Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I have over 10 years of experience in the industry, with a current focus as a domain consultant specializing in digital twin, AI, generative AI, and agentic AI solutions for utility and smart grid applications.

Q2. How mature are AI and machine learning technologies for predictive maintenance in smart grids today, and what are the biggest technological gaps remaining?

Digital twin and AI technologies hold significant promise for the future of smart grid applications. However, one of the main challenges today is achieving interoperability between current AI systems and existing digital infrastructure. Bridging this gap is essential to fully realize the benefits of predictive maintenance in smart grids.

Q3. How scalable and adaptable are these AI solutions for diverse grid infrastructures, including microgrids and renewable-heavy grids?

AI solutions are highly scalable for smart grids and microgrids, where digital integration is more advanced. However, conventional grid infrastructures often face challenges related to data quality and integration, which can limit the effectiveness of AI-driven solutions.

Q4. Can you describe emerging trends or breakthroughs in AI/ML that could disrupt or accelerate smart grid optimization in the next 3-5 years?

In the next three to five years, I expect to see advancements in predictive analytics, increased operational efficiency and reliability, and the emergence of more autonomous control and operation within smart grids.

Q5. How do you foresee the competitive landscape evolving in this domain, particularly among technology providers, utilities, and integrators?

Currently, the market is not highly competitive due to the early stage of digital adoption and the prevalence of estimation-based actions. However, I anticipate that in the coming years, as the industry moves toward fully digital and autonomous control.

Q6. What are the emerging use cases of generative AI in designing and simulating smart grid scenarios for better planning and forecasting?

One emerging use case for generative AI is the application of retrieval-augmented generation (RAG) or fine-tuning domain-specific knowledge to support smart grid planning scenarios. This approach enables more proactive maintenance, improved forecasting, and enhanced planning activities.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

I would ask how the organization plans to leverage emerging technologies within their products, and what measures are in place to ensure robust guardrails and privacy controls throughout all implementations.