How Gen AI Is Transforming Energy, Mining, and Heavy Industry
What exactly is Generative AI?
Generative Artificial Intelligence, often referred to as Gen AI, is a category of machine learning models capable of creating original content. This includes text, images, audio, computer code, and even three-dimensional models. While tools like ChatGPT and DALL·E have made Gen AI popular in creative and administrative fields, its impact is now extending to heavy industry. However, when it comes to traditional industries like energy, mining, chemicals, and manufacturing, the story is more complex. There’s enthusiasm about Gen AI’s potential to drive efficiency and support decision-making, but the reality is that widespread, proven operational improvements remain limited. Many companies are still exploring where and how these tools can truly add value. For now, the dominant use cases are more experimental, focused on things like document summarization, synthetic data generation, or training simulations, rather than delivering major leaps in productivity on the shop floor or in the field. In other words, while the promise is loud, the proof is still catching up.
The Value Proposition: Generating Billions in Productivity
Global private investment in Gen AI reached $33.9 billion in 2024, a rise of nearly 19% over the previous year. The total global AI market is expected to hit around $638 billion by the end of 2025, projected to expand further to $3.68 trillion by 2034. The generative AI vertical alone is valued at approximately $37.9 billion in 2025, with a projected CAGR of 44% leading to a market size of $1 trillion by 2034. Adoption among businesses is accelerating: 78% of organizations reported using AI in 2024, and ROI is tangible. Some sectors are seeing a return of $3.70 for each $1 spent on Gen AI. In manufacturing, predictive maintenance and AI-led automation are estimated to address equipment failures costing up to $1.4 trillion annually, unlocking substantial cost savings. Leading analysts suggest that transforming factories into AI-robotics integrated “smart” or “dark” facilities could generate hundreds of billions in additional value globally over the next five years.
What Is Driving This Shift
Three key factors are accelerating the adoption of generative AI in industry today. First, companies are sitting on massive volumes of data. In 2025, the global datasphere is projected to reach 181 zettabytes, with industrial sectors like manufacturing and energy contributing tens of zettabytes through decades of accumulated sensor, operational, and engineering data. However, less than 5% of this data is currently used effectively, highlighting untapped potential.
Second, AI models have become significantly more powerful and efficient. The cost of running large language models has dropped nearly tenfold each year, with token inference costs falling from around $60 per million tokens in 2020 to under $0.06 in 2025. This massive drop, roughly 1,000 times over three years, makes Gen AI applications faster, more affordable, and accessible across organizations of all sizes, enabling experimentation at a fraction of the previous cost.
Use Case 1: Knowledge Management and Decision Support
Industrial enterprises produce large volumes of documentation, including technical drawings, incident reports, maintenance records, and geological surveys. Much of this information remains unused due to its complexity or inaccessibility. Industrial sectors like energy, utilities, and mining are beginning to realise the real-world impact of Generative AI by unlocking decades of underused documentation, technical drawings, incident logs, and sensor data. For example, Avangrid in the US has deployed a Gen AI assistant that helps technicians troubleshoot turbine faults in real time, cutting downtime and improving maintenance accuracy. Duke Energy is using AI to monitor transformer health by analysing sensor streams and historical reports, while Rhizome’s predictive AI helped one Texas utility cut storm-related outages by 72 percent. In mining, companies using Azure OpenAI services have reduced the time to analyse complex geological data from weeks to minutes, accelerating ore modeling and investment decisions.
Recent infrastructure failures in Texas have further spotlighted the value of AI. In response to Hurricane Beryl and severe July 2025 flooding, CenterPoint Energy partnered with firms like Technosylva and Neara to deploy AI tools that predicted outage risks and guided rapid restoration plans. Meanwhile, researchers at the University of Texas at Dallas have developed AI that can reroute power in milliseconds, offering a vision of self-healing grids. While Gen AI was not confirmed as directly deployed during the most recent flood, these disasters have intensified investment in predictive weather analytics, flood detection systems, and climate-resilient infrastructure powered by AI, shifting Gen AI from theory to necessity.
Use Case 2: Operational Optimization and Safety Enhancement
Generative AI is playing a vital role in optimizing operations and improving safety. Shell has partnered with SparkCognition to accelerate seismic data processing using generative AI, reducing analysis time from months to hours while maintaining high accuracy. Their AI system generates high-fidelity subsurface images using just a fraction of traditional shot data, significantly shortening exploration cycles and lowering computational demands.
By late 2024, Shell had launched over 100 AI initiatives across its global operations. These include the use of generative models for optimizing reservoir modeling, improving drilling strategies, and supporting predictive maintenance in high-risk environments, enhancing both operational efficiency and safety.
Use Case 3: Engineering, Design, and R&D
Siemens and Microsoft have launched Industrial Copilot within NX X on Azure, enabling engineers to execute complex design edits, simulations, and automation code generation through natural-language prompts. Tasks that previously took hours or days are now done in minutes, cutting programming time by around 80 percent. This solution is being used by companies like Schaeffler and thyssenkrupp, who report accelerated turbine layout and battery assembly modeling along with significantly reduced manual coding errors.
Use Case 4: Workforce as Co-Pilot and Governance
Accenture’s 2025 research highlights that top-performing enterprises with strong AI governance are 2.9 times more likely to create enterprise-level AI value and 4.5 times more likely to employ Gen AI agents. They emphasize embedding human oversight and prompt-engineering roles into AI workflows. Additionally, Microsoft and Siemens Energy are working with EPRI to develop enterprise-grade AI sandboxes and responsible deployment frameworks for utilities, providing a secure path to integrating Gen AI into safety-critical operations.
What Leaders and Boards Should Consider
Generative AI is becoming a central part of the digital revolution in many organizations. Executives and board members must now make intentional decisions about how to use it well. This means looking beyond the hype and making careful, long-term choices. Here are the key things they must focus on:
1. Treating AI capabilities as a long-term competitive advantage
Companies that treat generative AI as a one-time project often fail to get results. The real value comes when it becomes part of how a company works. Amazon has quietly embedded AI across its business for years. It uses AI to improve warehouse logistics, product recommendations, and delivery timelines. As a result, Amazon runs with higher efficiency and better customer insight than many of its competitors. Boards need to think about AI as they would any strategic capability, like brand or supply chain. They should ask how it will give them an edge over time and which areas of the business can benefit from ongoing AI use.
2. Investing in infrastructure and talent to support scalable deployment
To get value from generative AI, companies must invest in the right tools and people. This includes data pipelines, cloud infrastructure, and secure computing environments. It also means hiring or training engineers, data scientists, and AI product leads. Goldman Sachs has built internal AI platforms to help teams test and deploy AI models quickly. The company trains employees to use these tools, so teams can build AI features themselves. This approach keeps innovation in-house and protects customer data. Boards must support these kinds of investments early. Without them, AI projects remain stuck in pilot mode and never scale.
3. Creating systems for risk governance, oversight, and validation
AI can create risks if not used properly. This includes biased results, hallucinations, and data privacy issues. Boards must make sure that the company has a clear framework to manage these risks. For example, Microsoft has set up an Office of Responsible AI. This team works across departments to review AI use cases, test models, and ensure accountability. It also trains staff on ethical AI practices. Boards should push for similar guardrails, not only to protect the business but to build trust with customers and regulators.
4. Encouraging adoption through internal training and change management
Employees often feel unsure about how to use generative AI. Some worry it will replace their jobs. Others do not know where to start. Companies need strong internal communication and training to support adoption. Adobe, for example, has embedded AI into tools like Photoshop and Illustrator. But it also trains creative professionals to use these features to save time and boost creativity. Adoption improves when people feel confident and supported. Boards must ensure that change management is not an afterthought but a core part of any AI rollout.
5. Measuring value through specific metrics such as process time savings or reduction in operational failures
The impact of AI must be tracked with clear numbers. These should go beyond cost savings to include time saved, improved quality, or fewer errors. For instance, Siemens uses AI in its manufacturing processes to predict equipment failures and reduce downtime. By measuring performance over time, it has shown clear returns on its AI investments. Boards should ask for these kinds of metrics regularly. They help keep efforts focused and show where more investment is needed.
6. Engaging with external partners, regulators, and industry groups to shape safe AI adoption
No company can manage AI in isolation. Leaders must engage in wider conversations about how to build and use AI safely. Meta, OpenAI, Google DeepMind, and others have formed partnerships to publish safety guidelines and coordinate research. This type of collaboration helps shape the future of AI in a way that supports both innovation and ethics. Boards should encourage their executives to take part in such consortia. It also helps companies stay ahead of regulation and public expectations.
Looking Ahead
There is growing public concern about whether generative AI will live up to its promise. Some experts have warned that productivity gains are slower than expected. Others say the risks may outweigh the benefits. But there are still many bright spots. When used with care, AI has helped improve drug discovery, reduce fraud, speed up coding, and personalize learning. The challenge now is to take what works and apply it more broadly.
If we take the best lessons from successful companies and apply them across industries, the future still looks promising. AI can help with climate monitoring, better public services, smarter education, and safer manufacturing. But this will only happen if boards and leaders treat it as a journey, not a trend.
This is the moment to lead with clarity, invest with purpose, and build with care.