Manufacturers are automating more than ever—and still falling short.
That’s the central tension revealed in Redwood Software’s “Manufacturing AI and Automation Outlook 2026,” a new global study based on a survey of 300 manufacturing professionals conducted by independent research firm Leger Opinion. The findings point to a widening automation gap: while AI ambitions are sky-high and investments in OT, ET, and IT automation are well underway, most manufacturers remain stuck in the middle of the maturity curve.
In other words, factories are automated—but not orchestrated. And that distinction may determine who can actually scale AI in the next wave of industrial transformation.
AI Is Everywhere—Readiness Is Not
The headline number is striking: 98% of manufacturers are exploring or considering AI-driven automation. Few technologies in industrial history have seen that level of near-universal interest.
Yet only 20% say they feel fully prepared to use AI at scale.
That gap highlights a growing realization across the sector: AI is only as effective as the execution environment it operates in. Without clean, connected workflows and reliable data flows, even the most advanced models struggle to deliver value beyond isolated use cases.
The study suggests many manufacturers are discovering this the hard way—after automating aggressively within individual systems, but failing to connect those systems into an end-to-end operational fabric.
The Mid-Maturity Trap
Redwood’s research shows that seven in ten manufacturers have automated 50% or less of their core operations. Automation exists, but it’s uneven, fragmented, and often brittle.
Most progress has been made within discrete environments—plant-floor automation, ERP scheduling, or engineering systems. The trouble begins at the boundaries, where processes cross systems, teams, or organizations.
That’s where automation tends to stall.
Key indicators of this stall include:
- Only 40% have automated exception handling, even though manufacturers rank it among the most disruptive operational challenges
- 78% have automated less than half of their critical data transfers, slowing real-time decision-making
- Manual scripts and point integrations still dominate cross-system coordination
- Inventory turns remain stubbornly difficult to improve, despite gains in uptime and throughput
The result is a manufacturing operation that looks automated on paper, but still depends heavily on human intervention to manage handoffs, resolve exceptions, and reconcile data across systems.
Automation Delivers—But Only Up to a Point
The report makes clear that automation investments are paying off—just not as broadly as manufacturers hoped.
60% of respondents report reducing unplanned downtime by at least 26% through automation, a meaningful operational win. Throughput and asset utilization have also improved in many environments.
But these gains are increasingly constrained by siloed execution. Improving one system doesn’t automatically improve the entire value chain. Inventory optimization, supply chain responsiveness, and end-to-end agility remain elusive when workflows stop at system boundaries.
This explains a paradox many manufacturers are experiencing: plants run faster, but businesses don’t necessarily run smarter.
Why AI Struggles in Fragmented Operations
AI ambition is clearly not the problem. Execution readiness is.
According to the research, manufacturers face three structural barriers that prevent AI from operating with real-time context:
- Manual data transfers that delay or distort operational signals
- Script-based automation that is brittle and difficult to scale
- Disconnected ERP, MES, and supply chain systems that fragment decision-making
AI thrives on continuous, reliable inputs. But when data is delayed, incomplete, or manually reconciled, AI models are forced to operate on stale context—or not at all.
That’s why Redwood argues that AI adoption without orchestration simply reinforces existing inefficiencies, rather than eliminating them.
Orchestration as the Missing Layer
Redwood’s core argument is that manufacturers aren’t failing at automation—they’re running into its natural limits.
“Manufacturers aren’t failing at automation—they’re hitting the limits of siloed execution,” said Kevin Greene, CEO of Redwood Software. “They have powerful automation across their enterprises, but it operates in fragmented workflows, slowed by friction at handoffs, unmanaged exceptions, and delayed or unreliable data flows.”
According to Greene, even best-in-class AI tools cannot scale under those conditions. What’s required is orchestration: the ability to coordinate workflows, data flows, and exception handling across systems, not just within them.
This is where Redwood positions its Service Orchestration and Automation Platform (SOAP)—including its RunMyJobs platform—as the connective tissue that turns isolated automation into an “automation fabric.”
Evidence From the Field
The study includes one telling comparative data point: Redwood customers are 2.7 times more likely to be in mid-to-high stages of automation maturity.
While the report is not a product benchmark, the implication is clear. Manufacturers that prioritize orchestration—rather than just task automation—progress further and faster toward AI readiness.
These organizations focus less on automating individual steps and more on ensuring that entire processes can execute autonomously, handle exceptions intelligently, and adapt in real time.
Why Exception Handling Matters More Than Ever
One of the report’s most consequential findings is how poorly exception handling is automated—and how much it matters.
Exceptions are where real-world manufacturing diverges from ideal process maps: late shipments, quality issues, machine failures, regulatory holds. They are also where human labor is most heavily concentrated.
With only 40% of manufacturers automating exception handling, most AI initiatives are effectively blind to the very moments that determine operational outcomes.
Orchestrated automation changes that dynamic by allowing exceptions to trigger coordinated responses across systems—rerouting workflows, adjusting schedules, and escalating decisions with full context.
Without that capability, AI remains confined to optimization at the margins.
The Autonomous Enterprise Is a Systems Problem
The concept of the autonomous enterprise—operations that can sense, decide, and act with minimal human intervention—has become a common north star in manufacturing. But Redwood’s research suggests autonomy is less about intelligence and more about integration.
Autonomy requires:
- Real-time data across systems
- Automated decision execution, not just insights
- Built-in exception handling
- End-to-end visibility and control
These are orchestration challenges as much as AI challenges.
As manufacturers push toward AI-driven operations in 2026 and beyond, the report argues that orchestration—not individual tools—will determine how fast they can move without sacrificing quality, compliance, or resilience.
A Reality Check for 2026 Planning
For manufacturing leaders, the takeaway is both sobering and actionable.
AI adoption is inevitable. Automation investment is already substantial. But without addressing fragmentation at the workflow and data level, most organizations will remain stuck in what Redwood calls the mid-maturity trap—too automated to be flexible, too manual to be autonomous.
The path forward is not more automation in isolation, but better-connected automation.
As the report makes clear, AI doesn’t replace orchestration. It depends on it.
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