The roadblocks to predictive maintenance — and why they’re worth overcoming 

AI-powered predictive maintenance is no longer a luxury; it’s fast becoming a competitive necessity 

The problem hiding in plain sight  

European road transport is under pressure from multiple directions at once. According to the IRU, the industry faces five interconnected structural challenges: driver shortages, rising costs, increasing freight demand, decarbonisation requirements, and rapid technological change [1]. Any of these would demand strategic attention. Together, they leave little room for operational inefficiency, and few inefficiencies are more costly than unexpected vehicle downtime. 

The urgency is compounded by a quiet but significant trend: the average age of vehicles in European fleets has climbed from 12.5 to 14 years, a 10.7% year-on-year increase [2]. Older vehicles break down more often, and maintenance planning matters more, not less, as fleets age. Add Euro 7 compliance pressure and tightening emissions monitoring to the mix, and the need to look ahead and proactively plan and prevent rises.  

Three phases of maintenance (and why the first two fall short) 

Fleet maintenance has evolved through three distinct models, each representing a different relationship between operators and their vehicles.  

Reactive maintenance is the most common: respond when something fails. It is simple but expensive. Emergency repairs carry premium costs, unplanned downtime cascades across delivery schedules, and each breakdown is a missed SLA [3].  

Preventive maintenance introduced fixed service intervals. For example, oil changes every X kilometers, tyre checks every Y weeks. An improvement, but still rigid; vehicles are either serviced unnecessarily or insufficiently because the schedule is driven by time, not actual vehicle condition [3].  

Predictive maintenance is the step-change. Rather than reacting to failures or following arbitrary schedules, it uses continuous data from the vehicle itself to anticipate problems before they materialize enabling maintenance to be planned, budgeted, and executed without disruption [3]. The Technology & Maintenance Council has found that predictive maintenance can reduce unplanned breakdowns by up to 47% [4]. For a fleet of 50 trucks, that figure represents dozens of avoided emergencies per year.  

How predictive maintenance works  

The mechanics of predictive maintenance follow a clear sequence:  

Data collection. Modern commercial vehicles generate continuous operational data (such as engine temperatures, fault codes, brake performance, etc.) captured via onboard diagnostics (OBD) and telematics hardware.  

Data analysis. Machine learning models trained on historical failure data, combined with the expertise and interpretations of fleet managers, analyze incoming signals to detect patterns that precede known failure modes. AI even starts to identify combinations of signals that human technicians or simple rule-based alerts would miss entirely [5].  

Actionable insights. The output is specific and prioritized, not “this vehicle may have an issue”, but a recommendation tied to a component, timeframe, and urgency level.  

Proactive scheduling. With sufficient lead time, maintenance is planned around operational needs: vehicles pulled during low-utilization windows, parts pre-ordered at standard rate, technician time allocated efficiently rather than scrambled.  

Benefits of predictive maintenance 

A 2024 study performed by Harris and Yellen found that AI-powered predictive maintenance significantly improved repair decision-making in heavy-duty trucking by helping technicians better predict vehicle breakdowns. Using the AI tool “PredictFix”, fleets reduced maintenance costs by €206 – €413 per truck annually, with 85% of the savings coming from avoiding unnecessary repairs. These findings highlight why predictive maintenance is increasingly seen not just as a technical upgrade, but as a direct operational and financial advantage for modern fleets [6]. 

Maximizing uptime. By catching issues before they become failures, predictive maintenance keeps vehicles on the road and revenue flowing.  

Simplified, predictable costs. Reactive maintenance is expensive precisely because it is unpredictable. Emergency repairs carry premium labor rates, parts sourced at short notice cost more, and last-minute schedule changes ripple across the operation. Predictive maintenance converts these erratic spikes into planned, budgeted line items. Maintenance windows are scheduled during low-utilization periods; parts are ordered in advance rates and technician time is allocated efficiently rather than scrambled.  

Enhanced safety and compliance. Vehicles flagged for early intervention are less likely to develop faults that compromise driver safety or trigger regulatory non-compliance [3].  

Implementation  

AI adoption in trucking remains low. IRU data shows that nearly 90% of small operators are not using it, and even larger companies have been held back by low perceived urgency and high upfront investment costs [1].  

Implementation typically involves:  

Hardware. Telematics devices that capture and transmit vehicle sensor data. This is the primary upfront cost for fleets without existing infrastructure.  

Platform. The AI diagnostic layer, usually delivered as a SaaS subscription priced per vehicle.  

People. Often underestimated: technician adoption is not optional. The AI tool’s value is only realized when maintenance teams trust and act on its recommendations [6].  

Real case  

One of the most commercially mature applications of predictive maintenance is tyre management. Michelin’s connected fleet program uses AI analytics to monitor tyre pressure, temperature, and wear patterns, flagging anomalies before they become failures [5]. The result: blowouts avoided, roadside callouts eliminated, and tyre lifespans extended. It is a concrete; countable demonstration of what AI diagnostics look like in day-to-day fleet operations and a useful proof of concept for operators considering broader implementation.  

Challenges of predictive maintenance 

The benefits of predictive maintenance are clear. The reality of implementing it across European fleets is more complex.  

Fragmented fleet data  

Large fleets rarely operate a single vehicle brand. Trucks, trailers, and components often come from multiple OEMs, each with its own systems, data formats, and platforms. This creates a fragmented data landscape where critical insights are locked in separate environments [11]. 

OEM data silos  

Many OEMs are reluctant to share detailed vehicle data openly. Instead, they keep fleet managers within their own portals and ecosystems. The result is a set of disconnected “island solutions” that prevent a centralized, fleet-wide predictive maintenance strategy [11]. 

Access vs. control trade-off 

To bypass OEM limitations, fleets often install their own telematics or onboard units to gain direct access to vehicle data. While this enables standardization, it introduces additional costs, operational effort (installation, maintenance), and logistical complexity, especially when vehicles are sold or replaced [11]. 

Infrastructure and integration costs 

Setting up predictive maintenance requires investment in hardware, software, and integration with existing systems. For many operators, especially SMEs, this upfront cost remains a key barrier [10]. 

Training and adoption 

Predictive maintenance is not just a technology shift, but an operational one. Drivers and technicians must understand and trust the system. Without proper training and processes, alerts risk being ignored or misinterpreted [9]. 

Data overload: capturing what actually matters 

Modern vehicles generate vast amounts of data. The challenge is not access, but relevance. Identifying which data points (fuel consumption, fault codes, wear indicators, compliance metrics) truly drive maintenance decisions is critical to avoiding noise and focusing on actionable insights [12]. 

Knowing when predictive is not the answer 

Predictive maintenance does not fully replace preventive maintenance. Fixed schedules still make sense for certain components or in cases where predictive infrastructure is not yet cost-effective. The most efficient fleets combine both approaches strategically [8]. 

The case for acting now 

The IRU is clear that AI will play a growing role in road transport as solutions become more accessible and cost-effective. [1] For mid-sized operators, the question is not whether to engage with this shift — but when. Early movers develop operational data and institutional capability that compounds over time. Late movers inherit a competitive landscape shaped by others. 

The vehicles are getting older. The regulations are getting stricter. The data is getting richer. Predictive maintenance is not a distant ambition. For operators willing to take the first step, it is available today. 

Interested in how AI is reshaping European road transport? Keep an eye on our latest articles as we continue exploring the future of digital trucking. 

Sources  

[1] IRU — Insights from the ground: AI and leading trends in trucking  

[2] TI Insight — European Road Freight 2026 Outlook 

[3] Geotab — Go Beyond Reactive Truck Maintenance 

[4] HDFleet — AI Trends in Fleet Management for 2025  

[5] Michelin Connected Fleet — How AI is Taking Predictive Fleet Maintenance to the Next Level  

[6] Harris & Yellen — Decision-Making with Machine Prediction: Evidence from Predictive Maintenance in Trucking (2024)  

[7] Falkoven — On Data-Driven Predictive Maintenance of Heavy Vehicles: A case study on Swedish trucks (2017) 

[8] Geotab —  What is predictive maintenance (PdM)? Benefits, challenges & examples for fleet management  

[9] Cummins et al. (2024) – Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities  

[10] Opsima – Preventive Maintenance vs Predictive Maintenance: Which Strategy Reduces Downtime & Cuts Costs? 

[11] Geotab — Breaking Down Data Silos in Fleet Management 

[12] Brunheroto et al. (2022) – Data analytics in fleet operations