Why manufacturing automation has hit a plateau — and what will get it moving

If you lead manufacturing operations or IT today, automation itself probably isn’t your constraint. In many environments, it’s working exactly as intended. Production lines are more stable. Downtime is lower. And automated systems are doing the jobs they were designed to do, often reliably and at scale.

Yet, in my conversations with plant managers, operations leaders and CIOs, a familiar theme keeps surfacing: progress feels harder than it should. Automation initiatives keep getting approved, but then momentum slows. Improvements arrive in pockets rather than end to end.

The data in Redwood Software’s new manufacturing automation research backs that up. Seven in ten manufacturers report automating 50% or less of their core operations. Only about a quarter say they’ve automated more than half.

This isn’t a failure of manufacturing automation or a lack of commitment. What the data points to instead is a structural limitation. You reach a plateau in automation maturity because automation often stops at system boundaries, not because you lack the right tools. Over time, your organization may have built an impressive collection of automation technology, but the connective tissue between those systems never quite materialized. Returns often flatten in this scenario because automation stops compounding, not because it never worked in the first place.

The middle-stage trap

When manufacturers described their automation maturity, the pattern was striking. Nearly half — 47% — placed themselves in the “Managed” stage, where automated processes exist but orchestration is partial. Another 26% identified as “Controlled,” with most tasks automated and orchestration present. Only about 2% described their operations as fully autonomous.

In other words, nearly three-quarters of manufacturers sit squarely in the middle automation maturity stages.

That clustering isn’t random. It reflects a ceiling most organizations hit after automating the obvious, self-contained processes. Early automation wins are straightforward: scheduling jobs, triggering reports, running batch processes, stabilizing equipment routines. These improvements deliver immediate value and reduce human error on the factory floor. But once those gains are captured, what remains is harder. 

The next level of improvement depends on workflows that span multiple systems — ERP, MES, supply chain platforms, quality systems and control systems built around programmable logic controllers. That requires orchestration, not just automation.

The challenge is that middle-stage maturity feels like success because dashboards are green and production rates look healthy. But the manual work hasn’t disappeared; it’s shifted into the gaps between automated processes, where people compensate with spreadsheets, emails and workarounds.

Where automation delivers and why connection matters

Automation delivers its strongest results when applied to processes contained within a single system. The report shows that about 60% of manufacturers have reduced unplanned downtime by at least 26%, with a meaningful share reporting reductions beyond 50%. Uptime, throughput and quality control consistently emerge as areas where automation excels.

These results are real, and they matter. They represent reduced risk, stabilized high-volume operations and improved consistency across production processes.

Challenges tend to emerge when outcomes depend on coordination across systems. 

• Inventory turns remain difficult to improve even as automation improves uptime, highlighting the limits of siloed execution 
• Data accuracy also lags, especially when information must move quickly between planning, execution and supply chain functions using real-time data

Lack of coordination isn’t limited to automation initiatives. Recent McKinsey research shows that broader disruptions — from supply chain volatility to shifting manufacturing footprints — are exposing the same structural weaknesses, where disconnected systems and fragmented decision-making limit performance even in otherwise well-run operations.

You can optimize maintenance schedules inside an MES or improve machining efficiency with CNC and control systems. Those are bounded workflows with clear inputs and outputs. But improving inventory performance requires synchronized data and decision-making across forecasting, production planning, material handling, warehouse operations and supplier networks.

When automation stops at system boundaries, single-system metrics improve, while cross-system outcomes lag. Orchestration addresses this gap by connecting existing automation into workflows that span the entire manufacturing environment.

The top bottlenecks between systems

When we asked manufacturers about their automation challenges, three issues arose most often: 

1. Forecasting accuracy gaps
2. Manual exception handling
3. Lack of integration between ERP, MES and PLM systems 

Together, these account for roughly 66% of reported bottlenecks. What’s notable is what isn’t on that list. Manufacturers aren’t pointing to weak automation technology, but to breakdowns between systems.

Exception handling is a clear example. Only 40% of manufacturers have automated it, even though 22% cite manual exception handling as a top disruption. Exceptions don’t respect system boundaries. A supply delay affects production schedules, inventory positions, customer commitments and financial forecasts simultaneously. Resolving that requires coordinated action across systems, not isolated scripts.

The same pattern appears in forecasting. Forecasts depend on timely, accurate data from many sources. When those systems aren’t connected through event-driven workflows, forecasts rely on stale information. By the time data is reconciled, the window for action has already closed.

These aren’t edge cases. And they persist not because automation has failed, but because automation alone was never designed to solve them.

Fragmented data automation 

Most manufacturers automate inside systems, not between them. The data shows that 78% have automated less than half of their critical data transfers. More than a quarter still move sensitive information through email or manual methods. Nearly 30% rely on scheduled scripts rather than event-driven automation that responds to conditions as they change.

Over time, this fragmentation compounds. Each new automation initiative delivers value in isolation, but also introduces another boundary that someone must manage. Complexity increases and manual handoffs multiply. Each additional project adds less incremental benefit than the one before it.

Manufacturing environments span decades of technology: legacy MES platforms, modern cloud applications, IoT and data collection layers and enterprise systems from multiple vendors. Connecting that landscape requires orchestration that can coordinate workflows across it all, based on events and business rules rather than schedules.

Reframing the challenge

Automation hasn’t failed the manufacturing industry. It has delivered real, measurable value where workflows remain contained. Fixed automation works. Flexible automation works. Individual automation solutions continue to advance.

What needs to change is the focus.

The next phase of automation maturity will be about connecting what’s already automated rather than adding more tools. Exceptions and handoffs — the points where risk and cost accumulate — need to become primary targets for improvement. Workflows must adapt in real time. How well you handle this shift will determine whether your manufacturing automation investment plateaus or continues to scale.

🠆 See a demo of what orchestration could look like using RunMyJobs by Redwood for SAP production planning.

What gets automation moving again

Manufacturers that climb beyond mid-stage maturity share common characteristics. 

• They automate exception handling across systems
• They connect data flows between ERP, MES and supply chain platforms
• They rely on event-driven workflows instead of scheduled scripts

These organizations are also more likely to explore artificial intelligence and machine learning use cases — not as a leap into the unknown, but as a natural extension of orchestrated operations. AI models are only as effective as the data feeding them, and orchestration ensures that data is timely, complete and actionable.

Orchestration changes the question from “What should we automate next?” to “Which workflows still depend on manual coordination?” It shifts success metrics from the number of automated tasks to the reduction of human intervention across the manufacturing industry.

The plateau is real, but it isn’t permanent. Changing your outcomes starts with changing how systems work together.

Get prepared for an orchestrated future now. Download the full “Manufacturing AI and automation outlook 2026” to see how your organization compares — and what it takes to move beyond the middle.