At its core, asset management is about maximizing the lifespan and performance of critical infrastructure – roads, bridges, buildings, and systems – while minimizing cost and downtime. Historically, this has meant a mix of reactive and preventive maintenance. Today, with the rise of predictive maintenance powered by real-time data and advanced analytics, we can proactively address issues before failures occur.
During a recent roundtable at the NJ TransAction Conference, our digital advisory experts, alongside representatives from several public agencies, discussed how predictive maintenance is changing the way we perform asset management in the AEC industry.
What is Predictive Maintenance?
Predictive maintenance is “telling the story” of an asset throughout its lifecycle. By leveraging inspection histories, sensor data, and AI-driven modeling, we can develop dynamic degradation curves that forecast asset health. These predictions are continuously validated against real-world field observations, allowing for more accurate and actionable maintenance strategies.
The true transformation happens when data-driven insights are combined with engineering judgment – bridging the gap between technology and practical decision-making.
The Business Case: Efficiency, Savings, and Strategic Reinvestment
Many of our clients are facing pressures from tightening budgets. One potential outcome from leveraging this technology is it allows agencies to reinvest savings from optimized maintenance into broader city improvements. Additionally, this technology minimizes opportunity costs and helps clients avoid preventable breakdowns that can disrupt operations and bleed resources.
Building the Foundation: Data Infrastructure and Governance
Successful predictive maintenance programs require:
- Unified Asset Classification Systems: Standardizing how assets are categorized provides consistency across datasets, enabling scalable, enterprise-wide decision-making. For example, aligning asset classes with frameworks like the Federal Transit Administration’s (FTA) Transit Asset Management (TAM) plan.
- Clean, Structured Data: Quality data is the foundation of reliable predictions. This means addressing legacy data challenges, standardizing formats, and verifying proper calibration of sensor inputs.
- Governance and Stakeholder Alignment: Good data governance practices, including clear ownership, quality controls, and defined workflows, are foundational. Predictive models are only as good as the inputs and the organizational alignment behind them.
Change Management: More Than Just Software
Implementing predictive maintenance is not solely a technical challenge, it’s an organizational one. Our team emphasizes working with clients to establish a common language and glossary before rolling out predictive models. Misalignment on terminology, processes, or expectations can derail even the best technology solutions.
Change management in this context requires balancing soft skills (training, communication, culture shifts) with technical deployment.
Overcoming AI Skepticism with Practical Applications
In our conversations with clients, there remains a healthy amount of skepticism around AI. During the panel, we discussed how sometimes a good statistical model does the job just fine. But when agencies are dealing with millions of data points across asset classes, AI and machine learning step in to detect patterns humans can’t. STV’s team has used models to prioritize repairs based not just on condition, but on failure probability and criticality – a major shift from traditional maintenance planning.
Typical AI techniques used include:
- Regression analysis for degradation prediction
- Anomaly detection for early failure indicators
- Time-series forecasting for lifecycle planning
Practical Data Collection: Start Simple, Scale Smart
Simplifying data collection is crucial to scalable predictive maintenance. For the challenges of data collection, we urge that it doesn’t need to be overly complex. Field inspections, vibration sensors, temperature monitors – these are already changing how we view high-risk assets. Just as important is empowering maintenance teams with mobile tools to log asset conditions during routine work. When front-line teams become data collectors, your predictive program gains both speed and precision.
Looking Ahead: A Data-Driven Future for Infrastructure Resilience
The overarching message from our panel discussion was clear: Data is only powerful when supported by strong governance and organizational alignment. You need clean inputs, trusted models, and, most importantly, stakeholder alignment. Predictive maintenance isn’t a silver bullet, but it is a strategic edge. It’s how agencies can deliver more with less, preserve what matters and build smarter cities for the future.
