Achieving an equitable and sustainable energy future
Artificial intelligence (AI), machine learning (ML) and data science can make a difference for asset managers. They can equip decision-makers with the ability to anticipate problems earlier, adopt proactive maintenance strategies and increase the resilience of operations.
Why Conventional Methods Struggle
Limitations of Time-Based Maintenance and Lagging Indicators
Many organisations still use scheduled or usage-based inspections to manage asset networks. While this is relatively straightforward to plan, these strategies don't reflect the actual condition, context or risk of each asset. As a result, teams face a difficult balance between unnecessary work, disruption and missed failures.
Beyond the limitations of scheduled maintenance, asset managers often grapple with fragmented data spread across different systems and dashboards that only show past performance rather than future risks. There is a lack of complete visibility across potentially vast distributed networks. Decision-makers need tools that go beyond reporting past and current performance. They need accurate forecasts and data-led decision support that delivers them the information to take effective and proactive steps.
Where AI and Data Science Create Value
Applying AI and Machine Learning to Improve Asset Decision-Making
There is no single solution to these challenges. But well-targeted applications of predictive analytics, modelling and machine learning can support better asset decisions at both operational and strategic levels. Here are four key areas of application:
Predicting Failure Earlier
Machine learning models, trained on historical performance and sensor data, can detect subtle signs of degradation that might be missed by human inspection or rule-based systems.
For instance, in energy networks, models that analyse temperature, vibration, and load can spot patterns linked with fault risk. This allows for early alerts, leading to fewer unplanned outages, more cost-effective maintenance, and extended asset life.
Understanding Long-Term Asset Behaviour
Mathematical models can simulate how different factors affect an asset's condition over long periods. This is crucial for planning inspections, renewals, and managing long-term risks.
Utility companies, for example, often need to understand asset degradation in locations that are inaccessible, and they need to do so without causing significant disruption. Physics-informed models can estimate the location and condition of underground pipes, the age and type of components in electricity infrastructure. This can help to determine optimal inspection timings and support long-term safety assessments - leading to smarter inspections, stronger regulatory confidence, and clearer investment decisions.
Forecasting Under Uncertainty
Data-led forecasting allows teams to test "what if" scenarios and prepare for an uncertain future. These models are particularly useful for strategic planning, especially when factors like funding, demand, or environmental conditions might change quickly.
A publicly funded organisation could simulate the impact of different funding levels on maintenance cycles. This insight helps them prioritise investments that can deliver the greatest operational and service benefits, leading to more resilient asset strategies aligned with future risks and demands.
Managing with Limited or Imperfect Data
Many asset-heavy organisations don’t have full sensor coverage across their networks. In such cases, statistical models can still generate valuable insights. They do this by using proxy data, such as usage records, external factors like weather, or the performance of nearby assets.
Rail infrastructure teams can use inspection records, traffic data, and terrain information to model earthwork degradation. This helps them prioritise high-risk areas for intervention, even when the amount of available data is limited, making better use of all available information.
What Makes These Systems Work
Applying AI and Machine Learning to Improve Asset Decision-Making
The value of AI doesn’t just depend on model accuracy. For organisations to trust and adopt new tools, they must be explainable, integrated, and appropriately governed.
1. Transparency and Explainability
Infrastructure decisions are often subject to internal scrutiny and external regulation. That means models must offer clear reasoning, not just outputs. Explainable AI ensures that human experts can interpret, challenge and trust results.
2. Independent Validation
To build confidence in new approaches, many organisations use independent validation. This ensures that models are reliable, well-calibrated and aligned with real-world conditions.
3. Human Expertise Remains Central
AI supports decisions. It doesn’t replace domain knowledge. The most effective implementations involve close collaboration between engineers, analysts and data scientists. Together they create tools that reflect operational reality.
4. Data Integration
Effective AI tools require structured, timely data drawn from across systems—such as asset registers, condition monitoring, maintenance logs and external datasets like weather. Where there is limited, or incomplete data, it’s possible to enrich datasets with public or commercial sources, completing the picture for robust AI results
Data driven and pro-active asset management is not simply about collecting vast amounts of perfect data and feeding this to a model. It's about choosing the right model for the data available - accounting for the characteristics present such as noisy observations, missing values and complexity of the relationships present.
Business Benefits at a Glance
How Data-Led Asset Management Can Reduce Risk and Boost Operational Efficiency
Organisations adopting AI-powered infrastructure decision support report measurable benefits, including:
- Reduced emergency maintenance – with unplanned downtime cut by up to 50%
- Increased asset availability – supported by a 55% boost in maintenance team productivity
- Extended service life of critical components
- More efficient inspections – guided by up to 85% more accurate forecasting
- Better alignment of investment with risk – driven by predictive insights and reduced maintenance costs by as much as 40%
These gains also support wider goals, such as improving sustainability, reducing cost and demonstrating regulatory compliance.
Source: The True Cost of Downtime 2024: A Comprehensive Analysis (Siemens Senseye Predictive Maintenance)
Developing a Smarter Asset Strategy
Using Decision Intelligence to support Safer, More Resilient Infrastructure
You don’t need a full system overhaul to begin. Many organisations start small and build up as confidence grows.
Asset managers are often not short of data. The challenge is turning it into decisions that reduce risk and deliver value. AI, ML and advanced modelling give organisations the tools to move from hindsight to foresight—supporting infrastructure that is safer, more resilient and more efficient.
These technologies are not about removing the human from the loop. They are about giving decision-makers better insight, more timely warnings and clearer options. When built with care, they support smarter infrastructure for the challenges ahead.
If you would like to explore how you can harness AI to support enhanced asset management, get in touch below:
