PEX Network’s key takeaways:
- New report finds that manufacturers’ artificial intelligence (AI) ambitions are running ahead of their operational readiness.
- Although most manufacturers have made significant investments in automation and are keen to adopt AI, many remain stuck at a mid-level of automation maturity.
- Nearly all manufacturers (98 percent) are exploring AI, yet only 20 percent are fully prepared to deploy it at scale.
AI ambitions are outpacing readiness in manufacturing, according to a new report from Redwood Software.
The Manufacturing AI and automation outlook 2026, based on a global survey of 300 manufacturing professionals, found that while 98 percent of manufacturers are exploring AI, only 20 percent are fully prepared to use it at scale.
The research reveals a growing automation gap in manufacturing. While most manufacturers have invested heavily in automation and are eager to adopt AI, the majority remain trapped in mid-stage automation maturity.
Many automate tasks or processes in individual systems while critical workflows, data flows, and exception handling remain fragmented and manual.
Why do AI ambitions outpace readiness in manufacturing?
AI ambition is widespread across manufacturing, but manual data transfers, script-based automation, and disconnected enterprise resource planning (ERP), manufacturing execution systems (MES), and supply chain systems prevent AI from operating with real-time context, according to Redwood Software.
Seven in 10 manufacturers have automated 50 percent or less of their core operations and only 40 percent have automated exception handling, despite citing it as one of the most disruptive processes. Meanwhile, 78 percent have automated less than half of their critical data transfers, limiting real-time decision-making.
Inventory turns remain difficult to improve, even as automation drives uptime and throughput gains, highlighting the limits of siloed execution. However, 60 percent report reducing unplanned downtime by at least 26 percent through automation.
Manufacturers who focus on orchestrating workflows, data flows, and exception handling across systems are better positioned to move beyond the mid-maturity trap and prepare for AI-driven operations.
“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.”
Even the best AI models and tools can't scale in that kind of execution pipeline, Greene added. A well-orchestrated operation, powered by a service orchestration and automation platform (SOAP), connects fragmented pieces into a single automation fabric, allowing manufacturers to scale automation and AI as they evolve toward autonomous operations.
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Manufacturing firms reduce downtime and operational costs with digital twins
Last year, separate research found that manufacturing firms are leading the way in digital twin adoption. Digital twin tools are increasingly powered by and integrated with AI to create powerful simulations that analyze data, predict outcomes, and automate decisions.
Over 40 percent of manufacturing firms are piloting digital twin technology with full rollouts continuing to emerge, according to the Manufacturing IT/OT Trend Report 2025.
Of the organizations that have used digital twin technology, 65 percent have seen downtime and operational costs reduced. More than half (55 percent) report improved predictive maintenance, while 40 percent have achieved better collaboration. Enhanced planning and efficiency (35 percent) and real-time simulation (25 percent) follow. Just 15 percent saw no measurable impact so far.