The 2026 Energy Trading Stack: AI Optimizations Worth the Investment
What is AI’s place in the modern energy trading stack? It’s a question left unanswered for many business leaders in the energy sector, as it lags behind other sectors in terms of AI adoption. Research has found that in utilities, GenAI adoption stands at 17% while it is 50% or higher in finance, healthcare, and manufacturing. This isn’t through a lack of trying. It’s reported that 96% of utility executives view artificial intelligence as strategically important, but only 26% have progressed beyond proof-of-concept deployments.
There’s clearly a bottleneck between idea and execution. Identifying and overcoming the obstacles preventing successful AI-optimization of the energy trading stack is the focus of two talks NTT DATA is presenting at Energy Trading Week in London. This is what trading firms, technology leaders, and market participants need to know about moving forward with new technology strategically.
The modernization mistake: Redirecting your investment
Even the modernization-resistant are looking to AI and other cutting-edge technologies to transform their operations, driven by the need to be fast, flexible, and compliant in order to win in the current energy market. The hesitation is warranted considering emerging statistics about AI transformation success rates, such as 60% of companies not achieving material value at all from their AI investment.
At NTT DATA, with years of experience in digital transformation and the evolving energy sector, one of the biggest mistakes we see time and time again is following the hype, not the business value. Decision-makers assume that if they implement the technology everyone is talking about, business value will follow. That is rarely the case because they take the wrong approach to transformation. They look for a way to make a technology fit within existing systems, prioritizing uptake over outcomes. Meaningful impact is created by identifying operational challenges or untapped opportunities first and using technology to create greater efficiency, capabilities, and cost-effectiveness second. Taking this approach, the digital transformation project starts and ends with business value.
There is plenty of hype around AI in general and how it can transform the energy sector specifically. It’s an increasingly complex, interconnected, and in-demand market. Proposed AI applications vary from creating accurate and reliable predictive analytics despite the volatility of renewable energy sources to cutting down on time spent doing paperwork by auto-generating and auto-sending necessary documents. Before jumping into developing exciting new features, explore where your technology investment is most needed to maximize outcomes.
Another obstacle to innovation success that needs to be addressed is compliance, managing legal responsibilities in a digitalized process. Compliance officers may feel that the greater concern is that, in an AI-powered system, they could lose control over operations. However, this isn’t an issue in well-designed solutions that carefully track inputs and outputs to maintain sovereignty of the AI models.
Architecture pre-checks: What operations really need
There is a question all business leaders need to ask before they move ahead with an AI optimization project. Is our system architecture ready for AI?
AI isn’t a simple add-on. It drastically changes the way of working. Legacy systems often have limitations that prevent the full implementation of AI. The main drawback is fragmented data. AI algorithms cannot produce real-time insights and seamlessly automate actions unless it has access to all the necessary data.
Redesigning the operational architecture creates modernization gains before AI is even part of the equation, producing a business-ready and AI-ready trading stack. Improving connectivity through the cloud and the Industrial Internet of Things (IIOT) are just as important as AI upgrades. Review the end-to-end process of your Energy Trading and Risk Management (ETRM) platform, focusing on:
- Improving visibility - Make key data, analytics, and market movements easily accessible while maintaining appropriate protection and governance. Data sharing supports human decision-making as well as AI capabilities.
- Speeding up decision cycles - Remove unnecessary barriers to acting on real-time information with real-time trades. Speed and flexibility are not just desired but required in today’s market conditions.
- Standardizing processes - While managing renewables requires a level of flexibility, every trade has a conventional workflow. Standardization leads to streamlining and optimization while preparing repetitive tasks for AI automation.
Practical AI applications: Where AI genuinely adds value
The question business leaders are eager to answer is how ETRM systems and AI can combine. The making or breaking of the ETRM has always been data. Understanding the market in the moment is how trading decisions are made and profitability is unlocked. In that way, AI is the perfect technology for the trading sector. It runs on data.
When built on an operational system with clear visibility, streamlined workflows, and standard processes, AI can create valuable gains. The disclaimer still stands to avoid the hype and AI-optimization for the sake of AI-optimization. Practical and thought-through transformations are where the payoff is to be found.
There are certain areas where AI is making a real, meaningful, and sustainable impact in the trading stack:
- Intelligent automation - There are points in the trading process that can be safely automated by AI. These are repetitive tasks that follow a set process which an algorithm can learn, such as documentation around confirmations, settlements, and reporting. Letting AI manage the paper trail speeds up the workflow while maintaining traceability and compliance.
- Pattern detection - One of the biggest advantages of AI is its ability to process and interpret large datasets at a speed and accuracy level that isn’t possible manually. Make the most of this capability by using AI for pattern recognition in the energy market. Integrate AI into predictive analytics to future-proof decision-making and anomaly detection for effective monitoring and fraud detection.
- Human-in-loop decisions - When it comes to trading decisions, humans will still have the final say but AI can assist by collecting and summarizing relevant insights. The partnership of AI analysis and expert perspectives leads to faster and more confident decisions.
In any digital transformation, we need to look at the existing system first. What are the gaps, challenges, and opportunities for growth? With a solid foundation set, we can move on to introducing new capabilities that enhance operations even further. This process can’t be skipped when planning AI optimizations.
At NTT DATA, we have seen generations of technology emerge and generate hype, recognising the patterns in successful implementations compared to transformations that are written off. For a level-headed approach to building an AI-powered energy trading stack, reach out to the NTT DATA team. As your expert guides, we’ll help you avoid the pitfalls of innovation hype and unlock the real value technological upgrades can generate.