The pace of change in energy markets has never been faster. Price signals that used to develop over several days are now moving within calculation windows. Geopolitical upheavals that used to take weeks to ripple through commodity markets are now engulfing interconnected European power grids and global liquefied natural gas (LNG) supply chains in hours. And amid this acceleration, the decision windows available to utilities, traders and asset operators are shrinking dramatically. Of course, complexity and uncertainty are not new to energy markets. There have always been geopolitical disruptions, technology cycles and regulatory upheavals. But this time, the speed of change, the volume of market signals and the relationship of risks are qualitatively different, and the cost of slow or ill-informed decisions has risen accordingly. Speed without precision is not a competitive advantage; faster wrong decisions just increase the impact.
COMMENT
The answer isn’t just more data or faster systems. This decision-making maturity is a concept that is fast becoming a strategic feature in the electricity sector.
Defining a crisis of complexity
Today’s operating environment presents a unique challenge for companies operating in the UK and European energy markets, and the factors go far beyond the structural transition from traditional to mixed traditional – renewable generation. To frame the challenge, consider the “5 Cons” of big data: velocity, volume, variety, veracity, and value. The energy applications occurring in energy systems drive all five simultaneously, and the implications for decision-making are profound. (caption id=”attachment_265764″ align=”alignleft” width=”197″)
Brock Masovsky(/caption)Starting with speed: The granularity of calculations is getting tighter. The UK is moving towards a half-hourly schedule, while most of Europe already operates at 15-30 minute intervals. Asset owners must simultaneously participate in spot markets, demand response programs, and ancillary network services markets—each with their own data rates and decision horizons. The volume is growing in parallel. More distributed generation assets, combined with demand-side additions to the market equation—electrified heating, transportation, and industrial load switching—have greatly increased the number of market signals that need to be absorbed and interpreted. Battery storage, both grid and distributed, adds even more complexity as operators balance asset dispatch with real-time price signals and forward-looking market positions. Variety is expanding thanks to new price dynamics. Solar saturation in markets like Germany has led to settlement windows with very negative power prices that rise and fall quickly, creating both significant risk and short-term opportunities for market participants. These are not exotic extreme cases; they become structural features of the market. Validity and value are the most important dimensions. Interconnected European energy markets and global LNG supply chains mean that external shocks—the COVID-19 pandemic, the war in Ukraine, the ongoing instability in the Middle East and around the Strait of Hormuz—are now spreading across borders faster than traditional risk models predict. In isolation, any of these events can be dismissed as a low-frequency emission. Taken together, they reflect a fundamental pattern that organizations should plan structurally rather than reactively. Regulatory complexity amplifies this. Evolving subsidy frameworks, capacity market reforms and national decarbonisation policy trajectories all require ongoing input into commercial and investment decisions. Even long-term positions that appear settled can change quickly – whether driven by political events or new demand dynamics, such as rising AI data center workloads. Companies that lack the infrastructure to quickly integrate these signals are making decisions based on incomplete information.
The best solutions don’t come from tools alone
The market’s response to data complexity has been the proliferation of specialized tools—platforms that offer enhanced market data feeds, AI-powered analytics, scenario modeling, and real-time risk monitoring. Many of these tools provide real capabilities in isolation. But when deployed without an integration architecture, they accumulate into a fragmented stack that increases operational friction rather than reducing it. (caption id=”attachment_265765″ align=”alignright” width=”226″)
Stuart Wallace(/caption)The main problem is that data and information are not synonymous. Data is the raw material; it must be processed, contextualized and tested before it can support a decision. Information only becomes relevant to decision-making when it is trusted, and trust requires both analytical rigor and governance. Without them, organizations spend time checking and rechecking results across different systems, introducing the very delay that better tools were supposed to eliminate. Worse, when analytics functions are distributed across too many disconnected platforms, firms risk coupling pathology: the findings of one system are used to confirm the findings of another in a feedback loop that creates latency without resolution. More data and more tools, in the absence of an integrated decision-making infrastructure, can slow down decision-making rather than speed it up.
What does decision-making maturity look like?
Decision maturity is not just a data management discipline; it covers the analytical layer, governance structure and organizational workflows through which data becomes decisions. It’s the combination of a robust data infrastructure, transparent analytics tools, and a structured decision-making system with appropriate stakeholder engagement at every stage that together enables companies to act quickly and confidently across the organization. A critical factor is prior integration. When a market disruption occurs or a short-term opportunity presents itself, less mature organizations face a sprint to consolidate and reconcile data across disparate systems before analysis can begin. By the time a decision is made, the window may close. In contrast, more mature organizations have already aligned their data models, analytical frameworks, and decision-making workflows, allowing them to respond without the friction of ad hoc integration. This forward integration also allows for a shift from reactive to proactive decision making. Companies with an advanced decision-making infrastructure routinely simulate scenarios in advance so that when a market event triggers a pre-planned decision-making path, the analysis that supports it is already done. Less trained competitors still diagnose the situation when their more mature colleagues are already working. A common misconception should be addressed: that a more structured decision-making infrastructure must be slower because of its complexity. In general, the opposite is true. Insufficiently reliable processes create hidden lag due to a lack of analytical confidence – decisions stall not because the process is slow, but because participants do not trust the information in front of them. A well-designed decision-making infrastructure eliminates these fluctuations. Firms with mature decision-making capabilities can change trading positions, risk management strategies, and capital allocation decisions at a speed that ad hoc processes cannot match.
Management: Accelerating decisions without relinquishing control
Decision-making maturity is not a liability for automation at the expense of supervision. This does not mean delegating consistent decisions to opaque AI models or off-the-shelf solutions that reduce the firm’s ability to perform differentiated analysis and improve productivity. The appropriate structure is managed acceleration: increasing the speed and quality of decisions while maintaining an organizationally appropriate level of accountability. This may involve automating certain well-defined types of low-ambiguity decisions, but only within well-defined risk parameters and management protocols. Where decisions have significant financial, regulatory or reputational consequences, human control and judgment remain important. Most importantly, decisions must be explainable, justifiable and verifiable. It’s not just an internal management requirement – it’s increasingly regulatory. In the EU, the AI Act, enhanced by the AI Omnibus amendment, imposes strict compliance requirements on AI implementations that are classified as high-risk. UK organizations operate under a different legal framework, but the government’s Pro-Innovation AI Framework program shapes the expectations of existing regulators in a similar way. Internationally, NIST, the OECD and the Council of Europe are collectively raising the bar for AI governance across jurisdictions. Companies investing in decision infrastructure must ensure that the control architecture is built into the design rather than retrofitted after deployment.
Maturity of decision-making provides an advantage in decision-making
The complex crisis faced by energy market participants is structural, not cyclical. The greater complexity of decision-making comes just as decision-making windows are shrinking – and there are no signs that either trend will change. If anything, the pace of market evolution, regulatory change, and geopolitical disruption will continue to increase. In such an environment, the firms able to outperform will not simply be those with access to the most data or the most sophisticated analytical tools. They will be those with the organizational maturity to effectively transform available data into actionable intelligence and the confidence in their decision-making infrastructure to act decisively on that data, outpacing competitors still struggling with the friction of fragmented systems. Decision-making maturity is ultimately a form of structural readiness. This does not eliminate the uncertainty; it equips organizations to operate effectively within it. In an era of volatility, such preparedness is a solid source of competitive advantage.—Brock Masovsky is senior vice president of commercial analytics Stuart Wallace is Head of Design and Data Management at Zema Global.