The elevator industry stands at a crossroads. Skilled labor is scarce, service costs are climbing, and building owners expect near-perfect reliability. At the same time, artificial intelligence promises to transform how we diagnose problems, deploy technicians, and prevent breakdowns before they happen.
But where does the industry actually stand on AI adoption? And more importantly, where are the real opportunities to drive value?
FIELDBOSS and Elevator World recently surveyed hundreds of professionals across the elevator sector, from technicians and service companies to consultants and supply chain participants, to understand how AI is being perceived, adopted, and planned for in the field. The results reveal an industry that’s cautiously optimistic, eager to solve real problems, but still navigating significant barriers to implementation.
The Current State of AI Usage in the Elevator Industry: High Interest, Uneven Adoption
While AI has captured the industry’s attention, adoption remains fragmented. Technicians are leading the charge, with 33% actively using AI tools in their daily work. Meanwhile, consultants trail at just 14% adoption, though they’re among the most curious and exploratory groups.
Service companies fall somewhere in the middle, with about 20% actively using AI and another third exploring it. Supply chain participants are currently behind but show strong intent to catch up—nearly three-quarters believe AI could drive product standardization across contractors.
The pattern is clear: interest is widespread, but many organizations remain in the “interested but unsure” category, waiting for proven use cases and clearer ROI before committing resources.
The Pressure Points Driving AI Interest
Three challenges dominate across all respondent types, and they’re steering the conversation toward practical AI applications:
- Skilled labor shortages top the list for nearly every group, particularly technicians, consultants, and service companies. As experienced professionals retire and fewer young workers enter the trades, the industry needs tools that can make every technician more effective.
- Rising service costs are squeezing margins and forcing difficult conversations with building owners. AI-powered optimization (from predictive maintenance to better route planning) offers a path to do more with less.
- Equipment reliability and downtime remain persistent pain points. Building owners and tenants have zero tolerance for out-of-service elevators, making predictive and preventive strategies increasingly attractive.
For supply chain participants, compliance requirements and parts sourcing difficulties add additional layers of complexity that AI could help address.
Where AI Can Deliver the Most Value
When asked where AI could make the biggest impact, respondents pointed to a handful of high-value use cases:
- Remote diagnostics and repair guidance topped the list. The ability to troubleshoot issues remotely (or arm technicians with AI-powered guidance before they arrive on-site) can dramatically improve first-time-fix rates and reduce costly callbacks.
- Predictive maintenance and downtime prevention came in as close seconds. Rather than waiting for equipment to fail, AI can analyze performance data to identify issues early, schedule maintenance during off-peak hours, and keep elevators running safely and smoothly.
- Regulatory compliance tracking also emerged as an opportunity, particularly for consultants and service companies managing complex portfolios across multiple jurisdictions.
- Enhancing the passenger experience also stood out, with technicians pointing to AI-enabled dispatching and shorter wait times as a practical reminder that operational efficiency and customer satisfaction often go hand in hand.
The Barriers Holding Elevator Teams Back
Despite the clear opportunities, several concerns are slowing adoption:
- Lack of standardization across manufacturers was the most frequently cited barrier. The elevator industry’s fragmented equipment landscape makes it difficult to deploy AI solutions that work across mixed portfolios.
- Reliability of predictions is another major concern. Teams need confidence that AI recommendations are accurate before they’ll trust them in high-stakes service situations.
- Concerns about data privacy and worker surveillance persist, particularly among technicians and service companies. Any AI implementation must strike a balance between operational insights and respect for privacy and autonomy.
- Cost of implementation ranked highest for service companies, many of which operate on thin margins and need to see a clear payback period before investing in new technology.
Interestingly, labor resistance was cited primarily by service companies, suggesting that concerns about AI displacing workers may be more prevalent in management discussions than among technicians themselves, who are often most eager for tools that make their jobs easier.
What the Next 2-5 Years Will Bring
Looking ahead, respondents expect AI to drive three major shifts:
Technologically, the industry will continue to move towards cloud technology, remote monitoring, hands-free interactive tools, and better communication between companies, technicians, and building users. Self-diagnostics and automation will help offset skilled labor gaps.
Operationally, service will shift from reactive to proactive. Digitization of tracking, condition-based maintenance, and targeted interventions will become standard practice. Less experienced technicians will rely more heavily on digital aids to handle complex repairs.
Economically, margins will tighten for firms that don’t adopt cost-tracking and optimization tools, while those that do will gain a competitive advantage. Standardization in contracts and greater transparency in service costs are expected to increase.
Nearly 40% of service companies believe AI will be “very important to protecting margins” over the next few years. Only 13% see no impact at all.
The Path Forward: Start Small, Scale What Works
The survey’s message is clear: AI in elevators isn’t futuristic; it’s a modern operations upgrade. The industry is ready to pilot targeted solutions that solve real problems and demonstrate measurable ROI.
For building owners and investors, that means prioritizing remote diagnostics and predictive maintenance tied to uptime metrics and service-level agreements. Look for vendors who offer cross-brand coverage and transparent reporting.
For contractors and service firms, the priority should be equipping technicians with guided troubleshooting tools, code references, and parts prediction capabilities. Start with a small route or customer segment, measure first-time-fix rates and the number of avoided truck rolls, then scale what works.
For consultants and supply chain participants, the focus should be on standardization, transparency, and building trust through tangible outcomes rather than vague promises.
The elevator industry’s AI journey is still in its early chapters, but the groundwork is being laid. Those who start now with clear objectives, proven use cases, and a focus on measurable outcomes will be best positioned to thrive in the years ahead.
Want the Full Elevator AI Story?
This blog post only scratches the surface of our comprehensive survey findings. For detailed breakdowns by respondent type, specific data visualizations, and actionable recommendations tailored to your role in the industry, download the complete whitepaper.
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