For years, IT departments have been seen as necessary overhead: a cost center essential for operational efficiency but rarely a direct contributor to profit. But today’s era is defined by digital transformation that views technology and data as key organizational pillars, amplifying the job of Chief Information Officers (CIOs) into a powerful engine for growth. In fact, according to Gartner research, 84 percent of CIOs report their responsibilities expand beyond traditional IT functions to now include driving business outcomes.
Demonstrating how technology investments can generate new revenue streams hinges on CIOs being able to align their IT strategy with core business objectives. For utility CIOs in particular, this means championing initiatives that prioritize grid stability, load management as well as regulatory-funded programs (particularly energy efficiency and electrification). The best — and most straightforward path — to achieving this alignment is to dig into the data.
By now, most utilities have invested heavily in smart meters and are collecting millions of data points every day. This is where utility CIOs have an open opportunity to first ensure their organization is getting the biggest ROI for those smart meters, then use that data to boost specific business initiatives.
Unearthing a Utility’s Most Valuable Asset: Data
Smart meter data, in its raw and unprocessed state, is often housed by utilities within third-party enterprise data platforms like Snowflake, AWS Redshift, Azure, etc. Besides smart meter data, utilities are also leveraging these data lakes, or data warehouses, to store GIS data and network mapping data, especially in the context of advanced analytics and grid modernization. While this array of data holds immense value, its full potential becomes unleashed once it moves from numbers on a spreadsheet to actionable insights.
This is where artificial intelligence (AI) becomes transformative. When layered onto existing data infrastructure, AI increases the value of this repository of data by extracting highly granular and meaningful intelligence about energy consumption across an entire service territory. Specifically, AI can assess behind-the-meter insights and itemize a home’s usage patterns not only by standard home appliances (i.e. refrigeration, lighting, heating, cooling, laundry, dishwasher) but also by emerging technologies like electric vehicles (EVs) and solar.
The good news is this AI layer that integrates directly into a CIOs existing IT stack minimizes disruption and accelerates time to value without overhauling foundational systems. While adding a layer of AI software does tack on a new capital expense, the cost savings achieved by lowering operating expenses (in some cases by 20 to 40 percent) across multiple departments and unlocking a myriad of customer-oriented and grid-focused use cases is an investment that pays for itself over time.
Turning Digital Information into Financial Value
Having unlocked the intelligence within smart meter data with an AI layer, CIOs and their teams can now effectively translate these insights into action.
Let’s explore how this information can be used to enhance EV charging related load management, for example.
Within minutes, grid managers can easily identify which customers have EVs, then segment EV customers based on their individual charging patterns — think: on-peak versus off-peak charging. The same can be done to isolate the highest consumption EV customers. Now, demand-side management (DSM) or customer program teams can target those high consumption, on-peak customers for valuable load shifting initiatives. These initiatives could include enrollment in time-of-use (TOU) rate plans and managed charging programs, or even incentivized behavioral nudges.
Another example is to focus on heat pumps as a means to support energy efficiency initiatives. Program teams can pull from their AI-powered behind-the-meter analytics to identify which homes have inefficient HVAC systems in order to streamline the strategy around upgrade outreach.
In these scenarios, the utility is spending less budget on mass outreach by only focusing on customers with the highest propensity for conversion and productivity among grid managers increases as they no longer have to spend weeks or manually running reports on consumption patterns. With more customers enrolled in TOU rates and managed charging programs, peak demand is also reduced, which leads to deferred or avoided costly infrastructure upgrades and lower wholesale energy procurement costs for the utility.
Streamlining Workflows and Employee Productivity
While managing multiple, often interconnected, demand side management programs can be complex and resource-intensive, adding an AI layer onto enterprise data platforms also enables utilities to free employees from the weight of routine administrative tasks—from customer communications and data synthesis to performance tracking.
Utilities can then implement more programs aimed at optimizing these core business objectives— grid stability, load management, and energy efficiency— without expanding their workforce.
This AI layer also allows utilities to implement more diverse programs, optimize program design and evolution, and increase program participation through personalized outreach.
Of course, the elephant in the room is the AI “readiness” of this existing workforce. Many organizations believe they need to hire highly specialized AI data scientists and engineers to build these AI systems and translate the data into digestible intel.
Fortunately, early demonstrations from GenAI are hinting that this may become less of a concern. Certain pilot programs are showing that any employee, even those without data science backgrounds, can directly engage with large data sets by asking simple conversational questions like,”Which customers have the highest grid impact based on their EV charging?” or, “How many customers have Level 2 EV chargers?”
No need to write code and no need to understand intricate data structures.
Empowering any employee to access and interpret complex data through natural language queries democratizes data analysis and leads to faster, more informed decision-making across the organization. Ultimately, this agility can allow utilities to proactively respond to market changes, capitalize on emerging opportunities, and ultimately enhance profitability all the while maintaining grid reliability and customer satisfaction.
Igniting Business Growth Through AI-Powered Data
For CIOs in the energy sector, transforming IT from a mere cost center to a powerful revenue driver isn’t just a goal—it’s essential for survival and growth. This means CIOs must champion AI as a core competency and cultivate a data-driven culture throughout the organization. It’s about forging strong partnerships across business units that combine the immense volume of data utilities already possess with the power of AI to pinpoint and seize new revenue-seeking, cost-saving opportunities.