AI in the energy business: hype or reliable future technology?
AI has become the key technology of our time, sparking debate among experts and policymakers about its potential benefits and risks. With the widespread availability of tools like ChatGPT, AI has entered both households and businesses, transforming the way we work and communicate. The energy sector is not an exception. Companies are increasingly seeking automation, enhanced user experiences (e.g., through natural language interfaces), advanced data processing techniques, and superior decision-making tools.
There are numerous areas within the energy industry where AI is being explored, tested, or already implemented. AI can play a role in all areas of energy trading, from front- to middle-, and back-office operations. The front-office applications are the most well-known, particularly in areas like natural language interfaces for software interaction. This is especially valuable in tasks like reporting and deal entry, where users could generate reports simply by typing commands in plain language—without needing detailed knowledge of the data structure. Another major area of AI application is predictive analytics, market data analysis and decision support where AI offers powerful tools based on analysis of vast amount of data. These areas currently show the highest adoption of AI-driven methodologies.
In the back office, AI can streamline labor-intensive processes such as reconciliation, regulatory compliance (e.g., using pattern recognition for MAR compliance), and significantly improve user trainings.
AI also plays a role in software development, with tools like GitHub Copilot being explored to accelerate coding tasks. AI can enhance code quality, add logging, generate documentation, and even explain code. Basically, AI proved to be a valuable tool in any language-related tasks, both for natural and programming languages. Additionally, AI can improve interface-building processes, such as automatically transforming incoming formats and importing the data based on API documentation—benefits that are particularly useful for companies operating within complex software ecosystems.
However, alongside these benefits come significant concerns. Industry professionals worry about the quality and security of data used by AI, the trustworthiness of AI-generated results (often seen as a “black box”), and the potential loss of competitive advantage when multiple companies use the same open-source AI tools. It’s still unclear which areas of the energy sector will see widespread AI adoption, and where concerns may outweigh the benefits. Much of this will depend on how effectively software vendors can address these issues by offering AI solutions that are secure, transparent, and customizable to individual needs.
ComTech plans to conduct research in this area to identify the most promising AI applications within the energy sector, analyze how vendors are addressing industry concerns, and gauge the overall sentiment toward AI technology within the energy world. We are seeking sponsors who are interested in promoting their own AI-based solutions and in benefiting from the findings of this market research.
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