Unlocking the Smart Grid: Overcoming Barriers to Intelligent Asset Management

The energy industry is under pressure to modernize its approach to asset management. Companies are investing heavily in sensors, data platforms, and AI models to make grids, pipelines, and plants more reliable and cost-efficient.  

But while the road has been challenging, utilities are finding practical ways to move forward. 

In interviews with leaders from various energy companies, including EDP and RWE, as well as industry research, recurring themes emerged. Data systems can be fragmented, pilots often stall, and adoption among field teams is never instant. Yet, those same leaders are working to bridge data gaps, scale predictive maintenance beyond pilots, and engage employees in new ways of working. 

The vision of intelligent asset management is within reach, and the industry is already taking steps to turn it from aspiration into reality.  

The following sections explore the barriers most often slowing progress, and the practical steps utilities are taking to overcome them. Explore the full agenda.

The Challenges of Intelligent Asset Management (And How Utilities Companies Are Coming Out Triumphant)  

In the lead-up to the Intelligent Asset Management Summit, we spoke with industry leaders about the realities of digital transformation in utilities. Three challenges surfaced again and again: fragmented data, predictive maintenance stuck in pilot mode, and workforce resistance to change. 

Yet alongside these hurdles, they are finding ways forward. From stronger data governance to new monitoring technologies and workforce engagement, leaders are proving that intelligent asset management is an operational reality. 

Challenge 1: Without Reliable Data, Everything Else Fails 

The foundation of intelligent asset management is reliable data. Yet most leaders agree this is where progress slows.  

Systems are fragmented, data formats are inconsistent, and the effort required to create a single source of truth is enormous. Without it, analytics, predictive models, and digital tools struggle to deliver real value. 

“Asset management in general has been a challenge to push through in the utility space… all of the data governance, data management - it’s a tough [area], and just because we’re leveraging technology doesn’t mean the culture has changed.” 

Others feel that the data they have access to isn’t always reflecting reality. At RWE, Senior Manager of Digital Operations, Rogers Whittle, pointed to the gap between field conditions and digital records. 

He stressed that, “the way that data is ingested into the company needs to be transparent to that reality in the field.” Otherwise, predictive maintenance and intelligent asset management will never deliver reliable outcomes. 

The lack of standard definitions compounds the problem. Even basic performance metrics aren’t aligned across the sector, leaving financing partners unable to make “apples to apples” comparisons between plants.  

Utilities companies that fail to get their data house in order risk wasting millions on tools that can’t deliver value. Those that succeed will unlock predictive insights, optimize capital planning, and build the foundation for a truly intelligent grid. 

The Solution: Strengthen Data Governance and Integrations 

Utilities companies are beginning to invest in frameworks that clean, structure, and align asset information, turning it into a reliable foundation for intelligent asset management.  

Leaders explain how their teams built such a framework, and while adoption took time, it has begun to give decision-makers a consistent view of asset health across departments. That visibility makes it easier to prioritize maintenance, plan capital investments, and reduce duplication of effort. 

Across the industry, leaders are championing standard definitions so financing partners can make “apples to apples” comparisons.  

These initiatives are moving the sector toward common benchmarks that improve collaboration with investors, regulators, and partners. 

With accurate, consistent data now flowing into their systems, utilities are seeing predictive models and digital twins deliver more trusted results. The shift from fragmented records to reliable insight is already helping leaders allocate budgets more effectively, extend asset life, and build the foundation for a smarter, more resilient grid. 

Challenge 2. Predictive Maintenance Works in Pilots - But Struggles to Scale 

For many utilities companies, predictive maintenance is the most attractive promise of intelligent asset management.  

The ability to foresee transformer faults, detect pump seal failures, or anticipate battery degradation before outages occur could cut costs and improve reliability at scale.  

To test this, companies often run pilots on a limited set of assets, such as a single plant, a handful of substations, or a specific pipeline. The concept being tested is whether AI models, combined with remote monitoring tools, can accurately forecast equipment issues and reduce downtime. 

These small pilots can show early success, but taking them system-wide is where trouble sets in. Integrating new monitoring technologies with decades-old legacy systems is complex. The upfront costs are easier to justify in a pilot than across an entire fleet, and while an AI model may perform well in one environment, replicating consistent, trusted results across thousands of geographically dispersed assets is far harder. 

As Stephan Blasilli, Head of Business Process Excellence at EDP Renewables North America explained:

“It’s very easy to create a model that is narrow in scope… it’s very difficult to scale it and ensure consistent results.” 

Utilities companies also struggle with the skills needed to support these initiatives. Michael Sales, Director of Maintenance and Reliability at PBF Logistics, highlighted skills and resources as another layer of complexity. His team’s predictive maintenance pilot quickly ran into a shortage of people able to work with cloud systems, data lakes, and machine learning:

“The pain point right now is resources that understand data lakes, large language models and cloud… it’s a lot of tools and technology that is new to a lot of people, and that’s where we still have that learning curve.” 

So, the challenge isn’t proving that predictive maintenance works, it’s building the data foundations, workforce capabilities, and operational trust needed to roll it out at scale.  

Until those barriers are addressed, utilities companies will remain stuck in pilot mode, unable to capture the full value of AI-driven asset management. 

The Solution: Deploy New Monitoring Technologies to Enable Predictive Insights 

The solution to stalled predictive maintenance pilots is better monitoring. A few periodic data points gathered manually can’t support AI models that need constant, granular signals to spot early warning signs.  

That’s why utilities are rolling out wireless sensors, vibration analysis tools, and IoT devices that deliver continuous streams of high-quality data. 

These investments are helping predictive models move from promising pilots to trusted, scalable systems.  

By standardizing how data is captured across plants and substations, leaders are reducing the inconsistencies that once held back adoption. As models are trained on richer, more uniform inputs, their forecasts become more accurate and easier for operations teams to rely on. 

Companies are also addressing the skills challenge head-on by training their managers and engineers to work directly with cloud platforms and machine learning tools.  

Building internal expertise ensures predictive maintenance isn’t dependent on outside consultants and gives field teams greater confidence in the systems they’re adopting. 

The payoff goes far beyond replacing clipboard checks. With continuous data streams, utilities can detect subtle anomalies, a motor running slightly hotter than expected, a pump vibrating just beyond tolerance, that would otherwise remain hidden until failure.  

Over time, this builds the operational trust leaders need to roll predictive systems out across entire fleets, not just isolated pilots. 

Challenge 3. Employee Mindsets Slow Adoption 

Putting technology in place is only part of the equation, the human side of IAM needs consideration too. 

Field teams are accustomed to long-standing practices, and persuading them to abandon spreadsheets, manual logs, or established routines is not straightforward. And new systems can be viewed as disruptive rather than helpful, especially when the benefits aren’t immediately visible.  

Sonma Agatha-Christy Okoro, Vice President, Business Analytics & Digital Operations at RWE Clean Energy noted how this reliance on legacy tools continues to hold organizations back. Despite investments in new platforms, teams often revert to Excel because it feels familiar and easy to control.  

Without structured change management and clear incentives, the promise of digital tools risks being undercut by old ways of working. 

“It took almost four years to see the benefits, because the business was not ready for that kind of change. Just because you bring in technology doesn’t mean mindsets have changed.” 

That lag underscores the cultural side of digital transformation and how a shift in thinking can’t be forced overnight.  

Explore this topic further at our upcoming panel discussion on AI and asset health.

The Solution: Drive Adoption Through Workforce Engagement 

Many technicians are deeply skilled in mechanical systems but wary of digital platforms that feel imposed from above.  

Excel and manual logs persist not because they’re efficient, but because they’re familiar and under the user’s control. Changing that dynamic requires active engagement, visible leadership, and a clear demonstration of value. Learn more about workforce engagement strategies.

Energy companies are beginning to see progress when they embed champions within their teams: respected field leaders who show peers how digital tools make their daily work easier. Instead of adoption being a mandate, it becomes a peer-led movement. RWE implements this approach through “super user groups” consisting of field technicians and site managers who participate in the design and rollout of new digital systems alongside the product team and subject matter experts. 

Others are focusing on building in-house expertise.  

For example, managers are being trained directly in cloud and AI platforms, giving them the skills to guide their teams with confidence. Leaders are upskilling in these technologies so theycan mentor theirstaff and build trust from the inside out. 

The results go beyond technical competence. Teams that once resisted digital tools are now recognizing the payoff - shorter downtime, fewer unexpected breakdowns, and more predictable workloads. With those benefits visible in their day-to-day routines, employees are increasingly embracing new systems as an asset rather than as a burden. 

The broader lesson is that change management is as much about culture as it is about capability. When workers see firsthand how digital tools reduce frustration and make them more effective, adoption shifts naturally - technology stops being “the system they have to use” and becomes “the system that helps them succeed.” 

Conclusion 

Data quality, predictive maintenance, and workforce adoption remain stubborn barriers, but leaders are already finding practical ways forward. Stronger data governance, new monitoring technologies, and structured change management are laying the foundations for smarter, more resilient grids.

Discover how to leverage AI, predictive maintenance and data-driven technologies to optimize asset performance, improve sustainability and reduce operating costs at our upcoming event:

Intelligent Asset Management in Energy Summit
December 2-4, 2025 | Houston, TX