IFS and Boston Dynamics launch agentic AI system for field operations
IFS and Boston Dynamics are joining forces to bring agentic AI to field operations in asset-heavy industries. The goal is straightforward: automate routine inspections, make better decisions faster, and turn those decisions into actions without delay.
The collaboration targets manufacturing, energy, utilities, mining, and similar sectors where uptime, safety, and cost control are non-negotiable. The system links Boston Dynamics' Spot robots with IFS.ai to close the loop from sensing to decision to field execution.
How the system works
Boston Dynamics' Spot robots perform autonomous rounds and capture real-time site data. They read analog gauges, check indicator lights, listen for air or gas leaks, scan for voltage issues, and use thermal cameras to spot overheating or spills.
That data flows into IFS.ai, where agentic AI analyzes conditions, predicts risk, and triggers the next best action. With IFS Loops, those actions can include creating work orders, scheduling preventive maintenance, flagging anomalies, and sending robots back to verify fixes-all in one connected platform.
What operations teams get
- Safety: Fewer people in hazardous areas while inspection frequency and coverage go up.
- Efficiency: Faster decisions and responses, better use of crews and parts, and fewer manual checks.
- Uptime: Issues are caught early, so failures are prevented and asset availability stays high.
From insight to action: examples you can run
- Thermal anomaly: Spot detects a hot pump bearing. IFS.ai creates a priority work order, reserves parts, schedules the job, and dispatches Spot post-repair to confirm normal temps.
- Gas leak detected: Acoustic data indicates a leak. The system triggers an isolation checklist, alerts the control room, and logs a compliance record automatically.
- Gauge drift: An analog pressure gauge shows deviation. IFS.ai compares against historical baselines, flags potential calibration drift, and schedules inspection on the next route.
Leadership perspective
According to Christian Pedersen, chief product officer at IFS, the joint system connects field observations directly to enterprise action-linking preventative scheduling, predictive failure analysis, and automated anomaly handling inside a single platform.
Dr. Merry Frayne, director of Product at Boston Dynamics, noted that the combination of autonomous inspection and AI decisioning raises both safety and operational performance in complex industrial sites.
Implementation playbook
- Pick a pilot area: Start with a single line, substation, or pit. Define hard KPIs: MTBF, MTTR, incident rate, and inspection coverage.
- Integrate your systems: Connect IFS.ai to your EAM/CMMS, historians, and alerting. Map data tags to assets and work types.
- Set action policies: Define thresholds, auto-approve limits, and human-in-the-loop steps by risk category.
- Train the crews: Short, focused sessions on exception handling, robot routes, and safety protocols. Keep the feedback loop tight.
- Scale in steps: Expand routes, add more sensors, then move from daily to continuous inspections as confidence grows.
Risk, governance, and control
Keep audit trails for every automated decision and action. Assign clear ownership for model updates, route changes, and policy thresholds. Use role-based access to restrict overrides, and align with site safety rules before enabling closed-loop actions.
Where it fits best
Sites with large footprints, harsh conditions, or strict compliance demands will see results fastest-think refineries, power plants, transmission networks, mills, and open-pit operations. If your team manages many analog instruments or remote assets, autonomous inspections will pay back quickly.
Learn more about the tech behind each component here:
Boston Dynamics Spot and IFS.ai.
If you're building skills for AI-driven operations, you can explore practical training resources here:
AI courses by job role.
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