{"id":551,"date":"2026-03-30T15:19:08","date_gmt":"2026-03-30T19:19:08","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/insights-hub\/?p=551"},"modified":"2026-03-30T15:20:17","modified_gmt":"2026-03-30T19:20:17","slug":"how-totalenergies-gas-mobility-is-scaling-hydrogen-and-biocng-refueling-operations-with-connected-data-and-agentic-ai","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/insights-hub\/2026\/03\/30\/how-totalenergies-gas-mobility-is-scaling-hydrogen-and-biocng-refueling-operations-with-connected-data-and-agentic-ai\/","title":{"rendered":"How TotalEnergies Gas Mobility is Scaling Hydrogen and BioCNG Refueling Operations with Connected Data and Agentic AI"},"content":{"rendered":"\n

Why Energy Operations Demand Scalable, Always\u2011On Expertise<\/h3>\n\n\n\n

Energy operations don\u2019t get to pause. Assets run across multiple geographies, teams rotate, and critical knowledge is often concentrated in a limited set of practices, playbooks, and diagnostic patterns that must be applied consistently\u2014site after site, shift after shift. That reality is exactly why many energy companies are investing in connected operations technology and exploring Agentic AI not as a novelty, but as a practical way to optimize execution, shorten response times, and scale operational capability globally.<\/p>\n\n\n\n

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At Siemens, we partner with organizations to transform industrial data into actionable insights at scale. One example is our collaboration with TotalEnergies Gas Mobility<\/a>, the team is strengthening day-to-day operations and maintenance of hydrogen and bioCNG refueling stations through connected data, while also evaluating Agentic AI to make operational support faster, more consistent, and easier to scale worldwide.<\/p>\n<\/div><\/div>\n\n\n\n

The Hidden Constraint of Outsourced Operations: Scarce Expertise at Scale<\/h3>\n\n\n\n

In many energy environments, operations and maintenance are frequently outsourced. That model can be effective, but it introduces a familiar constraint: specialized operational expertise becomes a scarce, high-demand capability. Troubleshooting paths, failure signatures, and \u201cwhat to check first\u201d logic must be applied across a large and growing asset base\u2014often with variation by site, contractor, and local practice. The challenge is less about who is available in the moment and more about how expertise is captured, standardized, and embedded into repeatable workflows so that decisions remain consistent, auditable, and scalable across regions.<\/p>\n\n\n\n

From Reactive Troubleshooting to Structured Operational Management<\/h3>\n\n\n\n

This is where connected operations technology becomes foundational. When asset data is consistently captured and contextualized, teams can move from reactive troubleshooting to structured operational management. The goal is not \u201cmore data\u201d or \u201cmore dashboards.\u201d The goal is to enhance decision-making in real-world conditions, enabling teams to detect anomalies earlier, troubleshoot with improved context, and align on a consistent response approach, even when third-party partners and distributed teams are involved.<\/p>\n\n\n\n

Using Agentic AI to Codify and Scale Tacit Operational Knowledge in Hydrogen and BioCNG Refueling Operations<\/h3>\n\n\n\n

Agentic AI builds on that foundation by addressing the most challenging aspect to scale: tacit, experience-based expertise. The most valuable operational knowledge often resides in patterns\u2014recognizing early indicators, separating noise from signal, and selecting the fastest path to the root cause. That’s difficult to document and even more challenging to implement across different geographies. Agentic AI offers a practical way forward by translating proven diagnostic approaches into repeatable “skills\u201d that guide teams with the same rigor and consistency every time.<\/p>\n\n\n\n
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We\u2019re building a way to codify operational know-how\u2014so proven diagnostic and decision practices become reusable capabilities we can scale across our hydrogen and bioCNG refueling stations worldwide.\u201d<\/em><\/p>

David Dowling, Manager of Data and Connectivity, TotalEnergies Gas Mobility<\/sub><\/p>\n<\/blockquote>\n\n\n\n

Engineering AI Agents for Repeatable, Real\u2011World Performance<\/h3>\n\n\n\n

A key learning from their early work is that strong agents are engineered, not improvised. Teams often assume the value comes primarily from the AI model, but in operational settings, the differentiator is how clearly the work is defined\u2014inputs, constraints, and success criteria.<\/p>\n\n\n\n

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