What if the biggest opportunity for AI in utilities isn’t in transformation programs but in the work happening every day? 

Across business units, manual workflows have not meaningfully evolved in years. There are redundant processes across systems, information gaps that create rework, and highly skilled employees spending time on repetitive tasks. These inefficiencies are widespread, measurable, and happening every single day. 

And yet, they are rarely the focus of enterprise AI efforts. 

At the same time, utilities are being asked to deliver more with the same or fewer resources. Grid modernization, reliability, and growing demand are colliding with regulatory and financial constraints. The challenge is not knowing what needs to change. It is finding the time and capacity to do it.  

That is where AI starts to matter. 

AI Is Advancing But Not Where Most Work Happens 

Utilities have made meaningful progress with AI. Many have articulated strategies, invested in data platforms, and launched AI Centers of Excellence. Yet that progress is not scaling across the organization. 

Most AI efforts remain focused on large, enterprise-level initiatives. These programs deliver value, but they are slow, tightly governed, and difficult to operationalize at scale, especially in a regulated environment where approvals take time and risk tolerance is low. 

The result: AI exists, but largely in silos. It is not consistently embedded in the day-to-day workflows where the majority of operational and customer work occurs. 

In conversations with utility leaders, a consistent pattern emerges. Clients want to understand what we are doing in AI. They ask about trends, hot topics, and what is coming next. But when the conversation turns to large and ambitious AI initiatives, the response often becomes more cautious. We hear things like, “We are not ready for that,” or “Our culture is still scared of AI.” 

That gap between interest and readiness is exactly why practical, workflow-level use cases matter. They allow utilities to build confidence and demonstrate value without requiring the organization to take on more risk than it is prepared for. 

The Gap Between AI Strategy and Execution 

There is a clear gap today between AI as a strategic priority and AI as something that meaningfully improves how work gets done. 

Across utilities, teams are still re-entering data across systems, chasing down missing or incomplete information, and working through processes that rely heavily on manual effort. These are not edge cases. They are part of daily operations. 

And they represent one of the most immediate opportunities for AI to deliver value. 

For utilities looking to take a more practical approach to AI, the starting point need not be complex. 

A More Practical Path to AI in Utilities 

A more effective approach starts with a simple shift in thinking. Instead of beginning with large transformation programs, it starts with the work already in motion. 

It focuses on where time is spent, where work slows down, and where teams rely on manual or repetitive processes. From there, the question becomes practical: Where can AI make this easier? 

 

Adoption Matters as Much as Innovation 

There is no shortage of innovation in AI today. The real challenge is adoption in the power and utility industry. If a solution is too complex, too disruptive, or too difficult to trust, it will not scale, regardless of how advanced it is. 

The most effective use cases are low risk, easy to integrate into existing workflows, and provide immediate value to the people doing the work. When teams experience that value directly, adoption happens naturally. 

Designed for How Utilities Actually Operate 

Utilities operate in a regulated environment, and that reality should shape how AI is introduced. 

Solutions need to be easy to explain, easy to govern, and low risk to implement. The goal is not to introduce unnecessary complexity, but to improve operations in a way that aligns with how the business already runs. 

This is why smaller, focused use cases tend to move faster. They are easier to approve, easier to test, and easier to expand once the value is proven. 

Start Small, Build Momentum, and Scale 

On the surface, these use cases may appear incremental. 

In practice, they are foundational. 

They improve efficiency across teams, build confidence in AI, and create a path for broader adoption over time. As more of these use cases are implemented, AI becomes part of how work gets done rather than something separate from it. 

What This Looks Like in Practice 

This approach is not theoretical. It is already happening. In our work with utilities, we are seeing practical ways AI can improve day-to-day operations in meaningful ways. 

Use Case 1: Reducing Rework and Backlog in GIS Work Order Processing 

In one engagement, a distribution team was working through a backlog of GIS work orders. 

A team of approximately sixteen resources was responsible for reviewing work orders, interpreting field information, and manually entering updates into the GIS system. The process was time-consuming and highly manual. 

Incomplete or missing information often led to rework between GIS and field operations. In addition, work orders were typically processed individually, even when they were related or located in close proximity. 

To improve this process, an AI-enabled solution was introduced to support the existing workflow. 

The solution identifies incomplete or missing information at the beginning of the process and routes those work orders back to field operations for completion before GIS work begins. It also analyzes work orders for proximity and similarity, grouping those with nearby or related addresses so they can be processed together more efficiently. In doing so, it helps standardize how work orders are reviewed and interpreted. 

The result is not a reduction in the need for GIS expertise. Instead, it allows the team to reduce rework, improve throughput, and move through the backlog more efficiently. 

Use Case 2: Improving Accuracy and Speed in Gas Operations Cost Allocation 

In another example, a gas operations team was addressing challenges related to cost allocation. 

When gas repair work is completed, teams are responsible for documenting the work performed, the equipment used, and whether the associated costs should be classified as operating expense or capital. 

In practice, this process was often inconsistent and highly manual. Analysts were required to search across multiple spreadsheets to identify equipment and determine how costs should be classified. This created inefficiencies, introduced variability, and increased the time required to complete reviews. 

At the same time, accurate cost allocation has direct financial and regulatory implications, making consistency especially important. 

To support this effort, a solution was introduced that consolidates multiple data sources into a single, accessible layer. Analysts can use a conversational interface to quickly determine whether a cost should be classified as operating or capital, based on existing data and rules. 

This does not remove the need for analyst judgment. Instead, it enables faster, more consistent decision-making, reduces time spent searching for information, and allows analysts to focus on higher-value work. 

Why This Approach Works 

Across both examples, a few patterns emerge. 

The underlying work remains the same, and the core systems do not need to be replaced. The focus is on improving specific points within existing workflows rather than redesigning the entire process. 

This approach works because it is practical. It applies directly to real work and delivers immediate value. It is low risk, operates within existing processes, and is easier to approve and adopt. It supports the workforce by helping people do their jobs more effectively rather than replacing them. And it builds momentum, as each success increases trust and creates opportunities for broader adoption. 

Getting Started: Where to Look First 

For utilities looking to take a more practical approach to AI, the starting point need not be complex. 

It begins by identifying where time is spent on manual or repetitive work and where teams are consistently slowed by missing information, rework, or inefficient processes. 

To make that process repeatable, we include an Automation & AI Opportunity Brief as a standard deliverable in every engagement. It captures the highest-potential workflow use cases observed during the work; what to automate or augment; the value hypothesis, feasibility, and risk considerations; key data and system dependencies; and recommended next steps to pilot and scale. 

From there, the focus should remain on existing workflows, systems, and data rather than on introducing entirely new platforms. 

Initial use cases should be small, easy to understand, low-risk, and visible enough for teams to recognize their value. 

Most importantly, solutions should be designed with the end user in mind. If the tool is easy to use and clearly improves the day-to-day experience of the workforce, adoption will follow naturally. 

Final Thought 

AI delivers the most value when it gives time back to the business. By reducing inefficiencies in everyday work, utilities can better utilize their time, money, and people, redirecting those resources toward investment and transformation. 

When small gains in daily work begin to add up across the organization, what becomes possible for utilities looking to meet this moment? 

At Everforth Apex, we offer a comprehensive range of technical services, including generation facilities, renewable energy projects, optimization, smart grid, resiliency, asset management, infrastructure, digital platforms, smart meters, battery systems, and renewable technologies. Our goal is to drive optimization and operational efficiency while ensuring cybersecurity, regulatory compliance, and sustainability.  

About the Author: Shandi Ramsey is the Segment Director for the Utilities Industry at Everforth Apex, with over 16 years of experience in technology services. She oversees sales and project delivery for over 55 utility customers across the US and Canada.