Guilherme Studart is the co-founder of Delfos Energy, an AI Asset Performance Management solution for Wind, Solar and Storage.
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“AI-powered” has become the new “digital.” Today, nearly every SCADA dashboard, maintenance platform and analytics tool in renewable energy claims some form of artificial intelligence (AI). But for operators managing real assets in the field, the question is no longer whether a platform uses AI. The real question is whether that intelligence actually improves operational decision-making.
In renewable operations, the challenge is not a lack of data. Most operators already have more data than their teams can realistically process. The real difficulty is turning operational signals into coordinated action across maintenance, forecasting and commercial teams.
For asset managers overseeing hundreds of megawatts across different geographies, value is tangible. It means identifying a turbine fault before it becomes a weekend outage. It means avoiding lost production because maintenance, market and operations teams were working from different assumptions.
A decade ago, standalone SCADA visualization tools were common across the industry before many of those capabilities became integrated directly into OEM and enterprise systems. Something similar is now happening with AI. Predictive-maintenance platforms, forecasting engines and optimization tools are increasingly becoming embedded capabilities inside broader industrial software ecosystems rather than standalone products.
Large language and reasoning models are accelerating this shift. AI-assisted workflows are gradually moving directly into operational environments instead of existing as external add-ons that teams have to manage separately.
But consolidation alone does not create operational intelligence. The real differentiator is whether these systems improve decision quality inside day-to-day asset management.
Most operations teams already deal with some level of alert fatigue. A single turbine event can trigger notifications across multiple disconnected systems before anyone fully understands whether intervention is even necessary.
Automation already handles many repetitive operational tasks well: generating reports, standardizing availability calculations, opening work orders and monitoring alarms. But intelligence requires more than automation.
A truly effective renewable-operations system should be able to:
• Execute tasks consistently and accurately
• Interpret operational context
• Apply guardrails that reduce false positives
• Learn from historical fleet behavior
• Support human judgment when decisions involve operational or commercial trade-offs
For example, an inverter alarm may initially appear isolated. But once historical fleet behavior, weather patterns and maintenance history are analyzed together, operators may realize the issue reflects a broader degradation trend developing across multiple sites.
That shift from reacting to symptoms toward understanding root causes is where operational intelligence begins to create value.
According to McKinsey & Company insights on AI in energy operations, a significant portion of industrial operational data is never meaningfully analyzed. Some renewable operators still rely on spreadsheets for reporting and budgeting. When half the operational process lives in Excel and the other half lives across disconnected vendor platforms, adding another AI layer often creates additional complexity instead of solving the coordination problem.
The operational consequences are familiar across the industry.
A turbine trips offline and triggers a SCADA alarm. A predictive-maintenance platform flags abnormal gearbox temperatures, but the maintenance platform lacks weather or access-road context. Meanwhile, commercial teams continue planning around outdated assumptions of available generation capacity.
Each individual system may be digitally optimized on its own, yet the broader operational process remains fragmented.
IRENA World Energy Transitions Outlook 2024 warned that fragmented data architectures risk reproducing operational inefficiencies instead of resolving them. Asset managers see this reality every day. A dashboard can appear sophisticated while the underlying operational coordination remains weak.
It starts with treating data as operational infrastructure rather than simply as software input. Information from SCADA systems, CMMS platforms, meteorological forecasts and grid telemetry need to be structured, aligned and connected.
Interoperability matters just as much. Maintenance systems, forecasting engines and market operations need to communicate directly with one another. Otherwise, operational insights remain trapped inside reports instead of influencing decisions in real time.
Human oversight also remains critical. Experienced operators still recognize contextual realities that models often miss, especially during abnormal weather events, curtailment periods or grid instability. In practice, hybrid systems where AI supports decision-making rather than replacing it consistently produce more reliable operational outcomes.
Most importantly, intelligence must remain actionable.
Asset managers do not need more dashboards. They need prioritized recommendations tied directly to operational and financial outcomes: which asset requires intervention, how urgent the issue is and what production impact may result if action is delayed.
When AI outputs connect clearly to metrics such as availability, performance ratio and levelized cost of energy, teams can finally measure operational value instead of software activity.
Over time, AI in renewable operations will likely become standard infrastructure rather than a differentiator on its own. I suspect that within a few years, most operators will stop talking about AI entirely. It will simply become part of the operational stack, much like SCADA systems or forecasting tools are today.
Operators will care less about whether a platform “uses AI” and more about whether teams trust the decisions it supports.
Faster is not always smarter. Automation without governance can create operational risk, especially when systems begin making decisions without sufficient operational context. But intelligence that helps teams prioritize actions, reduce uncertainty and improve coordination can strengthen both operational performance and long-term asset value.
The conversation, therefore, is no longer simply about adopting AI.
For every asset manager looking at a crowded operational dashboard on a Monday morning, the more important question is much simpler: does the intelligence inside my system actually help my team make better decisions, or does it just create more noise?
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