As solar panels, batteries, electric vehicles and other distributed energy resources reshape electricity networks, utilities are facing a new computing problem: critical grid decisions increasingly need to happen closer to where electricity is generated and consumed. Landis+Gyr is expanding its answer to that challenge in Australia, adding Future Grid and Operational Technology Solutions (OTS) to its Edge Application Ecosystem.

The move adds new analytics and operational applications to Landis+Gyr’s open platform, which is designed to process grid data closer to the edge and help utilities respond to changing network conditions in real time.

The addition of Future Grid and OTS is significant because the power industry’s AI challenge is increasingly moving beyond centralized analytics. Utilities need visibility into low-voltage networks, distributed generation and changing loads quickly enough to act before local problems become system-wide operational issues.

Landis+Gyr’s strategy is to turn its grid-sensing infrastructure into a platform on which third-party applications can run.

The company describes its Edge App Ecosystem as an open, scalable environment where developers can build applications for advanced analytics, operational optimization and customer engagement. Applications can be deployed across utility networks using Landis+Gyr’s Revelo grid-sensing platform, which combines high-resolution electrical measurements with edge computing capabilities.

In practical terms, the model resembles an app ecosystem for utility infrastructure: Landis+Gyr supplies the underlying sensing and computing environment while specialist partners develop applications for specific operational problems.

That approach could become increasingly important as utilities move from traditional one-way electricity networks toward systems with thousands or millions of distributed assets.

Why grid intelligence is moving to the edge

Traditional utility architectures were built around centralized generation and relatively predictable flows of electricity. Distributed energy resources complicate that model.

Rooftop solar can push electricity back into local networks. Batteries can change demand patterns. Electric vehicles create new and potentially flexible loads. Heat pumps and other electrified technologies can add significant demand at the distribution level.

The result is a grid that needs more granular visibility.

The International Energy Agency estimates that annual global grid investment will need to more than double from roughly $330 billion to $750 billion by 2030 to keep pace with electricity-system requirements, with around 75% of that investment directed toward distribution grids. The IEA also says digital technologies can help improve grid maintenance and extend asset lifetimes.

That creates a case for edge computing.

Instead of sending every piece of sensor data to a centralized cloud environment before making an operational decision, edge systems can process information locally and send relevant insights upstream. For utilities, this can reduce latency and potentially limit the amount of raw data that has to move through the network.

Landis+Gyr’s Revelo platform is designed around that concept. The company describes Revelo as a grid sensor capable of sampling, processing, storing and delivering high-resolution electrical data in real time.

Future Grid and OTS add specialist intelligence

Future Grid brings a software layer focused on low-voltage distribution networks. According to Landis+Gyr, its platform uses grid intelligence and AI-optimized analytics to turn large volumes of network data into operational risk and action information for utilities.

That could cover functions ranging from compliance and field operations to grid planning and control-room activity.

OTS brings a different but complementary capability, with expertise in data analytics, edge sensors and real-time systems. Its role is centered on helping utilities turn large-scale operational data into automated insights.

The two additions fit Landis+Gyr’s broader effort to build a partner ecosystem rather than develop every grid application internally.

The company already lists partners including Sense and Mitsubishi Electric. Mitsubishi Electric joined the application ecosystem in January 2026 under a collaboration focused on grid-edge intelligence and energy-transition use cases.

That ecosystem approach could matter as utilities confront a widening range of specialized problems. A distribution utility may need applications for transformer loading, voltage management, power-quality monitoring, outage detection, DER coordination and predictive maintenance. Building each application internally would be expensive and slow.

An open platform potentially lets utilities assemble capabilities from multiple specialist vendors.

AI is becoming an operational utility technology

The timing also reflects a broader change in how the energy industry views AI.

Gartner predicts that 40% of power and utility organizations could deploy AI-driven operators in control rooms by 2027. Its research also found that 94% of power and utility CIOs planned to increase AI investment in 2025, with an average planned spending increase of 38.3%.

But AI in a control room is materially different from an AI chatbot.

Grid operations are cyber-physical systems. An incorrect recommendation can have consequences for equipment, reliability, safety and customers. Gartner has consequently warned utilities to address governance, data integrity and risk mitigation as they adopt AI.

That makes the location of AI processing important.

An edge application operating on or close to grid infrastructure can potentially combine real-time sensor information with local analytics and operational rules. The objective is not necessarily to replace centralized systems, but to give them better and faster information.

Landis+Gyr’s own platform documentation describes edge applications for areas including grid-location awareness, transformer loading, voltage and power-quality monitoring, while its cloud applications extend into predictive maintenance, load forecasting and outage prediction.

That points toward a hybrid architecture in which intelligence is distributed across meters, edge devices, communications networks and cloud platforms.

The enterprise challenge is integration, not just AI

For utility CIOs and grid operators, the appeal of an application ecosystem will ultimately depend on integration.

Utilities have long-lived operational technology, legacy systems and highly regulated environments. New AI applications have to coexist with existing advanced metering infrastructure, distribution-management systems, outage platforms and enterprise IT.

An open ecosystem can reduce some of the development burden, but it also introduces questions around cybersecurity, application certification, data ownership, interoperability and lifecycle management.

Those questions become more important as applications gain the ability to influence operational decisions.

Landis+Gyr’s pitch is therefore less about AI as a standalone product and more about creating an infrastructure layer on which multiple forms of grid intelligence can operate.

That is potentially a more durable enterprise strategy.

Australia’s grid makes the edge problem particularly visible

Australia is a useful market for this model because of its high penetration of distributed energy technologies and the resulting pressure on distribution networks.

As more electricity generation and flexible demand moves to the customer side of the meter, utilities need better visibility into what is happening at the low-voltage edge.

The result is a shift in the definition of the smart grid. The next generation is not simply a network with connected meters. It is a distributed computing environment in which sensors, edge applications, cloud analytics and utility control systems continuously exchange information.

Landis+Gyr’s expanding partner ecosystem is a bet on that architecture.

The larger question is whether utilities will embrace open application platforms as a way to modernize the grid without replacing their existing technology stacks. If they do, edge AI could become less of a specialized innovation and more of a standard layer in utility infrastructure.

Market Landscape

The intelligent-grid market is converging around several technology layers:

• Grid-edge sensing: Smart meters and sensors capture voltage, current, power quality and other operational signals.
• Edge computing: Local processing turns raw grid data into faster operational insights.
• AI and machine learning: Analytics can identify anomalies, forecast demand, detect equipment risks and optimize operations.
• DER management: Utilities need software to manage solar, batteries, EVs and other distributed resources.
• Cloud analytics: Centralized systems remain important for forecasting, planning, fleet-level analysis and long-term optimization.
• Open application ecosystems: Utilities increasingly have the option of deploying specialist applications without replacing the underlying grid platform.

Landis+Gyr is competing in an ecosystem that includes utility technology providers, cloud companies, AI infrastructure vendors and specialist grid software companies. The strategic differentiator is increasingly the ability to combine real-time data, edge processing, AI analytics and operational integration.

Top Insights

• Landis+Gyr added Future Grid and OTS to its Edge App Ecosystem, expanding AI and analytics capabilities for increasingly complex Australian distribution networks.
• The ecosystem model lets utilities deploy specialist applications on shared grid-edge infrastructure instead of building every analytics capability internally.
• Revelo combines high-resolution grid sensing with edge computing, creating a foundation for real-time applications across distributed energy and network operations.
• Gartner expects AI-driven operators to reach 40% of power and utility control rooms by 2027, increasing demand for governed operational AI.
• Australia’s distributed-energy transition makes low-voltage visibility, edge intelligence and real-time decision-making increasingly important for utility technology teams.

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