What does an AI consultant for industrial operations actually do?
AI consulting for industrial operations covers five areas: where AI agents drive measurable cost and OEE improvement (predictive maintenance, throughput optimization, supply chain, quality), how to integrate AI with legacy plant systems (SCADA, MES, historian, ERP), how to manage change at the line level so operators actually adopt the AI tools, how to choose between vendor solutions and build engagements for asset-heavy operators, and how to architect data foundations before sensor sprawl produces unusable data lakes.
How is AI consulting for manufacturing different from generic AI consulting?
Industrial AI succeeds when it is owned by operations, not IT. Generic AI consulting tends to deliver pilots that live in corporate IT and never integrate with plant SCADA, MES, and historian systems — these pilots have no path to production value. Industrial-specialized AI consulting starts from the plant floor, the warehouse, the energy asset, the fleet, or the distribution center, and works backward to the data and architecture decisions. The integration with legacy controls is half the work.
Where does AI produce the clearest ROI in industrial operations?
Predictive maintenance is the strongest single use case in 2026: 30% reduction in maintenance cost, 15-percentage-point OEE improvement, and a fundamental shift from reactive to forecast-driven maintenance posture. Supply chain forecasting is meaningful but slower to deploy because it touches more systems. Quality and yield optimization is high-ROI for manufacturing but requires computer vision and process telemetry working together. Energy optimization is high-ROI for energy and oil & gas operators specifically.
How does AI integrate with legacy plant systems?
Most industrial sites run a mix of equipment vendors, control systems, and historian databases that are decades old. AI agents typically integrate at the historian layer (reading time-series data from systems like OSIsoft PI, AVEVA, GE Proficy) and the ERP layer (reading work orders, inventory state, and production schedules from SAP, Oracle, or Microsoft Dynamics). Modern AI architectures use these as data sources rather than trying to replace the underlying control systems.
How much does AI consulting cost for an industrial operator?
Paul Okhrem prices industrial AI consulting engagements at $1,000 per hour with a 100-hour minimum and a $100,000 project floor. Typical scope: 8–16 weeks for project work on a defined AI workstream (predictive maintenance rollout, supply chain forecasting, quality optimization), or 6–18 months for fractional Chief AI Officer engagements at multi-site industrial operators where AI strategy and governance need to be built across the enterprise.
Will AI replace plant operators, maintenance technicians, or planners?
No, but it changes their roles. Plant operators become exception handlers as routine monitoring shifts to AI agents. Maintenance technicians shift from reactive repair to forecast-driven scheduling — same skill base, different operating posture. Planners absorb more demand variability without proportional headcount growth. Operations leaders consistently report that AI agents free expert time for the work that requires judgment and physical intervention; routine monitoring no longer absorbs operator capacity.
How should an industrial operator evaluate AI vendors?
Three filters: integration with the existing control and historian stack (does the vendor support OSIsoft PI, AVEVA, GE Proficy, SAP, Oracle, Dynamics out of the box?), domain depth (does the vendor have real plant or asset knowledge, or are they a generalist with industrial messaging?), and operator-side support (will the vendor work with site operators during deployment, or only with the corporate IT team?). Most industrial AI vendor failures trace to weakness in one of these three areas.
What is the biggest reason AI projects fail in industrial operations?
Pilots that live in IT and never reach the plant. The corporate AI initiative builds an impressive pilot in a controlled environment, runs a steering committee for nine months, and never integrates with the SCADA, MES, and historian systems on the plant floor. The pilot becomes a slide deck. Industrial AI succeeds when it is operations-owned from day one, integrated with legacy systems early, and adopted at the line level before scaling.
Does Paul Okhrem work with manufacturing, logistics, energy, and oil & gas operators?
Yes, across all four. Manufacturing engagements are the most common — predictive maintenance, quality, and supply chain across discrete and process manufacturing operators. Logistics engagements cover route optimization, fleet operations, and warehouse robotics orchestration. Energy and oil & gas engagements focus on asset performance management, predictive grid management, and production optimization. The first call covers the specific operating context.
Where is Paul Okhrem based and does he travel to plants?
Paul is based in Prague and travels for industrial engagements. Industrial AI consulting requires plant visits — the work cannot be done entirely remote because the operational reality of a manufacturing line, warehouse, or energy asset is not visible from a video call. Travel is included in the engagement model for executive sessions, plant tours, and major implementation milestones.
