In 2025, the utility industry, like many others, was buzzing about the potential of artificial intelligence (AI) in day-to-day operations. The excitement continues into 2026, as highlighted by our UDC thought leaders in their predictions. In addition to AI, our leaders also point to the growing adoption of cloud technology and the increasing importance placed on regulatory compliance.
We’re excited to share the 2026 industry predictions from some of our UDC thought leaders!
Content Overview
Tom Helmer, Executive Solution Architect
AI as the Digital Coworker
The role of AI and interest in its capabilities will continue to grow within the utility sector. Utilities will initiate proof-of-concept projects to demonstrate AI’s capabilities as a ‘digital coworker’. AI’s agentic architecture will autonomously coordinate end-to-end workflows for new construction work—including enterprise asset management, graphic work design, and enterprise content management—as well as for asset management work—EAM, GIS, mobile GIS, mobile workforce management, imagery, and AI/ML (artificial intelligence/machine learning) image processing for condition assessments.
Gas Compliance
Distribution gas companies will begin allocating time and resources to ensure compliance with the Leonel Rondon Pipeline Safety Act. I anticipate that the Safety Act will require operators to go beyond recording and maintaining what’s in the ground; they will also need to show the operational status of their systems during incidents. Gas utilities will invest in GIS to provide the as-operating views and transparency needed to support predictive analytics and risk models to help prevent incidents.
Enterprising in the Cloud
In 2026, utilities will keep investing in cloud technologies. They will continue to move their digital enterprise delivery resource management (EDRM) portfolios and enterprise applications to the cloud. This will enable them to outsource the day-to-day management of their enterprise business systems in the cloud.
Jeff Zarse, Executive Consultant
Utilities Leverage AI for Recordkeeping
For utilities, the prominence of AI will continue to grow, transforming various business functions. As companies shift from paper-based processes to digital systems, much work remains to align historical data from paper sources with the current-state and future-state information architectures. I expect AI will help utilities identify and organize past paper records, as well as extract key structured data embedded within them. With data at the core of AI, I foresee data governance and information governance playing a significant role throughout utility organizations, with human subject matter experts evaluating and vetting AI results.
Upscaling of Electrical Generation
The AI arms-race will demand a substantial increase in electrical generation. The current US generation capacity is estimated at around 1.4TW, compared to China’s estimated 3.5TW. Moreover, China is growing at a much faster rate. It’s very likely we’ll see a significant rise in nuclear power generation from small modular reactors and other new generation sources.[1]
TJ Houle, VP Solutions Engineering
Technology Transformation
Real-world AI workflows will move beyond pilots into operational environments. Expect utilities to integrate physical AI—robotics, drones, and autonomous inspection equipment—into asset management and vegetation control. These workflows will help to reduce manual labor, improve safety, and accelerate outage response.
Hardware-software interdependency will deepen as utilities adopt edge computing and internet of things (IoT) sensors to proactively manage distributed energy resources. Predictive analytics will become standard for balancing grid loads, especially as electrification and electric vehicle adoption surge.
Grid Impacts from Shifting Delivery and Capacity Needs
The grid will experience bidirectional stress: increased demand from electrification and intermittent supply from renewables. Utilities will invest heavily in dynamic load forecasting and real-time geospatial visibility to manage congestion and optimize capacity.
Expect microgrids and localized generation to play a larger role, requiring advanced GIS integration for planning and resilience modeling.
Flexibility amid Regulatory and Tariff Changes
Regulatory uncertainty will force utilities to adopt scenario-based planning tools. GIS platforms will evolve to support tariff modeling and compliance tracking, enabling utilities to pivot quickly as policies shift.
Energy storage incentives and carbon credit programs will influence grid expansion strategies, making spatial analytics critical for siting and permitting.
Impact of Regulations on Clean Energy and Coal
Clean energy mandates will accelerate wind and solar siting analysis, but permitting delays and land-use conflicts will remain challenges. Utilities will lean on geospatial AI to streamline environmental impact assessments.
At the same time, the return of coal as a preferred option under certain administrations will create a dual-track planning environment. Utilities will need to reconcile legacy infrastructure upgrades with decarbonization goals, requiring robust spatial decision support systems.
William Craft, VP Enterprise Architecture
AI-powered Cloud Economics and Service Delivery
With increasing demand for better scalability and resiliency of systems, FinOps, a cloud financial management framework, will play a more critical role for utilities in 2026. FinOps will evolve from reactive cost-saving processes to a strategic operational model that reshapes spend governance and influences technology and business decision-making across cloud environments.
FinOps adoption is rapidly growing, with most large enterprises having formalized FinOps last year.[2] Cloud cost transparency and financial accountability across technology portfolios are transforming the focus from reporting into continuous optimization and execution. The key takeaway: FinOps will no longer be “budget policing”; instead, it will be embedded into how IT and OT teams architect, implement, and operate cloud systems.
FinOps expansion is quickly surpassing public cloud to include SaaS (software as a service), private clouds, hybrid clouds, and licensing costs. This trend indicates a strong likelihood that FinOps will be central to increasingly complex cloud environments to manage and visualize spend for real-time cost monitoring, anomaly detection, and AI-automated rightsizing. AI won’t just recommend optimization; it will become part of standard operating procedures, acting autonomously within policy guardrails and providing contextual cost alerts tied to key performance indicators like reliability, sustainability, and service uptime. As a result, FinOps will help cloud teams interpret cost impacts from workload surges, cost implications of distributed edge deployments, and variable graphics processing unit compute charges. This will make FinOps more predictive and proactive and less manual. The bottom line: FinOps will be the glue between cloud operations, IT architecture, and executive financial planning.
FinOps intelligence will be core to granular financial attribution of GIS data pipelines, spatial analytics jobs, and map rendering workloads in cloud environments. Cost tracking per spatial workload (i.e., extract, transform, and load (ETL), geoprocessing, real-time location services) will become a standard FinOps metric, much like cost per customer or cost per feature. The main prediction: utilities running smart grid analytics, IoT telemetry, and GIS-based predictive maintenance will leverage FinOps to link operational outcomes with return on investment.
In 2026, FinOps will be an essential, AI-augmented business discipline—seamlessly connecting cloud economics with utility service delivery.
Matt Zimmerman, VP Operations – Systems Integration
Rate Case Capitalization
I think we’ll see a further shift in rate case capitalization allowances for cloud technologies as the results of recent cloud proof of concepts and deployments, along with related operational and cost benefits, come in. As such, we’ll see more collaboration between technology vendors and cloud providers and an increase in cloud-related IT professionals. UDC will further grow its Architectural practices in anticipation of these new demands.
The GIS Market Evolution
The utility GIS market will begin to normalize and reach a steady-state spend around ArcGIS Utility Network as early adopters are achieving production Go-Live. The demand has never been stronger for GIS professionals to serve on Utility Network implementation projects globally. This demand should plateau and reach a steady state this year as Esri gets closer to sunsetting the geometric network technology stack. The industry will see an increased demand for integration services between the Utility Network and operational, IT, and EAM solutions. As the Utility Network’s critical velocity is achieved, there will be a natural progression to start tying the Utility Network to foundational systems such as ADMS, MWMS, EAM, and Design. UDC has continued to increase its workforce of highly skilled Utility Network-trained resources to meet this new demand.
Going Digital
We predict further growth in converging requirements between fieldwork, GIS, and more tactical hardware—driven by the need for zero-latency network management data and a fully digital workflow from design to construction to as-built. This demand has been stronger in the gas industry to meet stricter regulations at the state level, but we are also predicting an increased interest from electric field operations to collect data faster.
Ed Riegelmann, Sen. VP Federal GIS
Geospatial Data Governance to the Rescue
In 2026, Federal government agencies will continue on the path of strengthening geospatial data governance to enhance evidence-based decision-making through several strategic improvements. The Foundations for Evidence‑Based Policymaking Act of 2018[3] requires every agency to systematically build evidence to better inform policy decisions—including geospatial evidence. Formalizing geospatial data stewardship structures in agency-level Data Governance Councils in 2026 will be a pivotal effort to move GIS from anonymous project data to enterprise decision-ready knowledge.
A major initiative this year will be opening access to government geospatial data, especially to enable cross-agency data integration and reduced costs. The effort will be guided by the FAIR principles – making key geodata findable, accessible, interoperable, and reusable. Making this work will require educating staff and mandating complete and accurate metadata, including lineage to enforce trust and accountability for geospatial analysis data that can have a complex provenance.
AI Model Disclosure Standards Accelerate
In 2026, Federal agencies will require rapid development and improvement of disclosure standards for AI, including models used in geospatial analysis. AI models refer to a set of guidelines or requirements that dictate what information must be shared about an AI system to ensure transparency, accountability, and trust. Standards typically aim to help users, regulators, and stakeholders understand how the model works, its limitations, and its potential risks. AI governance is an absolute requirement to support trusted evidence-based decision-making by federal leaders.
Key elements of disclosure standards that will have the biggest impact on federal decision-making include:
- Alignment with ethical principles
- Regulatory limits
- Risk management
- Explainability of decisions in human-understandable terms
- Model known weaknesses, biases, edge cases, and potential harm
- In the case of geospatial data, AI models must have complete and accurate metadata to overcome no-trust decision frameworks.
Together, the Federal push for stronger geospatial data governance and accelerating AI model disclosure standards reflects a broader shift towards transparent, accountable, and evidence‑driven decision‑making in 2026. By expanding access to reliable geospatial data, enforcing FAIR‑aligned metadata practices, and requiring clear, responsible AI disclosures, agencies will be better equipped to generate trusted insights, manage risk, and ensure that both data and AI models serve as trusted foundations for mission‑critical policy decisions.
Read more thought leadership from our team or contact us to learn how we can help you achieve your 2026 modernization goals.
Read UDC’s previous industry predictions:
Footnotes
1. U.S. Energy Information Administration, https://www.eia.gov/international/analysis/countrv/CHN, dated 05 2025
2. State of FinOps 2025 Report, https://data.finops.org/, dated 2025
3. Foundations for Evidence-Based Policymaking Act of 2018, https://www.congress.gov/bill/115th-congress/house-bill/4174, dated 2019