Almost all manufacturers are exploring AI, but only 20% consider themselves fully prepared to deploy the technology at scale, according to research from Redwood Software. The study of 300 global manufacturing professionals reveals a critical automation maturity plateau, where organizations have invested heavily in operational, engineering and information technology automation yet remain trapped in fragmented execution models that prevent AI adoption.
The research exposes a fundamental disconnect between automation investment and operational readiness. Manufacturers automate tasks within individual systems while critical workflows, data flows and exception handling remain manual, creating execution pipelines incompatible with AI deployment. For technology executives responsible for digital transformation initiatives, this automation gap translates into delayed returns on AI investments as disconnected systems prevent the cross-functional data integration ML models require.
Overcoming Data Fragmentation and Integration Barriers
The data readiness challenge manifests across manufacturing operations where programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, manufacturing execution system (MES) platforms and ERP instances generate disconnected information streams AI models cannot process effectively. Organizations addressing these barriers prioritize service orchestration and automation platforms that connect fragmented automation pieces into unified execution fabrics, enabling manufacturers to scale AI capabilities as they progress toward autonomous operations.
Early AI-driven predictive maintenance and dynamic scheduling adopters report double-digit reductions in unplanned downtime and measurable productivity gains, as autonomous agents automatically order critical parts and schedule maintenance windows when utilization impact is lowest.
Manufacturing AI assistants that merge PLC telemetry, MES throughput and ERP parts lists into contextual alerts enable connected workers to act on predicted asset failures before they occur, with systems routing maintenance tickets to appropriate teams with attached troubleshooting documentation.
Integration architecture determines AI implementation success. Organizations must connect AI platforms with existing PLCs, SCADA, MES and historian systems through APIs and secure connectors that push events into ERP systems while pulling bill-of-materials details. Manufacturing operations should design integration patterns with retry logic and observability capabilities.
The architectural challenge extends beyond technology to organizational readiness. Disparate data sets and the inability to unify information across manufacturing process chains create the most significant barriers to AI deployment. Successful implementations validate AI-generated insights against trusted internal data sources, building operator confidence through pilot programs that demonstrate combined human-AI systems outperform either approach independently.
Technology executives evaluating automation platforms should prioritize solutions offering end-to-end workflow orchestration rather than point automation, with capabilities for exception handling, guided resolution and performance measurement including on-time-in-full tracking. Organizations that automate order-to-cash and procure-to-pay workflows report 85% reductions in manual issue resolution time and 35% faster time-to-cash in high-volume environments.
What This Means for ERP Insiders
Orchestration platforms are becoming mandatory pre-AI infrastructure. Redwood’s research confirms that manufacturers cannot deploy AI at scale without unified automation fabrics connecting fragmented OT, ET and IT systems. This architectural prerequisite creates immediate opportunities for ERP vendors to position orchestration capabilities as foundational modernization layers rather than optional enhancements, while forcing systems integrators to develop competencies in cross-system workflow design before AI implementation services become viable.
Data readiness gaps represent the new implementation bottleneck. The stark exploration-to-readiness ratio reveals manufacturers have moved beyond AI awareness into execution barriers centered on data quality and system connectivity. ERP platform providers must embed data orchestration, validation and lineage tracking as native capabilities rather than expecting customers to resolve fragmentation through third-party tools, while enterprise architects should prioritize data fabric investments ahead of model deployment in transformation roadmaps.
Autonomous operations require human-AI collaboration frameworks. Manufacturing case studies showing double-digit downtime reductions demonstrate that AI value materializes when systems augment rather than replace human expertise, with operators focusing on calibration and exception handling instead of routine monitoring. ERP vendors must design user experiences where AI recommendations surface within existing workflows with clear validation mechanisms, while transformation leaders should measure adoption through operator confidence metrics alongside traditional ROI indicators.
