If nearly every utility executive expects AI to drive revenue growth…why is turning that expectation into results still so difficult?
According to the IBM Institute for Business Value, 94% of utility executives expect AI to significantly contribute to revenue growth within the next three years. Meanwhile, 88% see it as a source of competitive advantage.
So, the expectation is clear. But, as we saw in our article “AI in Utilities: What Works Beyond the Pilot?”, the execution path is not.
Because the reality on the ground is complex:
- Grid operations are becoming harder to manage with renewables and electrification.
- Data is fragmented across SCADA, AMI, GIS, and billing systems.
- Regulatory pressure leaves little room for trial and error.
In that context, AI cannot sit on the sidelines as a separate initiative. It has to be embedded into the systems that already run the business.
And this is exactly where Oracle in utilities could make the difference.
A System-Level Approach to The Oracle Utilities Ecosystem
When we talk about Oracle in utilities, this does not refer to a single platform or a standalone AI solution.
What Oracle has built instead is a cloud-based, industry-specific ecosystem that combines core transactional systems, operational technologies, and a data layer where AI is embedded directly into workflows.
So, instead of a typical ERP adapted to utilities, this is a vertically integrated stack, designed around how the industry operates. And this distinction matters, because the value of the systems is in how they connect data, decisions, and execution across the business.
But let’s make a system-level approach to better understand and map how Oracle operates in utilities companies.
Customer & Revenue Management (CC&B / C2M)
This layer sits at the front of the utility value chain, managing everything related to customers, contracts, and billing. Solutions like Customer Care & Billing (CC&B) and Customer to Meter (C2M) are designed to handle:
- Complex tariff structures (time-of-use, dynamic pricing, multi-service billing).
- Customer lifecycle management (onboarding, service changes, disputes).
- Revenue assurance and regulatory compliance.
In practice, this is where consumption becomes revenue.
Within the Oracle ecosystem, this layer is tightly connected to metering systems (MDM), which feed it validated consumption data. That means billing is not just a financial process, but a data-dependent and operationally linked to metering accuracy.
For instance, a customer’s usage data flows in from metering, gets validated upstream, and is automatically translated into a bill. If something looks off (for example, a sudden spike), AI flags it before it reaches the customer, allowing companies to correct the issue proactively instead of handling a dispute later.
Metering & Data (MDM)
Now, if billing is only as good as the data behind it, then Meter Data Management (MDM) becomes a critical control point.
This layer of Oracle in utilities is responsible for:
- Ingesting high-volume AMI (smart meter) data.
- Validating, estimating, and editing (VEE) consumption readings.
- Structuring data for downstream systems.
In utilities, raw meter data is often incomplete, noisy, or inconsistent. MDM ensures that only clean, usable data flows into billing, analytics, and grid operations.
Within Oracle’s ecosystem, MDM acts as a central data hub, connecting:
- Smart meters and IoT devices.
- Customer systems (CC&B / C2M).
- Grid operations (ADMS / OMS).
But let’s put it in concrete terms to make it clearer how this works.
Smart meters continuously send consumption data, but not all of it arrives clean or complete. MDM processes those streams in real time, using AI to identify gaps or inconsistencies, correct them automatically, and pass forward a dataset that downstream systems can trust.
Grid Operations (ADMS / OMS)
Now we enter into the real-time operational core of Oracle in utilities. Their Network Management Systems, covering Advanced Distribution Management Systems (ADMS) and Outage Management Systems (OMS), are designed to:
- Monitor grid conditions in real time.
- Manage outages and restoration processes.
- Optimize load distribution and network performance.
This is essentially where utilities run the grid in real time, detecting failures, switching operations are executed to isolate or reroute power, and distributed energy resources (DERs) are coordinated as part of the network rather than treated as external inputs.
But what makes this layer more powerful within Oracle’s utility ecosystem is how tightly it connects to the rest of the ecosystem. Because grid operations are continuously:
- Pulling in meter data to understand consumption patterns.
- Interacting with customer systems to manage outage communication.
- Triggering field service workflows to dispatch crews with the right context.
So, in practice, this turns grid management from a set of disconnected actions into a coordinated, system-wide response. When a fault occurs, the system doesn’t just detect it. It assesses the likely impact, identifies affected customers, and helps prioritize response.
This way, teams are dispatched with context, while customers receive updates, all coordinated through connected systems rather than manual intervention.
Work & Asset Management
This layer of Oracle in utilities connects digital systems with physical infrastructure and field operations.
It focuses on:
- Asset lifecycle management (transformers, lines, substations).
- Maintenance planning and execution.
- Field workforce coordination.
In utilities, asset performance is critical. Failures are expensive, both financially and from a regulatory standpoint. Oracle’s approach integrates this layer with:
- Grid systems (to detect asset-related issues).
- MDM (to identify anomalies linked to equipment).
- Field service tools (to execute maintenance in real time).
So, instead of waiting for a transformer to fail, the system identifies early warning signs based on historical and real-time data. Maintenance is scheduled ahead of time; crews are dispatched with clear priorities, and the issue is resolved before it impacts service.
Opower Platform: AI Applied to Demand, Not Infrastructure
Traditionally, when utilities needed to manage demand, the response was structural: build more capacity, reinforce the grid, invest in new infrastructure. But those options, while useful, are increasingly expensive and slow to implement in a context where demand patterns are becoming more volatile.
That’s why most AI initiatives in the sector tend to focus on optimizing infrastructure. But Oracle in utilities introduces a different angle through Opower platform.
This is a cloud-based platform that sits within the Oracle utilities ecosystem in the customer engagement and demand optimization layer.
So, instead of operating the grid or process transactions, it uses predictive AI and behavioral models to influence how customers consume energy. We could summarize how this works like this:
- Opower platform ingests consumption data from systems like MDM.
- The platform enriches it with contextual signals, such as weather and household characteristics.
- Then, it predicts both usage and customer response to intervention.
- Finally, based on that, it delivers personalized communications (email, SMS, interactive voice response, etc.) designed to shift consumption patterns in measurable ways.
However, the strength of this approach lies in its results at scale. In fact, nearly 45 million households in the U.S. are benefitting from Opower’s AI-driven programs and insights. But let’s check some of the results:
- Over 3.5 billion personalized customer communications delivered across digital and physical channels.
- Over 100M high bill alerts delivered.
- 44.23 TWh of energy saved, equivalent to the consumption of over 100 million people.
- Nearly $4.3 billion in total customer bill savings (including $369 million in 2025).
But, beyond energy savings, the platform also contributes to improving customer lifecycle value. By proactively engaging customers with relevant, timely insights, utilities can reduce bill shock, increase satisfaction, and strengthen long-term relationships, an increasingly important factor as customer expectations evolve.
In short, what the Opower platform ultimately introduces is a different way to think about system optimization. Instead of only responding to demand through infrastructure, utilities gain the ability to shape demand before it materializes.
A Pattern to Oracle’s AI Strategy in Utilities
So, if there’s one consistent pattern in Oracle in utilities, it’s that AI is applied and embedded directly to operational workflows, with a clear priority on where it delivers value first. But, rather than treating AI as a single layer, this approach can be understood as a sequence of steps:
Practical guide of how to implement AI in utilities using a structured, value-first approach. It covers how predictive AI is embedded into core operations like metering, grid management, and asset maintenance, how integrated data becomes the backbone for scalable AI, and how intelligence is applied directly at decision points within workflows. It also explores the role of GenAI in improving how users interact with systems and data—positioned as a complement rather than the core engine. Ideal for leaders looking to increase efficiency, reduce costs, and build more intelligent, connected operations.
1. Start with Predictive AI in Core Operations
Oracle’s strategy begins where the impact is most immediate: operations.
Predictive AI is embedded into:
– Metering (data validation, anomaly detection).
– Grid operations (load forecasting, fault prediction).
– Asset management (predictive maintenance).
The focus here is straightforward: optimize how the system runs. These are high-frequency, high-impact processes where better predictions translate directly into cost savings, reliability improvements, and efficiency gains.
2. Use Data as the Backbone Across Systems
For predictive models to work, Oracle connects data across domains (metering, grid, customer, and assets). This way, the system builds a continuous data flow, where:
– Meter data feeds billing and grid visibility.
– Grid events inform field operations.
– Customer data provides context to consumption.
This step is less visible, but critical. Because, without it, AI remains fragmented and difficult to scale.
3. Extend AI to Decision Points Inside Workflows
Rather than generating insights that sit in dashboards, Oracle integrates AI directly into decision-making moments. This means:
– Anomalies are flagged before billing.
– Failures are predicted before they occur.
– Maintenance is scheduled before assets break.
At this stage, AI is no longer analytical. It becomes operational.
4. GenAI to Improve Interaction and Access
GenAI enters the picture, but in a more targeted way. It is primarily applied to:
– Customer service (call summarization, interaction handling).
– Data interaction (natural language queries, copilots).
The role of GenAI here is not to run operations, but to improve how humans interact with systems and data. And this reflects a clear positioning: Oracle is not “GenAI-first”, but using it as a complement, not as the core engine of value.
A Reality Check: What It Takes to Implement Oracle in Utilities
Up to this point, the value proposition behind Oracle in utilities is clear: a connected ecosystem where AI is embedded into operations and delivers measurable outcomes.
However, for most utilities companies, adopting this kind of system is not a software deployment. More likely, we’re talking about a multi-year transformation effort that requires both significant investment and highly specialized talent.
On the one hand, we are talking about an extremely complex environment, which has the necessity of:
- Integrating legacy systems (SCADA, GIS, AMI, ERP).
- Migrating large volumes of historical data.
- Adapting processes to regulatory and operational requirements.
So, even at a modular level, costs can escalate quickly. For example, a billing transformation (CC&B/C2M) or a grid modernization (ADMS/OMS) can each represent multi-million-dollar initiatives on their own, particularly when customization and integration are involved.
On the other hand, implementing Oracle in utilities systems requires a combination of skills that are difficult to find in a single profile:
- Functional experts who understand rate structures, regulatory frameworks, and utility operations.
- Technical specialists capable of handling integrations, data pipelines, and system configuration.
- Solution architects who can design how everything connects across IT and OT environments.
- Field and operational experts familiar with grid systems, assets, and real-world workflows.
This is not standard IT talent. It is hybrid expertise, sitting at the intersection of software, data, and energy operations.
In practice, this creates a strong dependency on system integrators and partners, especially in the early phases. Internal teams play a critical role but often need to be upskilled over time to take ownership of the platform.
How Inclusion Cloud Make Oracle Work in Utilities
At this point, the real question is how that value is actually realized in practice, under real operational constraints.
Let’s analyze Evergy case of the Oracle press center as an example.
Serving 1.4 million customers across Kansas and Missouri, the company leveraged Oracle’s Opower platform to support its transition to time-of-use (TOU) pricing. But this wasn’t just a pricing change but also a coordinated customer engagement at scale.
Through personalized communications, digital self-service tools, and continuous behavioral insights, this company achieved:
- 30% customer pre-enrollment in TOU rates.
- 80% of enrollments completed through digital channels.
- +$2 million savings in avoided call center costs.
But what we must understand is that this was not driven by a single feature (the Opower platform). It was driven by how the system connected customer data, communication channels, and behavioral AI into a coherent experience.
Because in this area, outcomes depend on how well the ecosystem is implemented, integrated, and aligned with the industry-specific context. That is: its legacy systems, regulatory environment, customer base, and operational model.
This is where experience starts to matter.
At Inclusion Cloud, we have been working as Oracle official partners for over two decades, supporting utilities in translating this ecosystem into operational results, not just system deployments. That means going beyond configuration, focusing on how each layer (metering, grid, customer, and AI) connects within the business.
Our approach is built around three principles:
- Business-first implementation: prioritizing use cases where AI and data integration can deliver measurable impact early.
- Architecture alignment: ensuring systems evolve in a way that supports long-term scalability, not just short-term fixes.
- Flexible delivery models: from targeted module implementations to full-scale transformations, depending on the organization’s starting point.
So, if you are evaluating how Oracle fits into your roadmap or looking to move from fragmented initiatives to a more integrated model book a discovery call.