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A Breakthrough in Facility Reliability

Picture nearly 6.5 million square feet of critical care, research labs and teaching spaces. That’s Rush University Medical Center’s campus in Chicago’s West Side. For decades, the in-house team and JLL struggled with reactive workflows, siloed data and knowledge vanish­ing when an engineer left. This maintenance intelligence case study shows how AI can capture that know-how, structure it and surface answers exactly when you need them.

In under a year, JLL’s traditional CMMS went from a simple work-order tool to a foundation for true predictive insight. By layering in iMaintain’s AI-first maintenance intelligence platform, Rush bolstered patient safety, boosted asset uptime and freed clinical teams from paperwork. Curious how it all fit together? Read our maintenance intelligence case study with iMaintain — The AI Brain of Manufacturing Maintenance to see the full story.

The Challenge: Complex Campus, Fragmented Data

Rush University Medical Center isn’t a single building. It’s a network of towers, labs, clinics and support facilities. Managing life-safety, HVAC, electrical and plumbing across shift changes quickly turned into tunnel vision:

  • Limited visibility – Before Corrigo CMMS, teams logged work on scraps of paper or spreadsheets. No single source of truth.
  • Reactive firefighting – Engineers spent time hunting past fixes instead of focusing on root causes.
  • Knowledge loss – Critical troubleshooting steps lived in notebooks or experienced minds, vanishing when staff rotated.

JLL’s zone-rounding initiative generated 3 210 work orders in one sweep. It was a step forward, but the data remained raw. Handheld devices gave real-time updates, yet nobody had an intuitive way to learn from each service call. The result? Repeated breakdowns, longer repair times and stressed clinicians.

Making CMMS Smarter

Corrigo brought order — easy submission, end-to-end tracking and dashboards. But dashboards alone don’t solve what you don’t know. Enter iMaintain. By linking to Corrigo’s data, iMaintain knit together work history, asset context and human experience. You end up with:

  • Structured intelligence – Every fix, every check, every inspection becomes searchable wisdom.
  • Context-aware support – Engineers see proven remedies in seconds, with confidence metrics.
  • Growth over time – Each repair adds new insights, making your system more reliable every day.

Curious how this integration works in a real factory or facility? Explore how iMaintain works.

Introducing iMaintain: AI Built to Empower Engineers

iMaintain isn’t a black-box prediction tool. It’s a human-centred layer on top of your existing maintenance processes, designed to:

  • Capture operational know-how straight from engineers – no extra forms.
  • Surface relevant insights at the point of need – no more guesswork.
  • Prevent repeat failures – consistent fixes backed by data.
  • Build trust – AI suggestions explain why they matter, with links to past work orders.

Key features include:

  • Natural language search – ask “How did we fix the chiller fault last April?” and get precise steps.
  • Intelligent workflows – guided checklists that adapt based on asset history.
  • Custom metrics – track reliability improvements, knowledge growth and team adoption.
  • Seamless CMMS integration – works with Corrigo, Maximo, SAP PM and more.

All this on a platform built specifically for busy maintenance teams. Want to see it in action? Schedule a demo with our team.

Real-Time Impact at Rush

Adding iMaintain to Corrigo didn’t require ripping out legacy systems. It simply amplified them:

  1. Proactive Rounding – Zone teams log work orders via Corrigo handhelds. iMaintain bundles those notes into searchable knowledge.
  2. Speedy Troubleshooting – Engineers follow AI-suggested fixes with built-in success rates. Average repair times dropped by 25 percent.
  3. Trend Analysis – Business intelligence tools identify recurring faults. iMaintain then suggests preventive tasks before failures hit.
  4. Team Collaboration – With all knowledge in one place, senior and junior engineers learn from the same source, reducing training times.

By the end of year one, Rush saw:

  • 30 percent fewer repeat failures.
  • 20 percent reduction in mean time to repair (MTTR).
  • Enhanced compliance – always “survey ready” for regulatory audits.
  • Freed clinicians – maintenance handled before a single patient notice.

At the halfway mark, the facility leadership decided to go deeper. Explore real world applications.

Quantifiable Results and Next Steps

The real proof comes from numbers:

  • Downtime Reduction – Over 1 000 hours of equipment availability gained.
  • Repair Efficiency – MTTR improved by nearly 18 percent.
  • Knowledge Retention – Zero loss of critical fix data, even after staff turnovers.
  • Energy Collaboration – Data from 110 ECM projects (LED, steam, chiller upgrades) fed back into iMaintain, creating playbooks for future energy work.

These results laid the groundwork for predictive ambition. Instead of chasing alerts, Rush’s reliability team now builds asset health models on a foundation of clean, structured data.

Ready to bring data-driven maintenance to your site? View pricing plans or Talk to a maintenance expert for a custom roadmap.

Building a Knowledge-Driven Future

Rush didn’t stop at machinery. They extended the approach to people:

  • Local 399 Union teams adopted a building-based model. Engineers now own a campus zone end-to-end, with iMaintain tracking performance.
  • The Pathways internship welcomed high-school students into facilities. By logging each student’s support tasks, iMaintain became a training library for new talent.
  • Awards and recognition – Multiple Healthcare Pillar Awards for patient experience, DEI and financial performance, driven by streamlined maintenance.

This maintenance intelligence case study proves that sustainable reliability starts with capturing what you already know. AI then layers on top, guiding actions and turning everyday fixes into lasting wisdom.

Testimonials

“Switching to iMaintain was a game-changer for our facilities team. We went from firefighting to forecasting in weeks, and our engineers trust the AI prompts because they’re built from our own data.”
– Sarah Patel, Reliability Lead, Rush University Medical Center

“iMaintain’s search feature is our secret weapon. I literally typed ‘chiller leak’ and got the exact steps from last November. No more sifting through dusty binders.”
– Miguel Rodriguez, Senior Mechanical Engineer

Conclusion

This maintenance intelligence case study at Rush University Medical Center highlights how a human-centred AI platform transforms reactive struggle into proactive success. By layering iMaintain on top of Corrigo, the team captured knowledge, slashed downtime and freed clinical staff to focus on patients. It’s proof that meaningful maintenance innovation respects your workflows, preserves your expertise and builds resilience for the future.

Ready to see what AI-driven maintenance intelligence can do for you? Read our maintenance intelligence case study with iMaintain — The AI Brain of Manufacturing Maintenance.