IFS and Boston Dynamics announced a ground-breaking collaboration to revolutionise how asset-intensive organisations manage and optimise their field operations. Uniting Boston Dynamics’ autonomous inspection robots with the power of IFS.ai creates a fully agentic AI system that seamlessly connects sensing, predictive decision-making and action in the field. With labour and skills shortages impacting industrial customers, leading to service gaps and prolonged outages, there exists an urgent need for technology that can supplement field workers.
IFS and Boston Dynamics revealed the joint solution for the first time at Industrial X Unleashed in New York. The two companies showcased a powerful combination of physical AI and agentic AI that creates an end-to-end autonomous system to connect robots and enterprise data in high-potential applications. Boston Dynamics’ Spot robots inspect industrial assets and sites, capturing critical operational data in real-time. Spot uses thermal cameras to detect overheating, can listen to air or gas leaks, read analogue gauges for pressure and flow, check indicator lights, identify hazards like spills, or detect voltage anomalies. This information feeds directly into IFS.ai, where agentic AI analyses the data, makes intelligent decisions, and triggers appropriate actions – creating a seamless loop from sensing to execution.
The collaboration uniquely focuses on serving industries where field operations are critical, including manufacturing, energy, utilities, mining, and other asset-intensive sectors. With field workers comprising part of the 70% of the world’s workforce that do not work behind a desk, IFS and Boston Dynamics are unlocking value in areas that have remained underserved by generic AI applications.
Together, IFS and Boston Dynamics are targeting measurable improvements across three critical operational metrics:
- Safety: Autonomous inspections reduce human exposure to hazardous environments while increasing inspection frequency and thoroughness
- Efficiency: Intelligent automation enables faster decision-making and response times, optimising resource allocation
- Uptime: Predictive insights and automated actions help prevent failures before they occur, maximising asset availability
Christian Pedersen, Chief Product Officer, IFS, said: “Asset-intensive organisations face unrelenting pressure to improve operational performance. Together with Boston Dynamics, we’re delivering a truly autonomous system that connects the physical and digital worlds for the first time. IFS.ai and IFS Loops turn robot observations into enterprise action, from preventative maintenance scheduling to predictive failure analysis and automated anomaly detection. Data flows from the field into enterprise systems, decisions are made autonomously, and actions are executed back in the field, all within a single integrated platform.”
Dr Merry Frayne, Director of Product, Boston Dynamics, commented: “This collaboration represents the future of industrial operations. Our robots excel at navigating complex environments and gathering critical data. Combined with IFS’s agentic decision-making capabilities, we’re enabling organisations to achieve levels of operational excellence and safety that simply weren’t possible before.”
Ron Utterbeck, CIO, Eversource, an IFS customer, said: “As the largest New England energy provider managing critical infrastructure across multiple states, this integration has the potential to radically transform our operations. As our grid continues to advance, we need to utilise not only traditional data gathering but also more advanced data gathering and modelling. To meet the reliability and the energy demands for our customers, we look forward to the opportunity in utilising advanced industry technology such to enable data collection at a different level that can support routine inspections of substations and facilities with automatically prioritising and dispatching our crews. This will allow our highly skilled crews to be focused on the right priorities at the right time and ensure mission-critical work is completed. It’s a genuine shift from reactive to predictive maintenance.”
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