| | 9 DECEMBER 2025hardening of the grid infrastructure, regulatory compliance, enterprise modernization and cross company strategic planning. Resilience planning is not a one-time effort, but it must evolve with the changing threats and technologies. Use scenario-based planning to incorporate climate modeling and risk assessments and engage in townhalls or conversations with the community and legislators. Ensure alignment with regulating entities and justify investments through cost-benefit analysis. Continuously learn from past disruptions and refine company strategies accordingly. Foster open and collaborative communications between IT, OT, operations and executive leadership to align goals and resources. Take opportunities to explore and deploy advanced technologies to enhance situational awareness and response capabilities. For example, expanding the IoT technologies and automation through leveraging sensors, drones and smart devices, will promote monitoring of the grid health and automate responses. Broaden the network bandwidth or use private LTE networks to enable faster and more secure communications across distributed assets. AI is not a panacea. It requires investment in people skills and technology resources. Preparing the enterprise for the impacts of AI will help drive success.Each merger, acquisition or divestiture can be different in the approach and ultimate outcomes. From a technology perspective, identify which goals are most important to focus on first. It could be to blend IT governance, operations and technology or creating a unified digitization vision between companies through mapping technology capabilities to operational priorities. No matter the goal, change management is key to preparing companies for the changes to the work culture. A transparent communication plan can help explain the "why" behind changes to the organizational structure and technology shifts. To avoid major disruptions, use a phased integration roadmap that prioritizes the systems to "connect" as part of day one activities, those that will combine capabilities and phase out legacy systems. Use interim solutions as bridge alternatives to maintain operations while further integration is being planned. Offer training and support to employees for technology adoption and encourage local champions. Remember how important it is to keep the lines of communication open, monitor the progress and celebrate the quick wins.Leading the AI transition With Responsible AIWhen I talk with other CIOs/CTOs, we agree that one of the most misunderstood technology trends by executives today is AI-driven automation, particularly generative AI and its role in enterprise transformation. Some may view it as a cost-cutting tool where tasks can be automated and operational efficiencies can be gained in the workforce. Although these are valid use cases, generative AI has more strategic implications. It is meant to co-exist with human decision-making and promote cross-functional collaboration. Culturally, the company needs to be ready to embrace this technology and ensure ethical oversight. It requires deep integration with data governance, security and business workflows, transforming the workforce to collaborate with AI to enable new products, services and potential revenue streams. For senior technology leaders navigating the energy transition, embedding AI and automation while maintaining compliance and public trust requires a purposeful, values-driven approach. Remember, AI is not a panacea. It requires investment in people skills, data fitness and technology resources. Preparing the enterprise for the impacts of AI will help drive success. Look for opportunities to leverage AI to improve grid resilience and modernization and tie back to environmental sustainability goals such as decarbonization. Spend time on building your company's AI roadmap with a solid AI governance framework that complies with evolving regulations and training the workforce in how to improve AI outputs. Solidify the company's approach on how to move forward from building foundational enablers, educating the workforce on components for data fitness, ensuring AI is secure and being responsibly used for innovation and process optimization. Monitor for success.
< Page 8 | Page 10 >