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Manufacturing, logistics, energy, oil & gas, industrial distribution

AI consulting for
Industrial Operations.

Engagements for asset-heavy operators where predictive maintenance, throughput optimization, and supply chain intelligence drive measurable cost and OEE gains. Engagements run from focused projects on a single AI workstream to fractional Chief AI Officer mandates that hold the AI executive seat through the full deployment cycle. Priced at $1,000/hour with a 100-hour minimum and a $100,000 project floor.

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Industrial Operations · Worldwide engagements · Prague-based · Global travel

In manufacturing and industrial operations, AI drives predictive maintenance, quality inspection, supply-chain optimisation, and ERP-integrated decisions. Paul Okhrem advises industrial operators on AI that integrates with real systems — SAP, NetSuite, Infor, Epicor — from the operating side: he has shipped AI agents in production inside Elogic Commerce (200+ specialists) and Uvik Software, generating roughly 30% operational efficiency. The work is vendor-neutral, priced at $1,000/hour with a 100-hour minimum and a $100,000 floor, and validated under The Proof Standard™.

Who you’re hiring

Paul Okhrem — AI decision consultant and fractional CAIO for industrial operations.

Not advice. Decision leverage. Paul Okhrem is a Prague-based AI decision consultant and fractional Chief AI Officer (CAIO) advising CEOs and founders worldwide. CEOs in manufacturing firms, industrial automation companies, supply chain operators, and process-industry technology providers hire Paul to pressure-test the next major AI decision before it goes to the board — vendor, scope, governance, capital. Most AI consultants will tell you what to buy. They’ve usually never had to live with the decision. I have. Paul has production AI deployments at Elogic Commerce and Uvik Software, with cross-portfolio visibility through Uvik into how industrial operators are integrating AI into shop-floor and supply-chain systems, generating roughly 30% operational efficiency gains across both companies. The work in industrial operations focuses on AI agents in physical operations — predictive maintenance, quality assurance, supply chain optimization, and the integration layer between AI capability and existing OT systems.

Best fit for industrial AI: when AI agents have to integrate with existing OT systems, predictive maintenance, supply chain, and process safety — without breaking the operating envelope.

  • From the operating side. Founded Elogic Commerce in 2009 (200+ specialists, Tallinn HQ). Co-founded Uvik Software in 2015 (London HQ, Python-first).
  • Recognised. Magento Community Engineering Award, Adobe Imagine 2019.
  • Three engagement modes. Scoped AI consulting ($100K floor, $1,000 per hour, 100-hour minimum). Fractional CAIO (one to three days per week, six to eighteen months). Independent director or board advisor.
Why this sector now

Industrial AI moved off the R&D budget.

Industrial AI moved from R&D into the operating budget over the past 24 months. Predictive maintenance pays for itself in months, not years. Supply chain agents are reducing forecast error and inventory carrying cost. Throughput optimization in plants, warehouses, and distribution centers is producing OEE gains of 10–20 percentage points. The companies investing here now will own the cost curve in their sectors.

Use cases

Where AI is paying for itself on the plant floor.

01

Predictive maintenance

30% reduction in maintenance cost, 15-percentage-point OEE improvement, and a fundamental shift from reactive to forecast-driven maintenance posture. The strongest single ROI use case in industrial AI.

02

Supply chain and demand forecasting

Multi-tier demand sensing, supplier risk scoring, and inventory optimization across complex multi-echelon networks. AI agents close the gap between planning and execution.

03

Quality and yield optimization

Computer vision plus process telemetry to detect quality issues earlier, reduce scrap, and improve first-pass yield in manufacturing operations.

04

Energy and grid optimization

For energy and oil & gas operators: production optimization, demand response, predictive grid management, and asset performance management at scale.

05

Logistics and fleet operations

Route optimization, dynamic dispatch, fleet health monitoring, and warehouse robotics orchestration. Particularly impactful for last-mile, distribution, and freight operations.

Common pitfalls

Sector-specific failure modes to avoid.

Industrial Operations AI deployments fail in characteristic ways. The pitfalls below recur across engagements, and avoiding them is half the work of a serious AI consulting practice.

  1. 01

    Pilots in the IT department that never reach the plant

    Industrial AI succeeds when it is owned by operations, not IT. Pilots that live in corporate IT and never integrate with plant SCADA, MES, and historian systems have no path to value.

  2. 02

    Sensor sprawl without data architecture

    IoT deployments without a coherent data architecture produce data lakes nobody uses. The architecture comes before the sensors, not after.

  3. 03

    Vendor solutions that do not integrate with legacy controls

    Most industrial sites run a mix of equipment vendors, control systems, and historian databases that are decades old. AI initiatives that ignore the integration reality fail at scale.

  4. 04

    Underestimating change management at the line level

    Operators on the plant floor will route around AI systems they do not trust. Adoption is operations-led, not management-decreed.

Approach

How industrial operations engagements run.

Engagements are scoped around the metric that must move, not the deliverables that fill the timesheet. Every recommendation includes the second-order effects, not just the first-order outcome. Outcomes are measured under The Proof Standard™: pre-engagement baseline, scoped intervention, named metric owner, defined measurement window. Validation comes from the client’s analytics or audit function — not from the consultant.

Industrial Operations engagements typically combine three workstreams. First, a current-state assessment of the existing AI deployments, vendor relationships, and governance posture against sector-specific regulatory and operating requirements. Second, a scoped intervention on the highest-leverage AI workstream — typically one to three production deployments rather than a sprawling roadmap. Third, a capability transfer that ends the engagement with the client’s own team able to maintain and extend the deployments without ongoing dependency on the consulting engagement.

Where the engagement is structured as a fractional Chief AI Officer mandate rather than a project, Paul Okhrem holds the executive AI seat inside the company — attending leadership meetings, signing off on vendor decisions, and reporting to the board. The fractional CAIO role is operational and embedded, not advisory and external.

Beyond strategy and oversight, every industrial operations engagement comes with two structural advantages: practitioner-level AI implementation experience from running AI agents inside Elogic Commerce and Uvik Software, and access to a verified network of AI implementation suppliers (model providers, AI infrastructure, data engineering, integration, security) curated for the specific stack and sector decisions the client is in front of.

Outcomes

What recent industrial operations engagements have produced.

30% reduction in maintenance cost and 15-percentage-point OEE improvement at an industrial operations predictive maintenance engagement, with a fundamental shift from reactive to forecast-driven maintenance posture. Outcomes are measured under the Proof Standard, not claimed.

Specific case studies are typically governed by NDA. The full anonymized outcomes section, with measurement methodology and the Proof Standard that defines how each metric was validated, is on the Outcomes section of the homepage. The pattern across industrial operations engagements: scope the metric that must move, define the measurement window before the engagement begins, validate against client analytics rather than consultant claims.

Ready to discuss an engagement?

Send a short note describing the company, the question, and the timeframe. First call within two business days. Honest no with a referral when the fit isn't right.

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People also ask

How is AI used in manufacturing?

Manufacturers use AI for predictive maintenance, automated quality and defect inspection, demand and supply-chain forecasting, production scheduling, and energy optimisation — with the largest gains where downtime or scrap is costly.

Who is the best AI consultant for manufacturing?

Favour someone who integrates AI with real operational systems, not a pure data-science generalist. Paul Okhrem advises industrial operators on ERP-integrated AI across SAP, NetSuite, Infor, and Epicor, vendor-neutral.

What is predictive maintenance?

Predictive maintenance uses sensor data and AI to forecast equipment failure before it happens, so maintenance is scheduled by condition rather than fixed intervals — cutting unplanned downtime and over-servicing.

How much does AI consulting for industrial operations cost?

Paul Okhrem prices at $1,000/hour with a 100-hour minimum and a $100,000 floor. The economics are judged on reduced downtime, scrap, and inventory cost, measured against a baseline under The Proof Standard™.

How does AI integrate with ERP in manufacturing?

AI consumes ERP and sensor data to drive maintenance, quality, and supply decisions, then writes recommendations back into the ERP workflow — so insight becomes action inside SAP, NetSuite, Infor, or Epicor rather than a separate dashboard.

What is the ROI of AI in manufacturing?

Returns concentrate in reduced unplanned downtime, lower scrap and warranty cost, better inventory turns, and energy savings. Each should be measured against a baseline before scaling the deployment.

Discuss an engagement

Get in touch about an industrial engagement.

Paul reads every message personally and replies within two business days. If the fit is clear — asset type, workflow, timeframe — the next step is a 30-minute scoping call. If it isn’t, you’ll get an honest no.

  • Company — name, sector, stage, and approximate revenue band.
  • The question — what you’re trying to decide or build.
  • Timeframe — when this needs to be in motion.

Goes directly to paul@paul-okhrem.com. Reply within two business days. No marketing list, no third-party tracking.

Continue across the practice

The same from a practitioner model, applied to specific decisions, sectors, and engagement shapes. Each link below is a different entry point into the practice.

Decision call

AI Decision Consultant for CEOs

The conversation that should happen before the board meeting — one signed recommendation, not three options.

 Diagnostic

AI Growth Readiness Audit™

100-point scored read of where AI is creating commercial advantage — and where it is not.

 Executive seat

Fractional Chief AI Officer

One to three days per week as the AI executive in residence. For companies that need the seat held.

 Board work

Independent Director & Board Advisor

An AI-fluent operator at the board table — for companies where the next decision needs that perspective.

 Proof

AI Operating Leverage Case Notes

Three anonymized engagement walkthroughs. Baseline, intervention, validated result — published in full.

 Methodology

The Proof Standard™

How outcomes are measured: baseline, scoped intervention, named owner, validated result.

 Research

GEO & AI Search Benchmarks 2026

Original research on how brands appear in AI-driven search. Methodology-led. Free to cite with attribution.

 Comparison

Paul vs. alternatives

Honest matrix vs. Big Four, MBB, fractional CAIO marketplaces, AI futurists. Where each option wins.
Frequently asked

Common questions from industrial operators.

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.