{"id":32998,"date":"2026-01-29T18:45:38","date_gmt":"2026-01-29T18:45:38","guid":{"rendered":"https:\/\/www.trccompanies.com\/?post_type=news-and-insights&p=32998"},"modified":"2026-04-27T11:52:19","modified_gmt":"2026-04-27T11:52:19","slug":"8-digital-transformation-trends-redefining-the-utility-market-in-2026","status":"publish","type":"news-and-insights","link":"https:\/\/www.trccompanies.com\/fr\/insights\/8-digital-transformation-trends-redefining-the-utility-market-in-2026\/","title":{"rendered":"8\u00a0Digital Transformation\u00a0Trends Redefining the Utility Market in 2026"},"content":{"rendered":"\n\n
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The most impactful tech trends for utilities in 2026 focus on turning overwhelming grid complexity into actionable intelligence, with expert practitioners actively involved in planning and execution. As sensing, analytics and automation scale across the grid, enabled by AI, cloud and mobile solutions, utilities that combine integrated enterprise systems with experienced partners will be best positioned to improve safety, resilience, capacity and customer outcomes.\u00a0<\/p>\n

In the US and around the globe, utilities face tremendous market forces. Surging demand\u00a0driven\u00a0by data centers, aging\u00a0infrastructure\u00a0and increasing extreme weather and cyber threats have pushed traditional technology to the brink. At the same time, distributed energy resources (DERs), flexible load\u00a0programs\u00a0and evolving market rules continue to\u00a0impact\u00a0how power flows across the grid.\u00a0\u00a0<\/p>\n

All these factors require utility companies to look to technology for developing more efficient and resilient ways to plan, operate and invest. It means building a smart grid that uses data and analytics to inform decision-making at every level, managing risk, reliability and affordability. The following eight trends highlight where digital technologies are moving from pilots to business-critical operational\u00a0infrastructure\u00a0and why expert guidance\u00a0remains\u00a0essential at every step.\u00a0<\/p>\n

1. Control Systems Become the Operational Core<\/h3>\n

Modern ADMS,\u00a0DERMS\u00a0and SCADA are\u00a0fast evolving\u00a0from standalone systems to an integrated control environment. Instead of fragmented views, converged control platforms give operators a single operational picture of rooftop solar, storage,\u00a0EVs\u00a0and flexible load across circuits and feeders. Staff can see,\u00a0simulate\u00a0and act on conditions in near real time.\u00a0<\/p>\n

As extreme weather and load volatility increase, utilities can no longer rely solely on manual switching and conservative operating assumptions. Coordinated control logic across ADMS and DERMS automates sectionalizing, load shedding and restoration. Operators can effectively orchestrate solutions at the point of consumption, such as DER portfolios, voltage optimization and flexibility programs, before committing to capital\u2011intensive upgrades\u00a0in\u00a0grid capacity.\u00a0<\/p>\n

This convergence also changes how resilience and capacity are measured. Investments in control systems are increasingly justified on avoided outage minutes, improved hosting\u00a0capacity\u00a0and the ability to safely run closer to system limits, rather than only on traditional reliability indices\u00a0and their inherent risk and inefficiency. Grid operators move from static planning to dynamic operations, using real\u2011time telemetry, forecasting and analytics pipelines to make precise decisions to optimally manage resources.\u00a0<\/p>\n

Across the organization, integrated control systems and energy management systems (EMS) connect transmission, distribution, outage and more for greater collaboration and visibility. High\u2011fidelity analytics running on cloud infrastructure feed these platforms with better\u00a0data, improving situational awareness.\u00a0<\/p>\n

As much as this integrated enterprise approach is transforming utility operations, experts in the loop only increase in value. Engineers and operators must configure control strategies, tune models, manage protection settings and ensure that data and analytics meet local operating mandates and safety margins, which is why many utilities are turning to partners with protection, planning and digital engineering expertise to design and deploy converged control systems.\u00a0<\/p>\n

2. AMI 2.0 Emerges as the Grid\u2019s Intelligence Platform<\/h3>\n

AMI is evolving from a billing tool into an operational data engine that underpins forecasting, planning and customer programs across the utility. Next\u2011generation AMI 2.0 streams interval usage, events and power\u2011quality indicators that feed probabilistic forecasting, DER analytics, outage operations and load flexibility initiatives in near real time.\u00a0<\/p>\n

AMI 2.0 helps close the loop between customer devices and control systems. Using granular consumption patterns, voltage signatures, and event data, utilities can infer behind\u2011the\u2011meter DERs such as rooftop solar and EV charging, detect emerging overloads, and target flexibility measures to specific transformers, phases and feeders. When integrated with GIS and asset systems, AMI data supports targeted asset replacement and maintenance by flagging overloaded transformers, chronic voltage issues and phase imbalance.\u00a0<\/p>\n

Outage operations are another major frontier. Modern AMI supports far more than last\u2011gasp signals. It informs automated restoration, nested outage\u00a0identification\u00a0and post\u2011event service quality verification, enabling faster restorations and better reporting to regulators and customers. On the customer side, AMI\u2011powered portals and alerts make usage,\u00a0costs\u00a0and program opportunities more transparent, strengthening engagement in demand response and time\u2011varying rate programs.\u00a0<\/p>\n

Technically, AMI 2.0 programs increasingly rely on cloud or hybrid deployments, which allow utilities to scale compute, analytics and storage while maintaining cybersecurity and regulatory compliance. AI and machine learning are layered on top of meter telemetry to detect theft, anomalous patterns and emerging equipment failures directly from AMI data, turning millions of meters into a distributed sensor network.\u00a0<\/p>\n

Yet technology alone does not guarantee value. Successful AMI 2.0 strategies pair the platform with expert program design, data governance and change management, ensuring that meter data is treated as a mission\u2011critical regulated asset and translated into actionable customer journeys, operational improvements and regulatory outcomes. Utilities are increasingly leaning on experienced partners to architect analytics pipelines, define use cases and integrate AMI 2.0 into broader grid modernization roadmaps.<\/p>\n

3. GIS Serves as the Backbone of IT\/OT Convergence\u00a0<\/h3>\n

G<\/span>IS is becoming the authoritative spatial fabric for the modern utility, ensuring that decisions involving assets, customers, work and network models can be navigated from a common map. As IT and OT converge, GIS serves as the map backbone, connecting SCADA, ADMS, AMI, asset management and work management through location-based data and analysis.\u00a0<\/p>\n

Modern GIS underpins digital twins, which rely on accurate topology, connectivity and asset attributes to simulate load, DER integration, switching and contingency scenarios. Geospatial capabilities, such as 3D modeling, help accurately represent indoor facility networks, complex substations, and evolving configurations.\u00a0<\/p>\n

In the field, mobile tools like TRC\u2019s Lemur extend GIS combined with work and asset management data directly to technicians, enabling them to capture precise locations, materials and as\u2011built changes at the point of work. Data enrichment services, such as\u00a0barcode capture, GPS\u2011grade locations and structured attribute collection, improve traceability and compliance. In addition, they support vegetation management, wildfire mitigation and resilience planning when combined with climate, flood and risk spatial data layers. \u00a0<\/p>\n

An enterprise\u00a0GIS architecture reduces rework and integration complexity by\u00a0eliminating\u00a0multiple, inconsistent data copies scattered across engineering,\u00a0operations\u00a0and customer systems. With\u00a0utility data and assets\u00a0georeferenced, utilities can more easily layer AI and advanced analytics atop their data. They can use\u00a0location\u2011based models to detect assets from imagery,\u00a0identify\u00a0vegetation\u00a0risks\u00a0and automatically build station models from geo\u2011imagery.\u00a0<\/p>\n

Making GIS the backbone of IT\/OT convergence is as much an organizational shift as a technical one. Utilities need governance, standards and cross\u2011functional buy\u2011in to treat GIS as an enterprise system of record. More organizations are turning to partners with multidisciplinary experience in GIS, OT and engineering to guide these changes and ensure that digital twins and IT\/OT integrations deliver safe, reliable outcomes.<\/p>\n

4. Cloud Services Define the Next Utility Operating Model<\/h3>\n

Cloud and managed services are redefining how utilities deploy,\u00a0secure\u00a0and scale their planning and operational systems. Rather than owning and\u00a0operating\u00a0all infrastructure in\u2011house, utilities are moving analytics, AMI, GIS\u00a0and even elements of ADMS\/DERMS support into cloud environments. The result? Faster deployments,\u00a0lower infrastructure\u00a0overhead\u00a0and improvements in performance and elasticity (scaling resources).\u00a0\u00a0<\/p>\n

Cloud\u2011hosted platforms\u00a0enable processing\u00a0high\u2011volume data at the scale\u00a0required\u00a0for real\u2011time analytics and AI workloads. Resilience improves as workloads are distributed across regions with built\u2011in disaster recovery, a\u00a0critical advantage as extreme events and cyber threats become more frequent and complex. Security can also be strengthened through modern cloud controls, though it requires disciplined governance and alignment with critical infrastructure standards.\u00a0\u00a0<\/p>\n

For midsized and municipal utilities, shared multi\u2011tenant cloud solutions\u00a0provide\u00a0enterprise\u2011class capabilities without requiring enterprise\u2011level staff and budgets. Cloud deployment accelerates experimentation: utilities can spin up pilots for new\u00a0analytics,\u00a0AI use\u00a0cases\u00a0or customer programs without multi\u2011year procurement cycles.\u00a0<\/p>\n

However, cloud adoption reshapes roles and responsibilities. Utilities need clear RACI models to avoid\u00a0accountability fragmentation\u00a0across IT, operations,\u00a0vendors\u00a0and regulators. Cloud\u2011native integration patterns\u00a0are becoming the\u00a0standard for tying together AMI, GIS,\u00a0markets\u00a0and control systems, demanding new skills and architectures.\u00a0<\/p>\n

In this environment, expert partners increasingly shift from one\u2011time project delivery to ongoing operational\u00a0partnership. Managed services providers help utilities\u00a0operate\u00a0cloud environments, tune data\u00a0pipelines\u00a0and manage application performance over time. This\u00a0allows\u00a0internal teams to focus on strategy, regulatory\u00a0engagement\u00a0and customer outcomes rather than infrastructure management.<\/p>\n

5. Data Quality Becomes the Gatekeeper for Modern Utility Functions<\/h3>\n

As utilities lean on\u00a0real-time\u00a0analytics,\u00a0sophisticated modeling,\u00a0AI and\u00a0data visualization, data quality\u00a0matters more than ever. Poor data\u00a0quality\u00a0in GIS, AMI,\u00a0or asset systems leads directly to planning errors, mismodeled circuits, incorrect operating\u00a0limits\u00a0and flawed\u00a0decision-making.\u00a0<\/p>\n

In 2026, leading utilities\u00a0will\u00a0treat data as a strategic asset with defined ownership, standards, validation\u00a0rules\u00a0and lifecycle governance. Data quality assessments are increasingly a first step before deploying new analytics, control\u00a0systems\u00a0or market interfaces. Utilities ensure that\u00a0timely,\u00a0accurate, and complete data is used for\u00a0models, forecasts,\u00a0querying\u00a0and decision-making.\u00a0\u00a0<\/p>\n

For example,\u00a0high\u2011quality substation and feeder models are recognized as prerequisites for advanced ADMS functions such as fault location, isolation and service restoration. Enrichment tools and mobile applications like Lemur allow crews to collect accurate locations, materials and attributes at installation, reducing guesswork years later when planning upgrades or responding to outages. These improvements often deliver immediate returns by reducing truck rolls, shortening investigations and avoiding repeat field visits driven by inaccurate records.\u00a0<\/p>\n

AI initiatives are particularly dependent on curated,\u00a0labeled\u00a0and governed datasets. Without this foundation, machine learning models can amplify underlying errors instead of improving insight, creating a false sense of confidence in automated outputs. Regulatory scrutiny is also rising around infrastructure records and cost recovery, making\u00a0accurate, traceable data critical for compliance\u00a0and for\u00a0defending investments in rate cases.\u00a0<\/p>\n

Solving the data dilemma starts with people. Utilities should develop processes and workflows for engineering, IT and field crews to operate together. Experienced partners can help utilities design pragmatic data roadmaps, set realistic standards, develop innovative and efficient data automation approaches and embed data stewardship into day\u2011to\u2011day workflows so that everyone sees data quality as part of their job.\u00a0\u00a0<\/p>\n

6. Energy Markets and Flexible Load Transform Utility Operations\u00a0<\/span><\/h3>\n

Today\u2019s utilities must contend with rising demand and explosive\u00a0growth in data centers. This means moving\u00a0from passive load\u2011serving to active portfolio and risk management\u00a0that\u00a0coordinates\u00a0flexible resources\u00a0and effectively\u00a0manages\u00a0congestion and volatility.\u00a0<\/p>\n

That\u2019s\u00a0where innovation plays a paramount role.\u00a0Advanced analytics support congestion modeling, pricing\u00a0forecasts\u00a0and resource optimization, helping\u00a0utilities to navigate tighter transmission constraints and variable wholesale prices. Flexible load programs, including\u00a0demand response, dynamic rates and other customer\u2011side resources,\u00a0can reduce peak demand.\u00a0<\/p>\n

DER portfolios, including solar, storage, EVs and building loads, are increasingly configured as aggregated resources capable of bidding into ISOs\/RTOs or responding to granular price signals. Data centers and large\u00a0industrial customers\u00a0are becoming strategic partners in flexibility, offering controllable loads and\u00a0on-site\u00a0generation or storage in exchange for tailored tariffs and reliability commitments. These arrangements require robust data pipelines\u00a0and validation processes\u00a0that meet\u00a0regulatory\u00a0requirements.\u00a0\u00a0<\/p>\n

To execute this shift, utilities must align planning, operations,\u00a0rates\u00a0and regulatory strategy so that market participation\u00a0complements\u00a0reliability and customer equity\u00a0objectives. Cloud\u2011based platforms and scenario tools allow utilities to simulate market positions, test\u00a0bids\u00a0and evaluate risk before committing in day\u2011ahead or real\u2011time markets. Experienced market practitioners and advisors can help interpret evolving rules, set up systems and processes and manage the full lifecycle of market participation. For many utilities, partnering with experts who understand both markets and grid operations is the fastest way to turn flexible load and DER portfolios into reliable, revenue\u2011generating assets.<\/p>\n

7. AI Moves from Experimentation to Operational Discipline\u00a0<\/h3>\n

In 2026, utilities are fully embracing AI and machine learning to perform massive processing, handle mundane tasks, and empower staff in day\u2011to\u2011day utility operations. Companies are shifting from pilot projects to industry-specific applications that solve specific problems such as asset condition assessment, outage analysis, vegetation management and anomaly detection.\u00a0<\/p>\n

Location\u2011based AI enables utilities to extract value from imagery, video,\u00a0and GIS data, automating tasks\u00a0such as\u00a0asset identification, pole and equipment recognition,\u00a0and vegetation encroachment detection. AI\u2011driven station modeling, using geo\u2011imagery and pattern recognition, can generate substation models faster and more accurately than manual drafting, feeding digital twins with up\u2011to\u2011date representations.\u00a0<\/p>\n

On the data side, machine learning models help to monitor AMI and SCADA streams to identify fraud, malfunctioning meters and emerging equipment failures. AI\u2011based scoring helps prioritize field work by ranking assets and locations based on failure risk, safety impact and customer consequences. AI also supports customer programs by segmenting participants, predicting demand response performance and optimizing targeting for energy efficiency and flexibility initiatives.\u00a0<\/p>\n

Making AI an operational discipline requires robust practices, such as versioning, monitoring, retraining and governance, so models are managed as living components of the grid. Utilities need auditable AI, especially in safety\u2011critical applications, which places a premium on models that reflect engineering realities.\u00a0<\/p>\n

Here again, human experts remain central. Engineers and data scientists must collaborate to define use cases, curate training data and interpret results in the context of local system conditions and regulatory expectations. Partnerships between utilities and specialized firms that combine deep domain knowledge with AI expertise are accelerating adoption while reducing the risk of misapplied automation.\u00a0\u00a0<\/p>\n

8.Asset Management And Intelligence Become Even More Important\u00a0<\/h3>\n

Comprehensive asset management, empowered by digital solutions,\u00a0is\u00a0emerging\u00a0as a defining discipline for utilities to balance reliability, resilience,\u00a0affordability\u00a0and decarbonization. Traditional asset management systems were designed\u00a0to\u00a0catalogue\u00a0and categorize physical\u00a0assets.\u00a0Utilities would deploy a system to build an\u00a0asset\u00a0registrar to justify a return\u00a0from a\u00a0public service commission.\u00a0\u00a0<\/p>\n

But assessment in 2026 requires more than managing the physical asset. Modern asset management focuses on optimizing infrastructure using sophisticated data and analytics. These digital applications help utilities oversee how assets are maintained, extended and perform under changing climate, weather, wildfire and other conditions. \u00a0<\/p>\n

Historically, detailed asset health assessments\u00a0required\u00a0costly, specialized sensors and expert analysts reviewing patterns at headquarters, limiting capabilities to a small subset of high\u2011value equipment. Advances in UAVs, sensors and analytics have fundamentally transformed these processes. \u00a0<\/p>\n

Today, low-cost sensors and real-time communications can be deployed on demand and at scale, capturing continuous data streams from critical assets at a fraction of the cost. High-definition imagery, coupled with AI computer vision, has turned image acquisition and management from a fringe technology to a core operational pillar for the modern utility enterprise. Real time AI models ingest this data along with weather feeds and operational variables to forecast asset performance and remaining life, moving utilities closer to proactive lifecycle asset management.<\/p>\n

Modern\u00a0asset management\u00a0is\u00a0a\u00a0coordinated\u00a0compendium of systems,\u00a0processes\u00a0and people. Working together toward better outcomes, technologies like\u00a0GIS, Lemur\u00a0mobile mapping, AI\u2011driven station modeling, AMI 2.0, and control systems contribute to the asset intelligence puzzle. Combined, experienced practitioners can design the architecture, govern the data, and interpret cross\u2011domain signals,\u00a0enabling\u00a0utilities\u00a0to\u00a0surpass traditional\u00a0management\u00a0to achieve maximum\u00a0performance and risk management.\u00a0<\/p>\n

The overarching opportunity involves using modern sensing and analytics to understand how assets degrade, how they respond to diverse climatological conditions and which interventions deliver the best value for customers and regulators. Expert partners who understand both the investment cycle and operational realities help utilities build roadmaps, prioritize use cases and ensure that comprehensive asset management strategies truly leverage today\u2019s digital toolbox rather than recreating yesterday\u2019s registries in a new interface.\u00a0<\/p>\n

Looking\u00a0Ahead:\u00a0Innovation Requires People<\/span>\u00a0<\/span><\/h3>\n

A<\/span>cross all eight trends, the common thread is clear: technology is amplifying the importance of expert judgement, not replacing it. Smart grid solutions, from converged control systems and AMI 2.0 to GIS, cloud platforms, AI and comprehensive asset management, deliver maximum value when experienced practitioners design, produce and refine them over time.\u00a0<\/p>\n

Ready to Innovate? TRC Can Help.\u00a0<\/span>\u00a0<\/span><\/h3>\n

For utilities looking to digitally transform in 2026, success leveraging both innovation with trusted partners who understand planning, markets and operations. By working with experienced experts, utilities can build comprehensive asset management strategies that turn data into reliability, safety and affordability gains for the communities they serve.\u00a0<\/p>\n

For utilities looking to digitally transform their operations, TRC can help. We offer services to turn these eight high-tech developments into practical, measurable solutions that enhance reliability, resiliency and service quality. By combining deep engineering roots with IT\/OT, cloud and analytics expertise, TRC works as an extension of utility staff from strategy through implementation and support. The result is integrated, optimized programs that align operations, regulatory goals and customer expectations.\u00a0<\/p>\n

\u200bSpecifically, our practitioners provide support across the full lifecycle of modernization initiatives, from roadmap design and vendor selection to system integration, field\u00a0enablement\u00a0and managed services. Our teams bring hands-on experience with ADMS, DERMS, AMI 2.0, GIS, mobile mapping, AI and cloud, helping utilities move from pilots to business-critical, converged operations centers and data-driven asset management.\u00a0<\/p>\n

\u200bTRC helps utilities:\u00a0<\/p>\n