SLB and Qualcomm Technologies Join Forces to Advance Edge AI Across Energy Operations
Global energy technology leader SLB has announced a new memorandum of understanding (MoU) with Qualcomm Technologies, Inc. aimed at accelerating the adoption of edge artificial intelligence (AI) solutions throughout the energy industry. The collaboration seeks to bring advanced AI capabilities directly to operational environments, enabling real-time decision-making across wells, production facilities, and critical energy infrastructure.
The partnership combines Qualcomm Technologies’ expertise in low-power edge computing and AI processing with SLB’s Agora™ edge AI and Internet of Things (IoT) solutions. Together, the companies aim to develop technologies capable of supporting energy operators in remote, complex, and highly demanding operational environments where rapid decisions can significantly impact efficiency, reliability, and safety.
As energy companies continue their digital transformation journeys, the need for intelligent systems capable of operating independently and responding instantly to changing conditions has become increasingly important. Traditional centralized computing approaches often face limitations due to connectivity challenges, latency issues, and operational constraints. By moving AI processing closer to the point of operation, SLB and Qualcomm Technologies hope to address these challenges and unlock new opportunities for automation and autonomous workflows.
Bringing Intelligence Closer to Operations
The energy sector is increasingly embracing advanced digital technologies to optimize production, reduce operational costs, and improve asset performance. However, many energy assets operate in remote locations where reliable connectivity cannot always be guaranteed. Offshore platforms, remote wellsites, pipeline networks, and isolated production facilities often require rapid responses to changing operational conditions.
Edge AI addresses these challenges by enabling data processing and decision-making directly at or near the source of data generation. Rather than transmitting large volumes of information to centralized data centers or cloud-based platforms, edge AI systems analyze data locally, allowing operators to respond more quickly and effectively.
According to SLB, the collaboration with Qualcomm Technologies is designed to help energy operators implement AI solutions that align with the realities of industrial operations. These systems can support continuous monitoring, predictive maintenance, equipment optimization, production enhancement, and automated operational workflows while reducing dependence on centralized infrastructure.
Rakesh Jaggi, President of Digital at SLB, emphasized the importance of practical AI deployment within energy operations.
“Together, SLB and Qualcomm Technologies aim to help operators apply AI more effectively across energy infrastructure,” Jaggi said. “Many energy operations rely on real-time decision-making in remote environments where connectivity and responsiveness directly affect performance. AI systems designed around the realities of energy operations can help support more consistent and autonomous workflows across those environments.”
His comments highlight a growing industry trend toward operational intelligence that functions reliably even in challenging field conditions. The ability to make decisions closer to equipment and production assets can improve responsiveness while reducing operational disruptions.
Growing Demand for Autonomous Energy Systems
The partnership comes at a time when energy companies are rapidly increasing investments in automation and autonomous operations. Across upstream, midstream, and downstream sectors, organizations are exploring ways to leverage AI and machine learning technologies to improve operational efficiency and enhance decision-making capabilities.
One of the emerging concepts gaining momentum is agentic AI—intelligent systems capable of independently analyzing information, making decisions, and executing actions with minimal human intervention. These technologies have the potential to transform production environments by supporting autonomous workflows that can continuously adapt to changing operational conditions.
However, achieving this vision requires computing power that can function effectively within operational environments rather than relying entirely on centralized cloud systems. This is particularly important in industrial settings where connectivity limitations, cybersecurity requirements, and latency concerns can impact performance.
The collaboration between SLB and Qualcomm Technologies aims to address these requirements by integrating advanced AI processing directly into energy operations. The companies believe that bringing intelligence closer to equipment and operational workflows can create more resilient systems capable of functioning effectively even in environments with limited connectivity.
By enabling AI processing at the edge, operators can gain faster insights, reduce delays in decision-making, and improve overall operational continuity. This approach also supports the growing need for operational resilience as energy infrastructure becomes increasingly digitized and interconnected.
Combining Industry Expertise and Advanced Computing
A major strength of the collaboration lies in the complementary capabilities of the two organizations.
SLB brings decades of experience in energy technologies, digital production systems, and operational expertise across global energy markets. The company’s Agora™ edge AI and IoT solutions portfolio has been specifically developed to support remote industrial environments where operational complexity and reliability are critical.
Qualcomm Technologies contributes advanced edge computing technologies designed to deliver powerful AI processing capabilities while maintaining low power consumption. The company has established a strong reputation in developing processors and intelligent computing platforms that support a wide range of industrial, automotive, and embedded applications.
Together, these capabilities are expected to create a foundation for new AI-driven applications across energy production operations. Potential use cases may include real-time asset monitoring, intelligent process control, predictive maintenance, production optimization, anomaly detection, and automated operational decision support.
Nakul Duggal, Executive Vice President and Group General Manager for Automotive, Industrial and Embedded IoT, and Robotics at Qualcomm Technologies, highlighted the importance of efficient AI systems for industrial environments.
“Many industrial environments require AI systems that can operate with limited power, constrained connectivity, separation between operational technology and information technology environments, and real-time operational demands,” Duggal said. “This collaboration brings Qualcomm Technologies’ low-power AI processing closer to energy operations, alongside operating assets, helping enable edge intelligence for new use cases and supporting progress toward more autonomous workflows.”
His remarks underscore the growing importance of AI systems that can function independently while meeting the unique requirements of industrial operations.
Supporting Cybersecurity and Legacy Infrastructure Modernization
Another significant aspect of the collaboration is its potential to help operators modernize aging infrastructure while strengthening cybersecurity.
Many energy facilities continue to rely on legacy operational technologies that were not originally designed to support modern digital capabilities. Integrating AI into these environments often requires solutions that can operate within existing systems without disrupting critical operations.
By deploying AI at the edge, operators may be able to enhance performance and intelligence without extensive changes to centralized infrastructure. This approach can simplify digital transformation efforts while providing additional layers of operational security.
The companies also noted that edge AI architectures can help strengthen cybersecurity by reducing the need to transmit sensitive operational data across networks. Processing information locally can minimize exposure to potential cyber threats while maintaining the integrity of operational systems.
Advancing the Future of Energy Operations
The memorandum of understanding reflects a broader shift within the energy industry toward intelligent, autonomous, and resilient operations. As organizations seek to maximize efficiency, reduce costs, improve sustainability, and enhance reliability, AI is becoming an increasingly important component of operational strategy.
By combining energy-domain expertise with advanced edge computing technologies, SLB and Qualcomm Technologies aim to accelerate the development of next-generation AI solutions capable of transforming how energy infrastructure is managed and operated.
The collaboration signals growing confidence in edge AI as a critical enabler of future energy systems. As operators continue to embrace automation and digitalization, technologies that bring intelligence closer to the field are expected to play a central role in supporting safer, smarter, and more efficient energy production worldwide.
With the partnership now underway, both companies are positioning themselves at the forefront of a rapidly evolving landscape where real-time intelligence and autonomous decision-making are becoming essential requirements for modern energy operations.
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