Transforming Maintenance: KBR’s AI-Powered Leap
When routine breakdowns and siloed know-how were costing KBR hours of downtime, they turned to a new approach. Instead of chasing perfect predictive algorithms from day one, KBR captured the very wisdom of their engineers. This case study shows how AI empowering engineers can reshape maintenance by turning day-to-day fixes into a shared, evolving asset.
By embedding iMaintain’s AI maintenance intelligence platform, KBR bridged reactive firefighting and true predictive insight. Engineers now see context-specific fixes at the push of a button. Supervisors can track progression metrics in real time. It’s a classic example of AI empowering engineers where human expertise meets smart automation—delivered fast, without ripping up existing systems. Discover how this practical shift can work for you: AI empowering engineers: iMaintain — The AI Brain of Manufacturing Maintenance.
The Challenge: Fragmented Knowledge and Repetitive Repairs
KBR’s in-house maintenance teams were masters of their craft, but their know-how was trapped in notebooks, emails and ageing spreadsheets. Every shift change became a mini fire drill:
- Engineers spent hours re-diagnosing faults
- Historical fixes lived on scattered paper trails
- Critical insights walked out the door when teams rotated
This meant longer MTTR, more unplanned downtime and a reliance on senior staff to solve the same problems over and over. The real cost? Lost productivity—and frustration on the shop floor.
Why Generic Engineering Platforms Fall Short
KBR initially explored a popular engineering automation platform. It offered a Python environment and let coders whip up tools in weeks. Great for general apps, sure. But it missed the mark for maintenance:
- No built-in asset-specific context
- Lacked structured fault histories
- Required developer skills for basic workflows
In short, it treated maintenance like any other engineering challenge. That’s brilliant for creating new process-design apps, but less so for day-to-day troubleshooting. KBR needed more than a sandbox—they needed a system purpose-built to capture and share maintenance know-how. The result? They complemented their chosen platform with a leaner, human-centred solution designed for real maintenance teams.
How iMaintain Bridges the Gap
Enter iMaintain, the AI maintenance intelligence platform built from the ground up for UK manufacturers. Here’s how iMaintain turned KBR’s maintenance ship around:
- Knowledge Capture
Operators log every repair, every root cause, every tweak. iMaintain structures this data automatically. No extra admin effort. - Context-Aware Decision Support
At the point of need, engineers see proven fixes, relevant assets and past investigations—all in one pane. - Seamless Integration
It sits on top of existing CMMS or spreadsheets, smoothing out data gaps without demanding a forklift upgrade. - Continuous Improvement
Every action—big or small—feeds back into the knowledge base, compounding value over time.
This approach meant maintenance teams spent less time hunting context and more time fixing faults. Engineers felt empowered, not replaced. And the human expertise they once feared losing now drives smarter, faster decisions. Explore how the platform works
Mid-Journey Momentum and the Second CTA
Six months in, KBR’s teams were solving recurring issues in half the time. The maintenance culture shifted from reactive to proactive. Suddenly, AI empowering engineers wasn’t a promise – it was their daily reality. Ready to see it in your plant? Experience AI empowering engineers in maintenance
Results: Faster Fixes, Reduced Downtime, Empowered Teams
The numbers speak volumes:
- Mean Time To Repair (MTTR) dropped by 30%
- Unplanned downtime cut by 25%
- First-time fix rate climbed above 80%
Engineers now solve problems in context, guided by real-world solutions. Supervisors track progress at glance. Reliability leads can forecast maintenance maturity rather than guess it. And everyone gains confidence in data-driven decisions. With this foundation, full predictive maintenance moves from wish-list to roadmap.
At this stage, KBR also invited other teams to Learn about AI powered maintenance—spreading the benefits across sites and disciplines.
Change Management and Best Practices
Turning a tool into transformation isn’t just plug-and-play. KBR took a people-first approach:
- Champion Network
Identified maintenance advocates across shifts to pilot new workflows. - Coaching and Training
Embedded iMaintain’s user-friendly interface in daily stand-ups. - Data Stewardship
Ensured logs stayed clean by making data capture as frictionless as possible.
These steps built trust and kept engineers engaged. The consultative support and change-management guidance from iMaintain’s team proved invaluable. Suddenly, capturing knowledge felt less like paperwork and more like sharing insights.
For teams gearing up to follow suit, consider a quick chat with an insider. Talk to a maintenance expert.
Pricing and Scaling the Solution
Budget matters. KBR appreciated transparent, tiered pricing that scaled with their usage. By aligning subscription levels to the number of assets rather than seat counts, the platform grows as maintenance maturity rises. If you’re exploring costs and benefits, get a clear view of investment and return: Explore our pricing.
Testimonials
“Before iMaintain, our engineers spent more time digging for fixes than actually fixing. Now every repair adds to our shared brain. MTTR is down, morale is up—and knowledge lives on.”
— Sara Jenkins, Reliability Lead at KBR“The human-centred AI in iMaintain surfaces exactly what we need, when we need it. It’s like having a senior engineer on call, 24/7, without the overhead.”
— Liam O’Connor, Maintenance Manager“We’ve gone from firefighting to foresight. That shift in mindset—powered by an AI platform that respects our expertise—is a game-changer.”
— Aisha Patel, Operations Director
Conclusion
KBR’s journey shows that true predictive maintenance starts with what you already know. By capturing every fix, every insight and every asset detail, iMaintain turned a fragmented process into a continuous intelligence engine. It’s a blueprint for AI empowering engineers—one that avoids flashy promises and delivers real-world gains.
Ready to join the next generation of maintenance maturity? See AI empowering engineers in action at your factory