Abhay Gupta is cofounder and CEO of Bidgely, evolving energy analytics for utilities with the power of data and artificial intelligence.
The utility industry’s digital journey has reached an inflection point. Utilities have spent the past decade building the foundation: smart meters, robust customer information systems (CIS) and expansive data lakes. Yet, for many chief information officers (CIOs), those massive investments have translated into data volume, not necessarily breakthrough operational value.
The world is captivated by horizontal artificial intelligence (AI)—the general-purpose systems from titans like OpenAI (ChatGPT), Google (Gemini) and Microsoft (Copilot). These tools are excellent for revolutionizing back-office administration, content creation and general business tasks. They offer broad efficiency but, by design, lack the deep, segment-focused knowledge and security needed for critical infrastructure.
The utility industry has the opportunity to pivot from generic tools to truly transformative, domain-specific intelligence. The answer lies in vertical AI: purpose-built systems that empower the energy CIO to be the architect of the smart, resilient grid.
How Vertical AI Delivers Unprecedented Value For Utilities
Horizontal AI is a fantastic engine, but it lacks the necessary fuel and roadmap for the utility world. It delivers broad value but critically misses the relevant knowledge required to manage electricity at scale and navigate regulatory mandates. It’s broad, but shallow.
Vertical AI, conversely, is engineered from the ground up to be natively versed in the energy ecosystem. It is the crucial translation layer that converts the utility’s raw data into strategic insights. With industry-specific operations and data, vertical AI is deep, contextual and action-oriented, delivering value propositions like predictive grid intelligence, hyper-personalized customer experience and operational risk mitigation.
For the CIO, vertical AI represents the strategy to finally operationalize those smart meter, grid telemetry and data lake investments—securely deploying advanced machine learning models in their preferred data environment. It’s the difference between asking a general-purpose AI to draft an internal memo and empowering a vertical AI system to prevent a transformer overload before it causes an outage.
Unlocking True Energy Insights
The core of vertical AI for the energy sector is its ability to extract and apply domain knowledge to vast, complex datasets. This foundation enables a crucial capability: transforming bulk meter readings and grid signals into rich operational and behavioral profiles for every asset and customer.
This specialized AI is trained on actual energy consumption patterns, grid dynamics and external factors like weather. This is how the CIO enables their organization to gain intelligence impossible to derive from simple data aggregates:
• Load Forecasting Precision: Moving beyond statistical methods to predict load at the feeder, transformer and individual home level, which is essential for managing the growth of EV charging and solar adoption
• Proactive Asset Management: Identifying localized stress on the distribution grid when high-load appliances or distributed energy resources (DERs) operate simultaneously, helping engineers prioritize maintenance
• Customer Personalization: Understanding energy usage patterns to propose hyper-relevant rates, tariffs and efficiency programs, strengthening customer trust and boosting program participation rates
This level of granular, predictive insight is what finally closes the loop, positioning the energy CIO as the leader who connects historic data investments directly to end-to-end operational transformation.
Navigating Your Vertical AI Journey
As a utility CIO, your greatest challenge—and opportunity—is moving the organization from AI experimentation to strategic, scalable deployment. The pivot to vertical AI is a move that requires a focus on context and cohesion.
Stage 1: Foundational Readiness (The “Why”)
• Ensuring Data Cohesion: The first test is seamless data flow. If your core systems (like AMI, CIS and ADMS) aren’t speaking the same language, vertical AI will be starved of the integrated data it needs to work.
• Identifying Critical Use Cases: Skip the generic proof-of-concept. Focus on high-value problems that only energy knowledge can solve: integrating DERs, improving peak load management or reducing localized outage frequency.
• Integrating Domain Experts: Embed subject matter experts (SMEs) from operations, engineering and customer experience directly into the IT team. Their intimate understanding of utility processes is essential for training and validating vertical AI models.
Stage 2: Strategic Deployment (The “Who”)
• Insisting On Energy-Native Models: Your partner must prove their AI is built on high-granularity, time-series energy data, not general data science templates. The depth of their energy domain experience is the most critical factor.
• Demanding Interoperability: Vertical AI should act as an intelligence layer that enriches your existing core systems, not as a disruptive rip-and-replace project. Its insights must be easily consumable by your current operational tools to accelerate time-to-value.
• Prioritizing Model Transparency: For a mission-critical industry, trust is everything. Insist on models that are auditable and transparent. Your team needs to understand how a prediction was derived to meet regulatory standards and confidently manage real-world risk.
Stage 3: Scaling And Governance (The “How”)
• Treating AI As Enterprise Infrastructure: Shift your mindset. Vertical AI is not an application; it is a foundational utility that powers many applications across the enterprise. Its insights should inform everything from billing alerts to crew dispatch optimization.
• Measuring Avoided Cost: Focus on metrics that translate into financial and reliability gains: “accuracy translated into avoided cost” (e.g., fewer outages, reduced peak capacity costs, higher program participation), not just model accuracy scores.
Leading The Intelligent Utility
The convergence of massive data investments and the insights of vertical AI is paving the way for the truly intelligent utility. The days of applying generic, broad-brush solutions to core grid and customer challenges are behind us.
For the utility CIO, this represents a unique and powerful moment to lead. By making the strategic choice to invest in specialized vertical AI, you can transform your organization’s data lakes from repositories of potential into engines of operational excellence. You become the decisive architect of a secure, resilient and customer-focused energy future.
The opportunity is now, and your leadership is the key to unlocking the full value of your digital investments.
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