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Utilities today generate more data in a day than they previously analyzed in a month. AMI networks, IoT sensors, SCADA systems, field ops tools, and customer platforms now produce a continuous flow of telemetry data that is growing exponentially every year.
- The number of smart meters globally is set to increase from 1.7 Billion in 2023 to 3.4 Billion in 2033
- IoT investment in utilities is estimated to cross the $150 Billion mark by 2035
Every meter ping, transformer reading, outage ticket, voltage fluctuation, and consumption signature adds to a constantly expanding ocean of operational data.
And this explosion is not slowing down. It is intensifying.
From my experience working with utilities that manage millions of meters, there is one unavoidable reality that is looming: The data revolution has brought extraordinary visibility across AMI networks. However, every gain in visibility in turn created an equal, perhaps even larger increase in operational load.
More Data, More Problems, and More Possibilities: So What Should Utilities Do With All This Operational Visibility?
Smart meters and AMI rollouts have unlocked operational awareness and insights that the industry could not have imagined two decades ago. What once arrived as a monthly read is now a continuous stream of voltage and consumption trends, along with events and power quality signal capture every few minutes. Loss patterns that were once invisible can now be viewed at 15-minute intervals. Demand curves update in real time. Transformer health signatures can be correlated with loading, harmonics, and temperature. Fraud behavior can be inferred from communication anomalies or consumption inconsistencies. Voltage deviations, billing mismatches, and consumer-side irregularities can be detected at unprecedented granularity.
This is all remarkable progress.
But every insight also becomes a new responsibility and often a new problem to solve. Operators are now confronted with new and perhaps, even more complex questions:
- Why did a particular feeder’s technical losses spike today?
- Why did 7% of consumers suddenly show negative consumption?
- What caused a transformer to cross 95 degrees twice in a single week?
- Which consumer clusters are creating evening peak strain?
- How many meters went silent in the past two hours, and where?
These questions are essential to answer. But answering them manually, every day, across millions of data points, is an impossible task.
Utilities do not suffer from a lack of data. They struggle with the organizational and cognitive load needed to interpret what the data is telling them, and to act upon it fast enough.
If Every Use Case Expands With Real Data, How Can Utilities Keep Up?
Consider a real-life utility problem that seems simple on the surface: detecting and reducing meter tampering.
In theory this seems straightforward.
But once you integrate AMI scale data into the equation, the complexity multiplies instantly.
Suddenly the solution requires comparisons of load curves, detection of phase imbalance, communication behavior mapping, billing-to-metering alignment, feeder-level triangulation, correlations with weather and seasonality, anomaly detection from AI models, and connections to field ticket behavior.
This simple use case now demands an end-to-end data engineering pipeline, a reasoning engine, continuous monitoring, multi-system correlation, human decision workflows, and verification loops. And this is only one use case.
Utilities today manage dozens and often hundreds of such cases including transformer health, feeder losses, reliability indices, outage prediction, billing exceptions, fraud, power quality, voltage stability, and demand planning.
Every new data source expands the utility’s visibility, but it also expands the workload. The strain on operators, control rooms, and engineering teams is becoming increasingly difficult to manage.
What Does the Future of Utilities Look Like When Intelligent Agents Work on Operational Data?
This is why I believe the next decade in the utility sector will belong to agentic AI.
The next generation of utilities will not rely solely on dashboards and or after-the-fact reports. They will operate with AI agents that behave like highly skilled employees. Agentic systems will read data continuously, understand what patterns matter, evaluate the relevance of each signal, and either take action or recommend actions to humans.
In other words, instead of dashboards that ask operators to look at a particular point, we will have agents that already did the looking and the reasoning.
Imagine specialized agents such as a Transformer Health Analyst Agent, a Loss Reduction Officer Agent, a Fraud Detection Analyst Agent, a Reliability and Outage Monitoring Agent, a Billing Exception Auditor, a Power Quality Insights Agent, or a Demand Forecasting Planner.
Each agent plays a precise operational role, much like a human colleague. But unlike humans, these agents work continuously, process millions of inputs per hour, and never overlook patterns hidden deep within the noise.
When hundreds of such agents run simultaneously across AMI, SCADA, GIS, billing, CRM, and field systems, utilities unlock something they have never had before. They achieve a continuously monitored and continuously analyzed grid supported by digital intelligence.
What Kind of AI Will Actually Work for Utilities?
Generic LLMs will not solve utility problems. Cloud chatbots cannot manage grid signals. Static, rule based systems will not keep pace with real-world operational complexity.
Utilities need industrial-grade AI, designed specifically for this sector and built with mission critical safety requirements in mind.
Such AI must think in utility logic including loss formulas, tariff rules, feeder hierarchies, transformer norms, power quality thresholds, and operational workflows, from billing to field response. It must incorporate regulatory constraints, safety standards, and operational discipline. In short, it must be trained on specialized context, not generic internet text.
Equally important is data privacy. Utilities operate critical infrastructure, so AI platforms must run on private GPUs inside utility controlled VPCs or on-prem environments with strict data residency and zero external data transmission. Consumer LLMs cannot and should not be allowed to analyze smart meters or grid telemetry.
The AI that succeeds in utilities will be private by design.
It must also be fully agentic and not just predictive. The legacy AI model of input followed by prediction and dashboard is not enough. The modern approach is input followed by reasoning, followed by decision, followed by action, and finally verification. Utilities need agents that form hypotheses, validate them against rules and history, escalate exceptions, take initiative, and learn continuously from operator feedback.
And perhaps most critically, these agents must interact with humans like teammates. They must be transparent, explain their reasoning, show confidence levels, draft notifications, suggest load balancing actions, and provide auditable logs. They cannot be black boxes. They must be trusted colleagues.
Finally, utilities need a single AI platform that can scale across all use cases. They cannot juggle dozens of vendors and siloed tools. They need one foundational platform where new agents can be added by configuration, extended by internal teams, and gradually expanded into smart city operations.
This is exactly the direction that WorkOnGrid is building toward.
Why Is Agentic AI No Longer Optional for Modern Utilities?
The challenges facing utilities are growing rapidly. These include rising theft and losses, increasing DER variability, sharper peaks, ageing assets, stricter regulatory requirements, shrinking technical staff, and greater expectations for transparency and speed.
These pressures cannot be addressed with manual decision making or traditional reporting systems. Human-only workflows cannot keep pace with this new operational tempo and setup. Agentic AI is not a luxury. It is becoming the only sustainable way for utilities to manage modern grid complexity.
Agentic AI does not replace humans. It scales them. It gives every engineer twenty virtual assistants. It gives every operations manager a team of digital analysts. It gives control rooms a constant and tireless system that monitors, analyses, correlates, and guides.
This is not the next step of digital transformation. It is the next workforce.
Conclusion: Will AI Agents Redefine Utility Operations for the Next Generation of Utilities?
The next major shift in the utility sector will not come from new hardware or additional dashboards. It will come from AI agents that operate on top of AMI and operational data, working continuously to analyze, interpret, correlate, and recommend decisions.
In the years ahead, the most successful utilities won’t be the ones with the most data. They will be the ones that have agents to understand the data and act upon it. As cities and grids grow more complex, now is the time to invest in AI systems that operate with the clarity, discipline, and reliability we expect from our best human engineers.
That is the future WorkOnGrid is building. A future where AI agents enhance teams, accelerate decisions, reduce risks, and transform how utilities operate.
