A Center of Excellence Approach with GE Vernova’s Asset Performance Management Software

Author Sticky

Ryan Finger

Director, Global APM Product Marketing

GE Vernova’s Software Business

Ryan is a member of GE Vernova’s global product marketing organization that focuses on pragmatic principles to get powerful software into the hands of our customers. He has a master’s degree in high tech product and digital transformation, paired with experience in Software-as-a-Service marketing to some of the world’s largest financial institutions.

He is now focused on simplifying how the world sees Asset Performance Management software as a driver of operational excellence and accelerator of the energy transition.

Jun 17, 2025 Last Updated
11 minutes

As energy enterprises embrace digital transformation, Centers of Excellence (CoEs) and centralized IT teams can lead standardizing asset management, IT infrastructure, and operational efficiencies. Modern Asset Performance Management (APM) platforms provide a foundation for enterprise-wide visibility, automation, and predictive analytics, integrating:
  • Open microservice architecture and APIs for connectivity
  • AI/ML for predictive and prescriptive maintenance and anomaly detection
  • Generative AI for structured and unstructured asset data ingestion
  • Computer vision for advanced asset inspections
  • "Bring Your Own Analytics" (BYOA) capabilities for tailored insights
Energy companies may utilize CoEs to centralize expertise in technologies like AI, data analytics, and customer experience. They aim to rapidly develop and deploy solutions across their operations, improve efficiency, manage costs, and stay ahead in the evolving energy landscape. This often includes dedicated teams focusing on specific areas like data integration and cyber security with the goal to share best practices across the organization.
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What are examples of centers of excellence?

A few examples of energy organizations working to develop CoEs for the enterprise are:
  • Data Analysis and Insights: CoEs dedicated to data analysis can leverage large datasets from smart meters and grid operations to predict demand, optimize energy generation, and identify potential issues proactively.
  • AI and Machine Learning Applications: Developing AI-powered solutions for tasks like predictive maintenance, anomaly detection in power plants, and customer behavior analysis, often within a dedicated AI CoE.
  • Cybersecurity: Establishing a cybersecurity CoE to monitor and address potential threats to critical energy infrastructure, ensuring robust cyber defenses across the entire system.

What are the benefits of using CoEs in the energy sector?

The benefits include:
  • Expertise Consolidation: Centralized knowledge and skills within a CoE allow for faster problem-solving and innovation across the organization.
  • Standardized Practices: CoEs can establish best practices and guidelines for technology implementation, working toward consistency across different departments.
  • Faster Time to Market: By streamlining development processes, CoEs can accelerate the deployment of new technologies.
  • Cost Consciousness: Sharing resources and expertise within a CoE can lead to improved efficiency and cost reduction.
By leveraging Asset Performance Management software within a CoE framework, enterprises can reduce maintenance costs, enhance operational efficiency, and improve asset reliability at scale.

The Role of Asset Performance Management software in a Digital Center of Excellence

Digital CoEs act as a centralized hub for enterprise-wide innovation, helping organizations in how they:
  1. Manage asset reliability and performance across multiple sites.
  2. Improve IT infrastructure resilience with integrated monitoring.
  3. Automate data ingestion and insights to reduce operational inefficiencies.
  4. Standardize cross-functional collaboration between IT, operations, and field teams.
Asset Performance Management solutions can help Digital CoEs unify asset data, streamline workflows, and implement AI-driven decision-making across the enterprise.

Enabling IT & OT Integration with Microservices and APIs

Traditional asset management systems often operate in silos, limiting visibility and responsiveness. APM platforms through using microservices and APIs solve this by:
  • Facilitating data exchange between ERP, SCADA, CMMS, ITSM, and cybersecurity systems.
  • Supporting modular deployment, enabling IT teams to build and scale custom applications.
  • Automating workflows, such as maintenance ticketing and incident resolution.
Example: An energy company integrates APM alerts into its IT Service Management (ITSM) system, automating maintenance request creation based on real-time asset health data.

AI/ML-Driven Predictive and Prescriptive Maintenance in Asset Performance Management

AI/ML models enhance Asset Performance Management platforms by:
  • Predicting failures before they occur, reducing unplanned downtime
  • Optimizing maintenance schedules, balancing cost, risk, and performance
  • Detecting anomalies in operational data, enabling rapid intervention

Generative AI for Asset Intelligence & Unstructured Data Processing

One of the biggest challenges in asset management is the vast amount of unstructured data, such as technician maintenance logs, engineering reports, equipment manuals, and more.

Generative AI transforms this unstructured data into actionable insights by:
  • Extracting relevant information from documents to assist engineers in troubleshooting
  • Providing contextual recommendations for maintenance strategies
  • Summarizing key insights from historical records and past asset failures

Computer Vision for Asset Inspections & Condition Monitoring

Computer vision enhances asset performance monitoring by automating inspections through:
  • Drone-based assessments of infrastructure, reducing inspection time and costs
  • AI-powered defect detection for corrosion, leaks, and equipment wear
  • Remote monitoring capabilities, minimizing safety risks for field workers
  • Operator routes at scale across all site

Bring Your Own Analytics (BYOA): Custom AI & Data Science for Asset Performance Management

Enterprises with in-house data science teams or third-party analytics tools need flexibility in applying custom models to asset data. APM platforms support BYOA by:
  • Allowing data scientists to apply custom AI models within the platform
  • Providing API-based integrations with external analytics tools (Power BI, Tableau, Python, etc.)
  • Enabling composable dashboards and KPIs tailored to business needs

Why Use GE Vernova’s Asset Performance Management for Increased CoE Efficiency

GE Vernova’s APM suite is designed to help energy enterprises increase asset reliability, reduce maintenance costs, and improve operational efficiency at scale. With a cloud-ready, microservice architecture, APIs / Connectors, and AI/ML for multiple use cases, APM may help centralized IT teams and Digital Centers of Excellence (CoEs) unify data across systems, streamline workflows, and predict asset failures before they occur.

As an emerging focus for GE Vernova’s APM, Generative AI is being investigated for the proper use cases to synthesize both structured and unstructured data—such as sensor readings, maintenance logs, and engineering documents—turning it into actionable insights.

Integrated computer vision models further enhance inspections, while BYOA capabilities allow organizations to apply custom models into the microservice-based architecture. By providing enterprise-wide visibility, flexibility, and scalability, GE Vernova’s APM helps energy companies reduce downtime, increase asset performance, and support long-term operational resilience.

Business Impact of Asset Performance Management-Enabled Digital CoEs

  • Cost Savings: Predictive maintenance and automation reduce unnecessary repairs and operational downtime
  • Operational Efficiency: AI-powered insights and generative AI accelerate troubleshooting and issue resolution
  • Improved Safety & Compliance: Remote monitoring and automated inspections reduce human exposure to high-risk environments
  • Scalability & Innovation: Open APIs, microservices, and BYOA capabilities can help flexibility as enterprise needs evolve
  • Improved Resource Allocation: Using APM software in a CoE can allow organizations to better manage the movement of employees that are performing critical functions.

The Future of Enterprise Asset Management with APM

As energy enterprises shift toward digitally driven, AI-enabled asset management, GE Vernova’s APM platforms provide the foundation for scalable, efficient, and cost-effective operations. Digital Centers of Excellence and enterprise IT teams are uniquely positioned to drive this transformation by adopting GE Vernova’s APM as a centralized intelligence hub, enterprises can feel more confident in long-term asset performance, cost savings, and operational resilience.

Author Section

Author

Ryan Finger

Director, Global APM Product Marketing
GE Vernova’s Software Business

Ryan is a member of GE Vernova’s global product marketing organization that focuses on pragmatic principles to get powerful software into the hands of our customers. He has a master’s degree in high tech product and digital transformation, paired with experience in Software-as-a-Service marketing to some of the world’s largest financial institutions.

He is now focused on simplifying how the world sees Asset Performance Management software as a driver of operational excellence and accelerator of the energy transition.