioned. Is it treated as a strategic capability or simply supporting infrastructure?

It’s common to only automate what’s immediately visible or urgent, then leave broader opportunities unexplored. You hit a plateau when you continue to do only this, normalizing automation but not expanding its reach. And it’s difficult to overcome without explicit goals tied to utilization and scale.

Autonomy requires confidence and capability

A less visible barrier to autonomy is confidence in automation itself. Many leaders hesitate to allow systems to operate without human oversight, especially when outcomes have financial, regulatory and operational consequences. That’s understandable, but only a true risk if you don’t have strong observability, auditability and recovery mechanisms in place. In which case, you have to default to manual checkpoints.

Redwood’s data suggests that organizations achieving higher levels of automation maturity tend to pair execution with visibility and control. Autonomy becomes possible only when trust in the system is established.

Be among the few that move forward

Those who progress toward autonomous automation behave differently long before they reach it. They treat automation as a coordinated capability, not a collection of tools. And they invest in simplification and accountability across IT, operations and the business — early, not after complexity has set in.

The “Enterprise automation index 2026” provides deeper insight into where most organizations stall and what differentiates those that continue to advance up the ladder of automation maturity. Use this data as a practical lens for evaluating and reworking your organization’s automation trajectory.