Seventy-seven percent of organizations increased their spending on upskilling workers to use AI over the past twelve months, and 35% describe that increase as significant.Asked what matters most to incorporating more AI into the energy sector, 29% of leaders name upskilling energy-sector professionals, almost exactly the same number who said so a year ago8. This is a constraint the sector identified early, has held to, and is now putting real money against. Upskilling investment is rising in every market surveyed, ranging from 88% in India to 62% in Brazil. However, the work is not finished. Lack of skilled talent remains the third-largest barrier to adopting AI in the sector, named by 32% of leaders, behind only cybersecurity and data quality.Both of those figures are worth holding together: an industry that is spending more on skills and still ranking them third suggests something about the size of the task, and the gap that still needs to close. If the industry's challenge is turning AI skills into AI capability, ADNOC shows what that looks like in practice. The most significant measure of ADNOC's AI progress is perhaps not how many people have been trained – although that is considerable – but how many are putting AI to work. Across operations, engineering, commercial and corporate functions, employees are using AI to solve problems, streamline processes and make better decisions. Thousands of AI agents have been built and deployed by employees themselves, each designed around a real business need. Many of these solutions come not from dedicated technology teams, but from the people who understand the challenge best: front-line engineers, operators, analysts and business professionals. ADNOC is demonstrating AI may automate more work, but it will not automate accountability. As intelligent systems become more capable, the role of people increasingly shifts towards judgment, oversight and decision-making. The priority for leaders is developing the skills needed to work effectively alongside increasingly autonomous systems. what happens when AI moves beyond experimentation and becomes a capability embedded across the workforce, turning everyday expertise into scalable digital solutions. The broader industry appears to be moving in the same direction. Fifty-two percent of leaders now spend six hours or more a week learning about AI or digital topics, up from 29% a year ago, while the share committing eleven hours or more has tripled to 25%9. One in four leaders now spend more than 11 hours a week building their own understanding of the technology. Capability, it seems, is increasingly being treated as a leadership responsibility rather than a training program. That helps explain another finding. When leaders look for external support, skills programs rank surprisingly low on the list. Presented with six possible actions to support sustainable AI and energy growth, they put grid investment and AI regulation first, followed by clean-energy incentives, system integration and cross- border coordination frameworks. Digital skills programs come last, attracting just 6% of responses, down from 8% a year ago. The implication is not that skills matter less, rather, leaders increasingly see capability as something that must be built inside their organizations, while governments focus on the infrastructure, regulation and coordination needed to support AI at scale. Looking across the findings for Powering Possible 2026, agentic workflows have reached the functions whose data was ready. Data readiness is turning into a question of governance and permission. Intelligence has begun to act on the physical asset. Every one of those findings resolves to people: a person or a team capable of making the decision, and accountable for having made it. The systems described across these pages do not run themselves, and the organizations getting the most from them are the ones whose people are learning, including their leaders. 6 PROGRESS DEPENDS ON PEOPLE 8, 9 Powering Possible 2025: Unleashing AI for Energy and Energy for AI “The energy sector  understands the technical challenges of adopting and scaling AI systems. The next stage is about how the pieces fit together: coordination, trust, readiness and deployment. Organizations will want confidence in the data and models they train, clear lines of accountability and the ability to collaborate across boundaries. AI systems and agents should support teams, with people keeping authority over operational decisions. Energy has always been an innovative industry. As AI becomes more embedded in physical systems, how well people, machines and organizations work together will measure its value” Prof. Olivier Oullier, MBZUAI CEO, Inclusive Brains 26