Industrial AI is moving from slideware to shop floors, substations, hangars, and warehouses. Two announcements from IFS at Industrial X Unleashed (Nov 13, 2025) show exactly how: a deep AI partnership with Anthropic and a robotics integration with 1X Technologies. Together, they point to an enterprise future where data, AI agents, and humanoid robots work in the same operational loop, governed, auditable, and tied directly to uptime, safety, and throughput.
What’s New
Ecosystem snapshot
IFS used Industrial X Unleashed (Nov 13, New York) to show how its Industrial AI platform plugs into frontier models, robotics, and grid tech, moving AI from “feature” to field execution. The through-line is closing the loop from sensing → decision → action across heavy-asset industries.
IFS + Anthropic: “Resolve” for frontline operations
IFS Nexus Black (the company’s industrial AI accelerator) is partnering with Anthropic to put domain-specific AI in the hands of field techs, plant engineers, and dispatchers. The first solution, Resolve, interprets multimodal inputs (video, audio, telemetry, schematics), predicts failures, guides fixes, and auto-captures work for future prevention aimed at asset- and service-centric industries like energy, utilities, telecom, manufacturing, A&D, and construction.
Early outcomes: Resolve (powered by Claude) is already cutting downtime on a live distillery site (projected £8.4M annual savings) and helping utilities restore power ~40% faster after major events.
IFS + Boston Dynamics: autonomous inspection meets enterprise AI
IFS and Boston Dynamics are fusing autonomous inspection robots (e.g., Spot) with IFS’s agentic AI to create a true sense–decide–act loop in the field, turning robot observations into enterprise actions like preventative maintenance and anomaly response. The focus is on measurable impact in safety, efficiency, and uptime for asset-intensive operations.
IFS + 1X Technologies: humanoid robots in enterprise workflows
IFS will integrate 1X’s humanoid robots with IFS.ai so robotic work can be scheduled, governed, and measured inside the same business systems that run production, maintenance, and field service. This is not a “demo robot” play; it’s a roadmap for production use across smart factories, MRO, utilities, and warehousing, with commercial availability targeted for 2026. Robots won’t operate in isolation; they’ll feed real-time data into IFS.ai and execute work orders in a business context.
IFS + Siemens: accelerating the autonomous grid
IFS and Siemens are pairing grid planning and smart-infrastructure expertise with IFS’s EAM/field service/AI scheduling to bridge engineering plans to real-time field execution. The goal: faster grid modernization, lower risk, and reliable integration of distributed energy resources, moving toward an autonomous enterprise grid.
Why It Matters
It targets the real constraint: skilled labor. IFS’s vision is a “multiplied” workforce, humans for judgment, digital workers (AI agents) for analysis and workflows, and robotic workers for physical execution, all managed as one enterprise system. In sectors facing a million-job gaps and rising demand from re-industrialization and AI infrastructure build-out, output can’t hinge on headcount alone.
It’s built for brownfield reality, not greenfield fantasy. Resolve sits over existing ERP/EAM/SCADA stacks, pulling signals from what you already have and guiding techs in the moment. That lowers change risk and accelerates time-to-value compared to “rip and replace.”
It closes the loop from event → decision → execution → learning. With humanoids and inspection robots in the same orchestration layer as AI agents and human crews, sensor data and work results flow straight into planning, compliance, and continuous improvement across the enterprise.
What It Looks Like in the Field
Disaster response
During storms and wildfires, Resolve helps planners predict impact windows, route crews to the highest-priority sites (even coordinating mutual aid), and guide on-site repair from images/video while auto-redirecting parts. Utilities using comparable Resolve-assisted workflows report restoring power ~40% faster after major events.
Field service automation (Utilities & Energy)
IFS.ai’s agentic “digital workers” orchestrate field workflows end-to-end: robots (Boston Dynamics, 1X) capture conditions, AI predicts failures, and work orders auto-trigger, dispatching crews or robots, reserving parts, and updating Enterprise Asset Management and Operations and Maintenance EAM/OMS on completion. Resulting in higher safety, faster response times, more accurate provisioning, and timely preventive maintenance.
Humanoids in enterprise workflows
With 1X + IFS.ai, robots won’t run “off to the side.” They can be scheduled for inspection rounds, basic handling, or hazardous tasks, with telemetry and outcomes recorded directly to enterprise systems, so maintenance plans, inventory, and service levels update in real time.
Where This Is Headed (and What to Watch)
- Safety, security, and governance
Asset-intensive environments demand rigorous guardrails. IFS emphasizes responsible, safe AI and enterprise-grade controls; customers should still insist on clear audit trails (who/what acted), data residency posture, and fail-safe behaviors for both AI agents and robots before scaling beyond pilots. - Integration depth and “system of record” boundaries
The value comes from Resolve and robots living inside work management, dispatch, parts, and completions, not as bolt-on dashboards. Map which system is authoritative for assets, work, and compliance to avoid data drift as you automate more steps. - Change management for a three-type workforce
When humans, AI agents, and robots share processes, roles shift fast. Define new standard work, escalation paths, and skills (e.g., prompt-to-procedure authoring, robot task design). Treat this as an operating model change, not just a tech rollout. - Start narrow, scale outcomes
Early wins come from high-frequency failure modes (pumps, valves, drives), repeatable inspections, or weather-driven outages, areas where guided diagnostics and automated documentation pay back quickly. Prove the KPI lift (MTBF, MTTR, first-time fix rate, SAIDI/SAIFI, overtime hours), then expand.
A Practical Adoption Path
- Pick 2–3 “nail it” use cases with measurable pain (unplanned downtime, storm restoration times, inspection backlogs). Establish before/after baselines.
- Integrate Resolve-level guidance where work happens; mobile tech apps, dispatcher consoles, EAM/field service, so recommendations become executed jobs, not PDF advice.
- Pilot humanoids in a controlled loop (e.g., night-shift inspection route) tied to enterprise workflows; capture exceptions and refine autonomy thresholds before scaling.
- Stand up lightweight governance: named process owners, model stewards, robot safety leads, and a change board to approve new automations and keep audit trails clean.
Proof points (callout)
- £8.4M projected annual savings at William Grant & Sons post-Resolve deployment.
- ~40% faster post-disaster power restoration with Resolve-assisted workflows.
- Safety, efficiency, and uptime gains from autonomous inspections + agentic AI (Boston Dynamics, IFS.ai).
- Enterprise grid modernization via IFS + Siemens from planning to field execution.
The Bottom Line
Industrial AI is finally aligning with the realities of heavy industry: aging assets, thin crews, harsh conditions, and zero-tolerance compliance. With Anthropic, IFS is putting multimodal AI in frontline hands; with Boston Dynamics and 1X, it’s bringing robots into the same command system that runs your enterprise; with Siemens, it’s connecting grid planning to real-time execution. For operators staring down skill gaps and uptime pressure, this is a path to higher resilience and output, without ripping out the core systems you rely on.
Greg brings more than two decades of enterprise operations, technology strategy, and large-scale transformation experience to Third Stage’s independent, technology-agnostic advisory model. He has led complex initiatives across ERP, cloud platforms, and emerging AI-driven operating models, helping organizations modernize their core systems, reimagine end-to-end processes, and turn digital investments into measurable business value.
As Chief Strategy Officer, Greg is responsible for Third Stage’s global strategy, revenue growth, and market positioning. He oversees business development, client relationship management, client satisfaction, the Third Stage Certification framework, and a growing network of global partnerships. Greg also plays a central role in shaping the firm’s thought leadership around the shift from traditional ERP to more flexible, AI-enabled Digital Enterprise Operations (DEO) architectures.
Greg is dedicated to reinforcing Third Stage’s mission: to provide truly independent guidance that prioritizes ERP and vendor neutrality, interoperability across the tech stack, and actionable intelligence—“I3”—so that clients can navigate today’s AI and cloud-driven landscape and successfully reach the Third Stage of their digital transformations.
