The Quintessential Grid Modernization Team for Streamlined AI Adoption
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Dr. Avnaesh Jayantilal
Grid Software, GE Vernova
May 01, 2025
3 minutes
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The journey to AI/ML for utilities is essential, but incredibly complex. That’s why it’s important to do as much prework as possible to maximize the chances of success.
One of the most crucial prerequisites to a successful AI/ML journey is forming a comprehensive grid modernization team. This team is responsible for shaping their organization’s long-term grid modernization strategy and vision over the next 5-10 years, preparing for regulatory changes, and adapting to evolving grid requirements. It is focused on ensuring flexibility for future needs, avoiding time-consuming pilot programs, and accelerating the time-to-value for new AI/ML-powered applications.
Here are the most important components of any grid modernization team:
One of the most crucial prerequisites to a successful AI/ML journey is forming a comprehensive grid modernization team. This team is responsible for shaping their organization’s long-term grid modernization strategy and vision over the next 5-10 years, preparing for regulatory changes, and adapting to evolving grid requirements. It is focused on ensuring flexibility for future needs, avoiding time-consuming pilot programs, and accelerating the time-to-value for new AI/ML-powered applications.
Here are the most important components of any grid modernization team:
Strong leader.
A strong team leader is essential for ensuring the AI journey both remains on track and also progresses consistently. The leader should have extensive experience in the utility industry, and understand the nuances, politics, and dynamics of the various departments that will benefit from AI/ML adoption. They should also be exceptionally skilled at meeting facilitation, project management, organization, conflict resolution, and communicating both up and down.
AI product manager.
A grid modernization team should also have an AI product manager who will both lend a product-management perspective to the journey, while also overseeing their organization’s new AI capabilities once adopted. The product management skillset is ideal for ensuring cross-functional alignment when selecting the right use cases, delivering successful app productization through MLOps and the data foundation, working with engineering to choose the right AI models, and driving acceptance and adoption. The nature of product management means that this member can help the team make key decisions that meet business and regulatory objectives.
Data scientist/analyst.
As discussed in GE Vernova’s first whitepaper in a series of four on AI for utilities, forming a grid modernization team is the third step in an AI adoption journey. The first two steps that must happen before forming the team are (1) building out a data foundation, and (2) ensuring data quality and accuracy. The same data expert(s) involved in those first two steps should also be brought onto the grid modernization team. Their knowledge of the data powering the new AI/ML applications will be invaluable in continuously verifying data integrity.
Data and ML engineer.
If available, bringing data and ML engineers onto the grid modernization team is also beneficial. Their skillsets can help ensure dedicated resourcing support and a focused team approach to bringing any AI technologies to life. In fact, data engineers can and should be involved even earlier in the adoption journey when building a data foundation – which is crucial for operationalizing AI capabilities.
Project manager.
Any project with the scale, scope, and implications of an AI/ML adoption journey calls for the expertise of a project manager. AI journeys are incremental, characterized by milestones that may incur delays or setbacks. It’s the project manager’s job to keep the team focused, informed, and on track to meet those milestones while satisfying deliverables and fulfilling objectives.
Control room operator.
Many grid AI applications are designed to be used in the control room, especially those that make predictions and automate operations. Thus, it’s key to ensure a grid modernization team includes at least one control room operator, to lend their perspectives on how certain AI technologies will (or will not) benefit the control room. As the AI journey reaches the deployment stage, the control room representative will also be invaluable in change management and user training efforts.
Network planner.
Network planners benefit greatly from AI adoption, especially through advanced, AI-powered simulations and scenario analysis use cases. In addition, the very nature of their work requires constantly pondering and planning for the future – just like the grid modernization team itself. A network planner is a valuable member of any grid modernization team, as they can lend perspectives on how the team’s decisions will impact the physical network of today and tomorrow.
Power systems engineer.
Electrical networks are incredibly complicated affairs, requiring extensive knowledge of physics, mathematics, technology, and economics to understand the where, why, and how of supplying power. A power system engineer is an invaluable resource for the modernization team to help fellow members understand how AI/ML will affect the grid from a systems engineering perspective.
Applications engineer.
Applications engineers have two critical responsibilities that are exceptionally relevant to carrying out the mission of a grid modernization team:
- Designing and developing applications that improve grid operations, such as monitoring systems, control systems, and data analytics platforms.
- Integrating said applications into existing grid workflows, processes, and infrastructure.
This knowledge of application design and delivery makes an application engineer a highly useful resource when adopting AI/Ml.
Get more guidance and learn new best practices for a utility AI adoption journey in GE Vernova Grid Software’s new whitepaper, Empower Intelligent Grids with AI.
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