Why risk management could define the Middle East’s energy future

This was meant to be the decade of energy transition. Yet mid-decade, the UAE has scaled ahead with its clean energy ambitions while navigating how to keep energy supplies reliable amidst growing demand. In Dubai, the Mohammed bin Rashid Al Maktoum Solar Park has reached a production capacity of 3,860 MW, with plans to exceed 8,000 MW by 2030. Through its Energy Strategy 2050, the country is progressing towards a cleaner and more sustainable energy future.

However, the shift has been no clean sweep, with the Middle East’s energy sector facing disruptions from cyber threats to geopolitical uncertainty. These pressures are making operations more complex and testing how quickly integrated energy portfolios can respond to change.

Spending on upstream and LNG is expected to cross $1 trillion in 2026, according to the IEA. But even as the sector extends hydrocarbon supply into the next decade, it is under increasing pressure to decarbonize both operations and products.

Meeting long-term energy goals, including the UAE’s targets for reducing energy demand and strengthening supply security under its National Energy and Water Demand Management Programme 2050, depends on how well energy portfolios adapt to changing conditions. As parts of the system are taken offline without warning and energy system reliability and resilience come under pressure, risk assessments are becoming an everyday necessity.

What’s changed is that risk has moved down the pipeline, from planning and annual outlooks into everyday operations. As a result, accounting for these constraints and how the business model may change is now more necessary than ever.

From static risk registers to real-time detection

That’s where dynamic risk management comes in. Less a piece of software than a way of decision-making using the same facts, dynamic risk management treats uncertainty as the new normal, using industrial data and artificial intelligence (AI) to track and act on threats in real time.

Industrial organizations now have more data than ever. Data is being collected at every level – from plant historians, planning tools, logistics deliveries, and enterprise systems. Combine it all together with domain expertise in one place, such as in the cloud, and machine learning (a subset of AI) can spot the subtle patterns that indicate how future events could unfold.

Using the resulting insights, operators can plan to adjust operational flows accordingly. At the same time, this end-to-end visibility offers another benefit: with a map of assets and systems comes a clear view of business interdependencies and where disruptions can trigger a domino effect.

The trend is already visible across the US, where shale producers are connecting infrastructure from wellhead to dock in response to takeaway constraints, Deloitte reports. With centralized control centers, AI-infused analytics can use real-time operational data to enhance reliability across field services, while automated IT operations help improve resilience. In the UAE, ADNOC Gas uses AI to analyse real-time and historical equipment data, helping teams detect anomalies and plan maintenance before failures occur.

By shortening decision cycles, dynamic risk management enables engineers to quickly understand the impact of operational changes and explore pathways forward.

Four real-world approaches to managing risk

Recent events have shown how quickly feedstock assumptions can change. When supply lines are disrupted and refineries must pivot to new sources, the advanced planning tools available today can pull in detailed crude assay data and simulate a range of alternative scenarios. As Astron Energy knows, reductions in creating and sharing assays expands operational limits and supports continued profitability — even under adverse conditions.

 Likewise, a supply model can immediately evaluate and adjust operational processes to match variable feedstocks in real time. Using a digital twin — a virtual representation of a real-world asset or process — can identify potential cost and energy savings. For the UK’s Dragon LNG, the resulting industrial insights delivered savings of $1 million from lower operating costs, while keeping the facility on track to becoming a net-zero emissions facility by 2029.

And since the patterns can translate predictions into actionable advice, operators at the downstream end can reliably evaluate a range of complex profit scenarios. In some cases, the AI can even be directed to automatically make split-second adjustments—within strict safety limits—to avert potential production setbacks. Since BP integrated disjointed tools and processes from eight oil refineries onto one cloud platform, its analysts have been able to assess commercial risks and explore options more quickly. A headline result for the global energy leader has been cutting decision time from two business days to two hours, so its daily output of 1.7 million barrels is produced faster and more efficiently.

At the delivery stage, operators need to maintain maximum equipment uptime and ensure safe distribution across pipelines and transport networks. Here, an ecosystem approach is one way to scale industrial transformation. Sharing operational data with internal and external partners delivers the insights needed to spark innovation and growth. Dominion Energy, for example, has been expanding its renewables business as it diversifies its energy mix. To track a constantly changing portfolio of distributed energy production facilities, the company uses a unified asset environment that is created and run automatically. With this end-to-end enterprise visibility, the US energy major identified 42 potential equipment failures in just one year and improved speed to market for environmental data by 50 per cent. Meanwhile, its customers have reduced their own operating risk profiles: access to energy sources and performance data helps them provide proof of regulatory compliance.

Planning for failure when managing risk

Onboarding a new approach to managing risk doesn’t come without its challenges. These differ from one organization to another, but the overarching consideration revolves around data quality. Dynamic risk depends on live signals. If data remains siloed, unstructured and out of context, each question will lead to unreliable answers and potentially expensive losses. A strong central data governance strategy is therefore non-negotiable.

Second, AI. It may be ranked as the most popular technology for managing additional risk responsibilities in the next three to five years, but AI implementations aren’t the same for everyone. To avoid expensive AI failures, start with safe and ethical principles. Build in clear role definition and human oversight, and start with small, non-critical but quantifiable pilots before scaling.

Then there’s interoperability. Across the value chain, each unit uses its own tools and speaks its own language. Success lies in bringing them into a single shared story, for example, within a multi-discipline environment in the cloud.

Perhaps most important is the cultural challenge. The energy sector’s volatility has led to a high-tolerance firefighting culture. Forward planning is boring by contrast, though its returns are potentially greater. Likewise, the values underpinning the renewables sector are significantly different from those of the traditional fuels space. In both cases, embracing a culture of change is the first step to mitigating the risks that threaten resilience and returns.

The transition to cleaner energy will not be straightforward, but progress is expected to continue. As Bloomberg puts it, the economics of renewables are now too compelling to ignore with a net zero-aligned transition projected to raise energy system costs by only 9 per cent.

The collateral benefit is that sustainability contributes to stability, as the ECB noted recently. Pivoting to a stronger, cleaner energy system requires staying the course and planning for the approaching risks.