Six strategies for developing a trusted system that informs planning, maintenance and regulatory reporting
For utility executives, the case for asset intelligence has taken on new meaning. Grid modernization, aging infrastructure and surging energy demand have made knowing exactly which assets are on the network, their condition and their precise locations a strategic imperative. Meeting the requirements of modern energy delivery demands it. A growing number of utilities are responding by launching pilots to test AI-driven image analysis, geospatial data integration, predictive asset management and other digital capabilities. Unfortunately, many of these pilots never scale.
The gap between a successful proof of value and an enterprise-wide deployment is wider than most organizations anticipate. It is not a technology gap. It is an organizational, data and governance gap. Pilots stall when they are built on poor data, live in disconnected silos and lack executive mandate. By understanding the most common failure points and the strategies utilities can use to overcome them, organizations can digitally transform and scale to deliver programs that inform capital planning, maintenance prioritization and regulatory reporting.
Why Pilots Fail: The Most Persistent Challenges in Deploying Asset Intelligence
The first and most pervasive challenge involves poor data quality. Most utilities have some form of asset management system in place. Still, those systems often contain records that are never reconciled with physical reality: locations are inaccurate, attribute metadata is missing and asset classes are miscategorized. A utility may invest significantly in a pilot only to find its outputs unreliable because the data feeding it was never trustworthy. A well-designed pilot can surface these problems before they sabotage a full deployment, but only if leadership enters with that expectation. Organizations caught off guard rarely authorize the investment required to fix root causes.
Siloed systems compound data challenges. Utilities typically operate across geographic information systems (GIS), advanced distribution management systems (ADMS), advanced metering infrastructure (AMI), enterprise asset management (EAM), work order management (WMS) and outage management systems (OMS) platforms that often do not communicate. This results in a fragmented picture of the network, undermining the promises of situational awareness and asset intelligence. Research from Esri underscores how widespread this problem is: while 85 percent of utilities use GIS in the field, only about 20 percent edit data in the field. This means 80 percent still collect field data on paper. Without digital capture at the point of work, the GIS is perpetually behind physical reality, and any asset intelligence program built on top of it inherits that deficit.
The lack of executive mandate also contributes to failure. Asset intelligence deployments require sustained commitment from GIS teams, IT, OT, engineering, operations, regulatory affairs and finance. Without strong sponsorship, these programs fracture into disconnected departmental initiatives that consume budget without delivering enterprise value.
Closely related is inadequate planning. A well-structured program begins with a defined business case tied to measurable outcomes. Without it, pilots are impossible to evaluate or scale. According to Forrester, 90 percent of decision-makers report significant difficulty scaling technology use cases across their organizations. Unfortunately, this pattern plays out in utility asset intelligence programs with striking regularity.
Finally, even utilities that get strategy, data and systems right can stumble on governance. When coordination across organizational boundaries is absent, the data model degrades. Field findings are not written back to asset records; imagery is not processed into actionable intelligence; model outputs are questioned but never validated and the feedback loops that would make the system smarter simply never close.
Challenges utilities face in deploying asset intelligence programs include:
- Poor or incomplete asset data
- Siloed systems that prevent asset data from flowing across platforms
- Lack of executive sponsorship and organizational alignment
- Insufficient strategic planning and undefined business cases
- Weak data governance and poor feedback loops
Six Strategies for Building Asset Intelligence Programs That Scale
Scaling asset intelligence from pilot to enterprise requires more than better technology. It also requires deliberate choices about data architecture, system integration, organizational alignment and governance. The following six strategies distinguish utilities that successfully scale their asset intelligence programs from those that remain stuck in pilot mode.
1. Build a Comprehensive Strategy and Roadmap, with the Pilot as the First Proof Point
Before launching a pilot, build a defensible business case aligned with a long-range strategic roadmap. That roadmap should define what success looks like at enterprise scale, the expected benefits and how they will be measured and the capabilities. Multiple variables, including data, systems, staffing and governance, are needed to sustain the program. The pilot then becomes a mechanism for validating specific elements of that roadmap.
Choose a pilot area that surfaces known asset data quality issues so the effort demonstrates both the value of accurate intelligence and the cost of operating without it. If the pilot uncovers data problems, treat them as intelligence, not failure. Just as important, define what comes after the pilot before the pilot begins. That means establishing times like decision gates and success criteria, a repeatable scaling pattern and funding pathways.
2. Establish a Single Trusted Asset Layer Anchored in Enterprise GIS as the System of Record
Asset intelligence depends on a shared, authoritative representation of the network. The most effective foundation is a modern enterprise GIS platform, such as the Esri ArcGIS Utility Network, configured as the single system of record for network topology, asset location, attribute data and connectivity. Asset locations flow into ADMS for grid operations, into work management systems for maintenance planning and into regulatory tools for compliance reporting.
When that foundation is current and accurate, downstream systems perform better. When it is not, every system that depends on it inherits the same inaccuracies. Modern geospatial solutions help utilities migrate from fragmented legacy environments to fully integrated, enterprise-grade GIS platforms that serve as this trusted foundation.
3. Build Integration Between GIS, AMI, ADMS and Work Management So Data Flows Across Systems Rather Than Sitting in Silos
A trusted asset layer is necessary but not sufficient. Data must flow continuously across systems with location as the integration point. When AMI data flows into a spatially grounded ADMS, operators gain immediate situational awareness. They understand what assets are under stress, where the network is vulnerable and where crews should be dispatched. When maintenance history from the EAM system is visible in that same spatial context, planners can correlate performance data with asset age, condition and location to prioritize capital investment with far greater precision.
IT/OT/GIS integration demonstrates how location can serve as a critical connection point across IT and OT systems. Utilities that architect their integration layers to accommodate AMI 2.0 data streams will be positioned to move from reactive maintenance toward predictive asset management as those capabilities mature.
4. Implement Data Pipelines That Push Field Findings, Imagery and Sensor Data Back into Asset Records in Near Real Time
One of the most persistent gaps in utility asset management is the latency between what field crews observe and what enterprise systems record. Inspection findings are documented on paper and entered days or weeks later. Drone and aerial imagery sit in disconnected repositories. Sensor readings available in real-time operation are never written back in a form that informs maintenance decisions. Closing this gap requires an intentional data pipeline architecture in which field findings from mobile mapping tools, imagery analysis or sensor telemetry have automated pathways into the enterprise GIS and EAM systems.
Maintenance planners working from yesterday’s field results make decisions based on current intelligence; those working from records six months out of date are operating on outdated information. Modern mobile mapping solutions designed for non-GIS users in the field can dramatically accelerate this cycle.
5. Create Feedback Loops So Field Crews Can Validate and Improve Model Outputs Over Time
Asset intelligence models that predict failure probability, classify asset condition from imagery and estimate remaining useful life are not static. They improve with use, provided that field crews have structured mechanisms for validating outputs and contributing corrections.
When a technician arrives at a flagged asset and finds the model was wrong or discovers a defect it missed, systematically capturing that information separates programs that get smarter over time from those that stagnate. Field workflows should make this friction-free: one or two taps on a mobile device. Crews who see their feedback reflected in updated model outputs develop confidence in the system, ultimately driving adoption.
6. Define Clear Data Ownership, Standards and Roles Across IT, OT, Engineering and Regulatory Teams
Governance is where many technically promising asset intelligence programs ultimately fail. Without it, data foundations erode; asset records fall out of sync with physical reality, quality standards vary by team and ownership of critical systems becomes contested.
An effective governance model explicitly defines data ownership, establishes consistent standards across systems and creates coordination mechanisms among IT, OT, field engineering, asset management and regulatory affairs. Governance structures that align these groups through defined roles, regular data quality reviews and clear escalation paths make enterprise asset intelligence sustainable. Designing for regulatory compliance from the outset, rather than retrofitting it later, reduces both risk and cost.
Why Select TRC
TRC helps utilities design and execute asset intelligence programs that are built to scale from the start. That means structuring pilots with a clear business case and defined success metrics, ensuring the underlying data and systems are ready to support an enterprise deployment and providing the integration expertise to connect GIS, ADMS, AMI and work management platforms into a unified, actionable asset layer.
TRC brings deep experience across the full arc of these programs, from strategy and roadmap development through pilot execution, data remediation, system integration and long-term program governance. For utilities navigating the complex organizational and technical terrain between a promising proof of value and a fully operational enterprise deployment, TRC provides the expertise, the methodology and the hands-on practitioners to close that gap. Contact TRC today to learn how we can help your organization turn asset intelligence from a pilot into a competitive advantage.
