ITASCA, Ill. — Maintenance teams in automated manufacturing environments are increasingly turning to AI-driven planning and operational support tools as factories struggle with aging workforces, growing asset complexity and pressure to reduce unplanned downtime.

The shift is driving greater interest in systems capable of embedding AI directly into maintenance operations, helping teams manage expanding volumes of operational data, prioritize repairs and improve asset reliability without significantly increasing headcount.

Ultimo, an enterprise asset management provider owned by IFS, recently expanded its Intelligent Asset Management platform with three AI-powered “digital workers” focused on maintenance planning, technician support and health, safety and environment workflows.

The systems are designed to integrate into maintenance operations through Microsoft Teams and Ultimo’s enterprise asset management platform, automating tasks such as reporting, work preparation and maintenance planning.

Ultimo’s latest Maintenance Trend Report found that 63% of industrial organizations are struggling with aging workforce issues, a challenge increasingly affecting maintenance operations across manufacturing and industrial facilities.

The company said the digital workers are intended to automate repetitive operational tasks while allowing maintenance personnel to focus on higher-level reliability, troubleshooting and operational decisions.

Berkvens Doorsystems, an early user of the technology, said the systems reduced daily reporting and operational analysis time for team leads by 30 to 60 minutes per day.

“With AI, all relevant information is automatically summarized and combined, saving each team lead 30–60 minutes daily during the start of the day and matching our own analysis by more than 95%,” said Stefan van Bussel, team lead technical services at Berkvens Doorsystems.

Ultimo said the systems are designed to operate with human oversight, allowing lower-risk maintenance activities to be automated while escalating higher-risk decisions to maintenance personnel.

The company said additional AI-driven maintenance functions, including autonomous work preparation and shift-log analysis, are expected later this year.