Why you shouldn’t let AI model your risk
First published on: 8/6/2026
Robust risk modeling remains at the top of the agenda for those operating in today’s energy markets. Energy markets are structurally more volatile than ever, geopolitics directly translates into price and supply risk, and uncertainty has become the one reliable constant.
For organizations aiming to model risk safely and turn market volatility into Decisioning Advantage, AI cannot be the strong point of reliance.
A recent focus group of Zema Global customers revealed that the primary concern for industry stakeholders in today’s energy markets is outdated risk systems.
With spiraling data volumes and once-in-a-generation events occurring seemingly weekly, businesses need a robust Decisioning Infrastructure. It acts as a foundational layer that organizes and prepares data for analytics, AI, and human judgment, so their risk models can withstand today’s mercurial markets.
AI learns from the past, but energy risk is about the unpredictable
Energy markets are shaped by outlier events: from geopolitical conflict and extreme weather, to regulatory shocks and grid failures.
In just the past twelve months, we’ve observed the unprecedented unfold in real time. In 2025, Europe watched the Iberian Peninsula go dark as its grid collapsed. Meanwhile, in the U.S., wildfires in California caused $60 billion in damage.
We entered 2026 with the U.S. incursion into Venezuela sending geopolitical shockwaves across the world. The latest Middle East conflict escalated rapidly jeopardizing shipping through the Strait of Hormuz — one of the world’s most critical energy corridors — sending oil prices surging and rattling global supply chains.
These events underscore a crucial truth: the future often rhymes with the past, but it doesn’t replicate it — and that’s exactly where AI struggles. To address this, organizations need to expand their thinking to include multiple scenarios and forward‑looking modeling. AI models are optimized to respond to historical patterns and probability distributions, yet energy risk is dominated by constant and unexpected shifts.
The biggest risks are precisely those with weak historical signal, making human judgment, robust governance, and well‑designed scenario frameworks essential.
What’s more, trading desks rely on volatility and energy risk as an opportunity, buying when they expect large price moves and selling when they expect stability. However, relying on AI as a predictor of volatility in energy markets can lead to flawed assumptions.
Robust, governable data with human oversight is key when capitalizing on the unpredictable nature of energy markets. AI building blocks can be used to operationalize governed data and portfolio analytics, but they fail where firms attempt to simply add another black box.
Data quality and coverage are structural pillars for healthy risk systems
Energy data can often be fragmented, delayed, revised and incomplete. Feeding data which is less-than-perfect through AI amplifies biases and blind spots already present in datasets.
Sophisticated AI models are incapable of fixing flawed inputs. Instead, they scale their impact. Data is more accessible than ever due to innovation in data storage. Today, AI offers the ability to derive insight quickly from these mammoth datasets.
This raises a fundamental issue: data is easier to gather but difficult to manage. This self-perpetuating dilemma coils back upon itself. Organizations become data rich but insight poor.
Ungoverned data lakes often lead to a “mushrooming effect” whereby business users, lacking context or understanding of data lineage, pull the wrong information for their AI analyzes, resulting in a rapid spread of misinterpretations that grow unseen until they become systemic.
Using AI to model your risk without a reliable infrastructure in place can lead to flawed, high-risk outcomes and missed opportunities.
The right role for AI
It’s indisputable that AI excels in several arenas. Pattern detection, stress-testing assumptions, and monitoring for structural weak points are all areas in which AI can help organizations manage their energy risk. But AI risk models are only as good as the data they are fed. Final risk judgments should remain human-led, integrating deep domain expertise, strategic context, and regulatory considerations.
This is where Decisioning Infrastructure — which organizes and prepares data, embeds curve analytics, and systematizes portfolio level analysis for AI and human decisioning — becomes invaluable.
The shift required to stay ahead of compounding risk: anchor both data and analytics in Decisioning Infrastructure, ensure institutional memory remains at the center of risk models, and take a human-led approach to AI and modeling.
