One factory, two realities: Why manufacturing leadership and the front line see automation differently

One data point from Redwood Software’s Manufacturing AI and automation outlook 2026 stood out: Upper management predominantly sees operations as 51–75% automated. Plant and front-line leaders? They report 26–50%.

Both groups are looking at the same factory. Both are telling the truth. And that’s exactly the problem.

The view from a distance

The further you are from execution, the more automated things look. Dashboards are green. KPIs trend in the right direction. Automated systems do what they were designed to do. From a leadership vantage point, the investment is paying off — and in many ways, it is.

About 6 in 10 manufacturers have cut unplanned downtime by at least 26% with automation, with a meaningful share reporting reductions beyond 50%. Uptime and throughput are improving. Production lines are more stable. These are legitimate, measurable outcomes.

The 51–75% perception reflects what leaders can see:

✅ Individual manufacturing systems performing well 

✅ Investments translating into operational efficiency gains 

✅ The organization trending toward greater stability

That view is inherently scoped to what happens inside those systems. 

Up close, friction comes into focus

Move closer to execution, and the picture changes. Individual platforms may work, but coordination across them — ERP to MES, planning to procurement, quality events to supply chain adjustments — still depends on human intervention.

Front-line teams don’t have to be skeptical of automation to encounter its limits. What looks like a 70% automated operation from a conference room feels closer to 40% when you’re the one bridging systems with spreadsheets because they weren’t designed to talk to each other.

That dynamic shows up clearly in the data. Only 40% of manufacturers have automated exception handling, despite 22% citing it as a top source of disruption. More than a quarter still move sensitive information through email or manual methods. 

Where maturity lives: The space between systems

It would be easy to treat this as a reporting problem: something better dashboards or more shop-floor visibility could close. It isn’t. The gap maps to how automation has been applied — and where it hasn’t.

Most organizations have done solid work automating within systems. ERP processes run as expected. MES workflows are stable. Control systems do their jobs. Those results show up cleanly in dashboards and quarterly reviews, and they’re real.

But no meaningful manufacturing workflow stays inside one system. Forecasting feeds scheduling, production affects inventory, quality events ripple into supply chain decisions. At every one of those handoffs, automation stops and someone picks up the slack.

That’s the 51–75% vs. 26–50% gap in a nutshell. Leadership watches systems perform. Front-line teams manage what happens in between: the timing, the manual data pulls, the spreadsheet that keeps two platforms in sync because nobody built a bridge.

Nearly three-quarters of manufacturers sit in mid-stage automation maturity right now. Tasks are automated, but the workflows connecting them remain only partially orchestrated. Each new automation initiative can make this harder to see. A new initiative makes an individual system more capable, which looks like progress from the top, while the manual stitching between systems stays unchanged and unmeasured.

78% of manufacturers have automated less than half of their critical data transfers. The majority of cross-system execution still depends on how information moves between platforms, not on how well any individual system runs.

This is also why AI readiness remains elusive for most manufacturers right now. If the coordination layer doesn’t exist for your people, it won’t exist for your models. You can’t automate your way to AI-ready if the gaps are structural.

Start with handoffs

The perception split tells you exactly where to look next. Not at the systems themselves, but at the handoffs between them.

The manufacturers breaking through have shifted their focus accordingly. They’re automating exception handling across systems, connecting data flows between platforms and using event-driven workflows instead of scheduled scripts. They’re also 2.7x as likely to have reached the higher stages of automation maturity.

The “Manufacturing AI and automation outlook 2026” breaks down where those coordination gaps show up most often, what high-maturity manufacturers do differently and how the perception divide plays out across roles, systems and KPIs.

See where your organization stands. Read the full report.