Utilities face growing pressure to modernize aging infrastructure, improve reliability, meet regulatory expectations, manage workforce transitions, reduce environmental impact, and demonstrate measurable results—often with constrained budgets and resources and fragmented data.

This pressure will continue to drive utilities to make significant investment in operational technology (OT) and information technology (IT) but disconnected systems among OT and IT still limit visibility, coordination, and the ability to convert data into business results.

For many utility leaders, the priority is ensuring OT/IT investments deliver measurable operational results and a meaningful return on investment. This is easier said than done because turning OT/IT into operational value requires alignment across:

• People: Skills, training, cross-functional collaboration, and change readiness
• Process: Standardized workflows, formal decision frameworks, and defined accountability
• Governance: Clear ownership of data, along with standards for quality, content, security, and access
• Data: Accurate, complete, accessible, interoperable, and trusted information
• Technology: Integrated, secure, and scalable systems

While alignment (shared purpose) can ensure everything is moving in the same direction, collaboration (shared execution) is also required between OT and IT to effectively work together and apply the technology, resources, processes, and governance principles. However, to achieve meaningful, long-term operational value, a third aspect – relationship (shared trust) between OT and IT leadership must exist to successfully accomplish things of significance, like establishing a sustainable digital twin.

OT/IT Maturity Levels

For utilities to deliver effective and consistent operational results, a certain level of OT/IT maturity must exist. The concept of OT/IT maturity refers to how successfully a utility unifies its OT and IT. Given the various organizational, process, data, and technology issues a utility is often challenged with, utilities do not become OT/IT mature overnight.

Maturity progresses in stages as data becomes more accurate and reliable, and workflows and systems are more automated and connected. While there are many factors to consider in determining maturity, the following provides four basic levels for helping to gauge the state of your OT/IT maturity.

Level 1 Maturity: Manual and Disconnect Processes and Systems
At this level, your utility relies heavily on manual processes and fragmented data, making information difficult to easily access and dependent on individual knowledge. Operational assets run on isolated systems. Information is manually logged or stored in disconnected databases. These inefficiencies enable data silos and slow down execution of operational processes.

Level 2 Maturity: Automated Processes and Consistency Improvement
At this level, your utility applies technology to defined workflows, improving data capture and process consistency while systems remain only partly connected. At the early stage of digital maturity, systems and teams act in silos, but technology applies automated logic to handoffs between systems. This enables more informed planning, fewer surprises, and more repeatable work.

Level 3 Maturity: System Connectivity and Data Alignment
At this level, your OT and IT systems begin to connect, and data becomes more accessible across the utility. Devices stream real-time data from physical network assets directly into a cloud or local computing platform. Integration improves coordination, accelerates decisions, and strengthens OT/IT alignment.

Level 4 Maturity: Unified Digital Utility
At this level, your utility effectively leverages data, models, analytics, and artificial intelligence (AI) to anticipate risk and support proactive decisions. Fully converged OT/IT environments feature centralized governance and enterprise-wide integration. This improves risk management, resource allocation, and confidence in planning and operational decisions.

Defining the Digital Twin

Before discussing the dependency between OT/IT maturity and digital twins, it is worth reviewing what a digital twin is and why they will matter. Simple stated, a digital twin is a virtual replica of a physical network. It provides the real-world relationship between the physical as-built, and its digital representation tried to a geographic location. Combining three-dimensional mapping and connected network from GIS, real-time operational data from OT systems, and analytical tools, it provides the ability to simulate, monitor, and predict how physical networks behave over time.

The digital twin becomes the repository of all vital information about nearly every asset in a network and how it relates to every other asset. It includes such things as relationships, operational status, and workflows. As technology advances grid capabilities, digital twins will provide real-time insight for better operational coordination, planning, and reliability.

OT/IT Maturity Drives Digital Twin Capability

A digital twin’s capability is directly limited by a utility’s OT/IT maturity. That is because the extent of a digital twin’s ability is dependent on the alignment, collaboration, and relationship aspects noted earlier. As the underlying IT/OT infrastructure advances from basic, siloed networks to fully integrated, AI-driven environments, digital twins evolve from static three-dimensional models to highly predictive and autonomous decision-making operational systems. The following generally describes the phases of digital twin development:

• Phase 1 – Static Twin: The digital twin acts merely as a virtual three-dimensional blueprint or database of a distribution, transmission, or collection system network. It relies on GIS and design/engineering data for visualization and the network connectivity but lacks active monitoring or automated data flow.
• Phase 2 – Predictive Twin: The digital twin functions as a replica of the physical network and features, integrated with an advanced distribution management system or supervisory control and data acquisition system, and displaying operational and real-time data. With historical and real-time streams, it leverages analytics to simulate “what-if” scenarios, anticipate maintenance issues, and generate predictive insights.
• Phase 3 – Self-directed Twin: The digital twin can learn from operations, make automated adjustments, and take action on behalf of the operator, effectively optimizing the physical asset for maximum efficiency without human intervention. It can enable a self-healing grid system that uses sensors, AI algorithms, and automated switches to autonomously detect, isolate, and reroute power around faults.

Today, the maturity spectrum of digital twins within the utility industry varies significantly depending on the commodity. Many utilities are still in the investigative phase, utilizing a digital twin primarily for proof-of-concept for projects. Some of the more technically advanced electric utilities have integrated digital twins into their core operations, enabling real-time monitoring and decision-making across the organization. This evolution reflects a growing understanding of the strategic value that digital twins can provide, and the necessity for ongoing investment in technology and quality data.

As utilities continue to explore the potential of digital twins, it is crucial to assess their current maturity level and identify areas for advancement. By evaluating existing use cases and determining how digital twins can be expanded or integrated into operational functions, utilities can begin unlocking the value of these new opportunities for improving efficiency and innovation.

About the author. Dave DiSera is an executive consultant with SSP and is a former CIO. He holds a BS degree in Physical Geography with a concentration in computer mapping and an MBA with an emphasis in information technology management.