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Technology Vision 2025: Harnessing AI’s transformative power in the energy sector

5-MINUTE READ

February 18, 2025

The Accenture Technology Vision 2025 explores a future where artificial intelligence (AI) is not merely a tool but a foundational force reshaping industries.

AI is rapidly advancing from automation to autonomy, acting independently on behalf of people and systems. This shift unlocks new possibilities for business reinvention but also redefines the relationship between autonomy and trust.

For the energy sector, AI presents an unprecedented opportunity to enhance operational efficiency, drive sustainability and optimize resource management. However, these opportunities will be lost unless business leaders secure enough trust from employees and consumers to fully engage with AI’s evolving capabilities.

With 81% of energy executives acknowledging trust as a critical factor in AI adoption, leaders need to act now to prepare for a future where AI is everywhere—operating autonomously and reshaping how businesses and people interact.

In this blog we dive deeper into each of the trends and explore the implications for the energy industry.

#1 The binary big bang – AI expands exponentially, reshaping systems

The Binary Big Bang marks a paradigm shift from traditional app-based digital ecosystems to AI-powered agentic systems. AI agents capable of making decisions and automating workflows are revolutionizing how enterprises operate. Within the energy industry, these systems are driving innovations such as real-time reservoir management, predictive maintenance and dynamic supply chain optimization.

AI’s evolution is being driven by foundation models that expand the limits of software and programming. These decisions made today will define capabilities for the next decade.

According to our research 25% of energy executives anticipate a significant increase in AI agent utilization within three years, with 85% agreeing that AI agents will reinvent digital system construction. This shift enables improved scalability (55%) increased flexibility (47%) and enhanced innovation (42%) making AI a strategic advantage for the industry.

For example, Repsol a leader in the multi-energy sector recently announced its plans to extend its co-innovation agreement with Accenture to advance its Digital Program and accelerate the adoption of generative AI (gen AI) across the company¹.

Enhanced process efficiency through AI agents and NVIDIA technologies will streamline and optimize internal processes such as planning, forecasting, application maintenance and incident resolution.

AI agents will improve predictive maintenance by accurately identifying potential equipment failures and optimizing maintenance schedules, enhancing operational efficiency and reliability. Repsol is also integrating AI agents and NVIDIA technologies for digital twins and robotic solutions, which will further optimize operations and reduce costs in industrial and logistics centers. To support this, Repsol is expanding its training plan to facilitate the learning and adoption of these new technologies, with Accenture providing support for these initiatives.

Another example is ADNOC who are pioneering the use of highly autonomous agentic AI through a partnership with G42, Microsoft and AIQ². This first-of-its-kind solution, ENERGYai, will integrate advanced AI agents across ADNOC’s entire value chain. These intelligent systems will perceive, learn and act autonomously to drive value and enhance sustainability.

Chevron meanwhile utilizes AI-driven multi-agent systems and optimization algorithms to enhance supply chain efficiency³. These systems coordinate transportation logistics for oil, gas and equipment, reducing costs and delays. Autonomous AI agents make real-time decisions, managing fleet schedules, pipeline monitoring and overall logistics operations.

These AI-driven transformations highlight the importance of data unification and contextualization. However, many energy companies still operate within fragmented systems, limiting the effectiveness of AI. As AI agents continue to bridge these silos, companies will move towards more holistic, integrated operations, enhancing decision-making and enterprise-wide connectivity.

Looking forward, sustainability (83%) will continue to be a major focus, with AI-driven solutions optimizing carbon capture, emissions monitoring and energy efficiency initiatives to meet regulatory and environmental goals. Followed by IT (71%) engineering and manufacturing (60%) finance (53%) and supply chain management (52%) identified as the primary AI agent users within the next three to five years.

In the near term, AI capabilities will focus on task-specific automation and function modernization, eventually expanding into workflow automation and third-party system integrations for more comprehensive operational overhauls.

Key Takeaways:

AI agents enable real-time optimization of drilling, exploration, real time blend optimization (RTB0) and supply chains, significantly improving operational efficiency.
Data unification and integration across systems are crucial for maximizing AI’s potential and driving intelligent decision-making value chain optimization.
Companies that adopt AI-driven agentic systems will gain a competitive edge in adaptability, sustainability and enterprise-wide transformation.

#2 Your face, in the future – differentiation in an AI driven world

As enterprises integrate AI into customer interactions, maintaining a unique brand personality becomes crucial. The risk of AI-driven engagement becoming generic is high, given that many AI systems are built on neutral, standardized models.

According to our research 90% of energy executives recognize the importance of maintaining a consistent AI personality in customer-facing applications, while 81% agree that chatbots lacking differentiation pose significant challenges.

Further research shows that 79% of executives believe conversational AI will gather relevant customer context beyond basic demographics and purchase history.

In line with this, Aydem Energy has successfully addressed this challenge by using Azure OpenAI Service to develop an AI-driven WhatsApp assistant⁴. This assistant, capable of handling over 1,000 customer interactions daily, has transformed customer service by providing real-time updates on billing inquiries, meter readings and claims. The initiative has significantly enhanced customer satisfaction while reducing operational costs.

Trust remains a critical factor though, with 75% of executives emphasizing the need to build trust between AI personas and customers. This reinforces the importance of AI systems that not only provide efficient responses but also reflect the company’s values, tone and customer engagement strategy.

Key Takeaways:

Maintaining a distinct AI personality ensures differentiation in an increasingly AI-driven customer experience.
AI-powered personalization enhances customer relationships by understanding behavior beyond simple demographic data. For example, priority customers like the military require specialized service approaches. This dynamic personalization tailors experiences to each scenario, optimizing engagement and efficiency.
Trust-building in AI interactions is essential to maintaining long-term engagement and brand loyalty.

#3 When LLMs get their bodies – AI & robotics revolutionizing field operations

AI-powered robotics is transforming physical operations, evolving from narrow, task-specific automation to versatile systems capable of operating in dynamic environments. 64% of executives see broad task completion as a key advantage, reinforcing the role of generalist robots in autonomous field operations.

As these systems take on more complex roles, trust and collaboration between humans and AI-driven robotics become essential. 84% of executives believe natural language communication enhances trust and efficiency, while 83% of energy executives stress the importance of responsible AI principles in robotics deployment.

Some big players in the energy industry have already started executing on this trend.

In 2024 SLB and Equinor drilled the most autonomous well section to date⁵. SLB digital technologies for surface automation, autonomous on-bottom drilling, and directional drilling were combined to enable 99% of a 2.6-kilometer section to be drilled in autonomous control mode. Later in 2024, SLB also added AI-driven geosteering to its autonomous drilling solutions to achieve more efficient and productive wells.

AI-powered drones and robots are also revolutionizing pipeline monitoring. TotalEnergies leverages AI vision systems and IoT sensors to monitor greenhouse gas emissions and detect leaks in pipelines and facilities⁶. Drones equipped with AI provide real-time surveillance, while autonomous agents issue alerts and take corrective actions when anomalies, such as methane leaks are detected, enhancing environmental sustainability.

Looking ahead, physical AI will play a key role in renewable energy operations. AI-driven autonomous systems will manage wind and solar farms, optimizing energy generation and reducing maintenance downtime. The next stage of robotics involves fully autonomous decision-making, where AI-powered robots operate independently without human oversight, creating safer and more efficient field operations.

Key Takeaways:

AI-driven robotics enhances efficiency in field operations, minimizing human risk in hazardous environments.
Natural language communication improves trust and collaboration between humans and AI-powered systems. Clear decision traceability is key to building trust, especially among field workers who are often sceptical of AI responses.
Responsible AI principles must be embedded in robotics deployments to ensure safety and ethical compliance.

#4 The new learning loop – AI & human collaboration in workforce transformation

AI is not only automating processes but also redefining how organizations approach learning and workforce development. AI is fostering a cycle of continuous learning, allowing employees to enhance their skills while AI systems adapt and refine themselves based on human input. This human-AI collaboration is particularly crucial in an industry where workforce knowledge retention and upskilling are vital.

Workforce reskilling is now a top priority, with 70% of energy executives highlighting the need to upskill or reskill employees in generative AI within the next three years. As AI transforms the workplace, organizations are making generative AI tools more accessible, with 38% expecting these tools to be significantly or fully integrated into workflow automation within the same timeframe.

We can already see how some companies are using generative AI to improve learning and development for their workforce and how adaptive learning paths powered by verified skills data can significantly reduce learning time.

Siemens Energy with 100,000 people worldwide is one such example⁷. Due to its large and diverse workforce, the skills of employees and subsequent learning requirements varied across the board. Working with Workera AI, Siemens Energy adopted personalized learning paths and gained a detailed understanding of the skill sets each employee needed for their role — while also identifying the skills they didn’t need. As a result employees received uniquely tailored learning paths based on their verified skills and experience.

With 98% of executives foreseeing a major shift in employee tasks toward innovation, generative AI is expected to free workers from repetitive tasks and allow them to focus on higher-value strategic initiatives.

AI is also playing a pivotal role in research and development within the energy sector. The fusion of human expertise and artificial intelligence (AI) is revolutionizing research and development, enabling innovation at unprecedented speed. AI-driven systems process vast amounts of geological, operational, and materials science data, identifying patterns and opportunities that would take human researchers years to uncover.

However, rather than replacing human ingenuity, AI enhances it—allowing scientists and engineers to focus on high-value problem-solving while AI handles complex computations and real-time analysis. This collaborative intelligence creates a continuous learning loop, where AI refines its models based on human feedback, and researchers leverage AI-generated insights to accelerate discovery and decision-making.

Key Takeaways:

With 70% of energy executives prioritizing reskilling in generative AI and 38% planning full workflow integration within three years, AI adoption is becoming essential for workforce efficiency and innovation.
AI is not just automating tasks but reshaping how organizations approach learning and workforce development. A continuous learning loop between AI and employees enhances skills, improves retention and accelerates upskilling efforts.
AI is revolutionizing research and development by handling complex data analysis, freeing scientists and engineers to focus on strategic problem-solving. Rather than replacing human expertise, AI amplifies it, creating a dynamic cycle of learning and discovery.

AI is no longer just a tool—it is a transformative force reshaping the energy sector

From optimizing operations and enhancing safety to driving sustainability and workforce evolution, AI-driven innovations are unlocking new efficiencies and redefining industry standards.

However, success in this new era depends on strategic adoption, trust-building and seamless integration across systems. Energy leaders who embrace AI responsibly and proactively will gain a competitive edge, ensuring resilience and long-term growth in an increasingly complex and dynamic landscape.

The future of energy is AI-powered—those who lead this transformation will shape the industry for decades to come.