Artificial intelligence has become a strategic priority for the energy and natural resources industry, but transforming expectations into results remains a pending issue for most companies. 68% of industry leaders believe that AI will have a substantial or transformative impact on the performance of their businesses over the next five to ten years. However, less than 20% say they have managed to scale these initiatives with measurable effects on the business.
This is one of the main conclusions of “AI in Energy: From Pilots to Payoff”, Bain & Company’s new analysis based on its annual survey of more than 800 executives from around the world in the oil and gas, utilities, chemicals, mining and agribusiness sectors. The study highlights how the energy transformation is closely linked to the ability of companies to convert AI into concrete results.
The data reveals a considerable gap between the expectations placed on AI and its actual impact. Many companies continue to experiment on a small scale or develop coordinated pilot projects that have not yet achieved the expected results. For the energy industry, this situation represents a particularly relevant challenge given the need to improve efficiency, optimize resources and respond to an environment marked by the transformation of the energy model.
But the main obstacle to progress does not seem to be in the technology itself. 48% of managers point out that the greatest difficulty lies in the fact that many projects have poorly defined objectives or lack a clear connection with the generation of value for the business. This barrier even exceeds other challenges traditionally associated with digital transformation, such as the shortage of specialized talent (45%), problems related to data quality and governance (43%) or the limitations of the existing technological infrastructure (36%).
“The challenge is no longer proving that artificial intelligence works, but rather determining where it can generate a relevant economic impact and designing the organizational structure necessary to capture it. When companies accumulate pilot projects without a clear link to business results, the risk is having a lot of activity in AI but little real value,” explains Pablo Cornicelli, partner at Bain & Company.
The oil and gas and chemical sectors, the most advanced
The Bain report also identifies where the most significant progress is occurring. The oil and gas and chemical sectors appear to be the most advanced in the practical adoption of AI. In parallel, the functional areas where organizations are obtaining the most tangible results are customer service, research and development (R&D), as well as operations and maintenance.
In the field of energy maintenance, predictive capabilities make it possible to anticipate incidents and reduce downtime
In these functions, artificial intelligence is helping to optimize processes, improve decision making and increase operational efficiency. In the field of maintenance, for example, predictive capabilities make it possible to anticipate incidents and reduce downtime. In R&D, they facilitate the analysis of large volumes of information to accelerate innovation processes, while in customer service they contribute to automating interactions and improving the quality of service. In the context of the energy industry, these applications can also contribute to improving asset management and optimizing increasingly complex operations.
Four keys to moving from pilots to results
Given this scenario, Bain identifies four keys to overcome the experimentation phase and scale artificial intelligence with impact on the business. For companies in the energy field, these recommendations are especially relevant:
- Focus efforts where AI can generate the most value. Instead of dispersing resources among numerous pilots, prioritize two or three areas with potential for economic impact and link them from the beginning to measurable objectives. In energy activity, this can translate into initiatives aimed at improving operational efficiency, maintenance or asset management.
- Redesign processes and ways of working. Scaling AI is not just about incorporating new tools, but about reviewing processes and identifying where technology can improve decision-making and efficiency. This change is essential so that the energy transformation is not limited to the incorporation of new technological solutions.
- Develop capabilities in data, technology and talent. Companies do not need to solve all their gaps before moving forward, but rather reinforce these capabilities in priority areas and combine business and AI knowledge. In the energy sector, having professionals capable of connecting technical knowledge with new artificial intelligence capabilities will be decisive.
- Create a model that allows success stories to be replicated. This requires defining responsibilities, establishing governance mechanisms, and having a common methodology to take initiatives from identification to adoption at scale. In this way, companies can prevent AI projects from being isolated and turn them into a transversal lever for energy transformation.
