“We can now automatically group together cases that have a common underlying cause into ‘work order trend topics.’ These can speed up resolution and reduce backlogs. For example, managers can act quickly to resolve clusters of cases, rather than tackling them individually. And agents feel much more empowered, as they can proactively resolve issues by analysing other work orders in the topic,” Poku explains.

It’s a powerful shift—from a reactive scramble to a proactive assurance that systems will keep running smoothly.

Navigating the Challenges of Implementing Predictive Analytics

Adopting predictive maintenance, however, isn’t without its roadblocks. For predictive models to be accurate, they need high-quality, consistent data, which can be difficult to obtain, especially with legacy equipment. So, how can field service teams overcome these data challenges?

Frank Klomp, Managing Director of Optimize My Day, stresses the importance of reliable data. “Data is key. Many service companies struggle with collecting consistent, high-quality data,” he notes. To address this, many are turning to advanced data warehousing tools like OMD’s Powerhouse, which unifies and processes data for predictive insights.

“OMD’s Powerhouse is such a warehouse combining data ingestion with intelligent forecast-models based on AI to improve the predictive capabilities of service organizations,” Klomp explains. But data quality isn’t the only issue—many companies are also working with older equipment that lacks IoT compatibility. As Klomp points out, “The installed base, older systems in particular, may lack the appropriate sensors and IoT technologies to send live data directly to the data collectors. They must be modernized or replaced in order to improve the forecasting capacity.”

There’s also the human side to consider. Shifting teams from traditional maintenance to predictive models means rethinking processes and helping teams trust data-driven insights over hands-on experience. “Change management is crucial, as teams may be resistant to relying on analytics; regular training, transparent communication, and highlighting early successes help build trust and adoption,” Klomp says. And he’s right—it’s not just about bringing in new tools but creating a culture that values predictive insights as a core part of the job.

Measuring ROI: Tracking Predictive Maintenance’s True Impact

For companies weighing an investment in predictive maintenance, proving ROI is essential. Predictive analytics can yield significant benefits, from fewer emergency repairs to improved First-Time Fix Rates and extended equipment life. But how can companies quantify these gains?

Poku advises that ROI should be approached with clear metrics and tools that make analysis simple. “There are two key parts to this in my view. The first is deciding what data points you need to measure. And the second is how you bring all those together into one place and turn them into usable insights,” she says. ServiceNow has created preconfigured dashboards that offer a consolidated view of key performance metrics in real time.

“Typically, companies will want to track the status of work orders, time to resolution, agent utilisation, and so on. But this data is only really useful if you can analyse it effectively,” Poku explains. A comprehensive, accessible dashboard allows managers to see exactly where predictive maintenance is driving results. “In short, if the right data is presented in a convenient, usable format, customers can better track their cost allocations and get an accurate view of the ROI of their predictive maintenance tools.” Ultimately, predictive maintenance’s value isn’t abstract—it’s measurable and actionable.

The Future of Predictive Analytics: AI, Edge Computing, and Digital Twins

Looking ahead, the potential for predictive maintenance continues to expand, especially with the rise of AI, edge computing, and digital twins. What could these new technologies mean for the future of field service?

Johann Diaz sees edge computing as a particularly exciting development. “One of the most exciting future developments in predictive analytics is the increased integration of AI-driven edge computing with IoT sensors. Edge computing allows data processing to happen at the device level, enabling near-instantaneous analysis without relying on centralized servers.