Industrial AI Executive Advisory

Disciplined artificial intelligence decision making for industrial systems

Independent, vendor neutral guidance for engineering and operations driven organisations navigating predictive models, causal analysis, machine learning, and generative AI.

For C suite leaders in engineering, production, energy, and supply chain driven organisations.

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Dr. Ivan M. Neri

Dr. Ivan M. Neri  ·  Austria & Denmark

PhD in supply chain management. Degrees in mechanical engineering, production engineering, technology management, and artificial intelligence. AI researcher and university lecturer in Vienna. Consulting and AI strategy implementation in Europe. I advise executives on where AI creates industrial leverage and where it does not. No vendor affiliations. No product to sell.

CEOs and senior executives in industrial and engineering organisations who are evaluating how artificial intelligence should, or should not, be applied within their operating model. Leaders under pressure to act on AI but seeking clarity before committing capital.

In industrial environments, the greater risk is not being left behind. The greater risk is misallocating capital under pressure.

Artificial intelligence is advancing rapidly. Predictive models, causal analysis, machine learning, and generative AI each serve different purposes. When these distinctions are not understood, organisations invest in initiatives that introduce operational complexity rather than leverage.

AI project failure rates

Generative AI pilots that fail to reach measurable impact 95%
AI projects that fail to reach production 80%
Non-AI technology projects that fail 40%

Sources: MIT NANDA, The GenAI Divide: State of AI in Business 2025  ·  RAND Corporation, The Root Causes of Failure for AI Projects, 2024  ·  S&P Global Market Intelligence, 2025

Most AI advisory is driven by vendors promoting their own solutions. My role is different. I provide a structured, independent assessment of where AI genuinely creates value in your specific operating context, and where it does not.

Structured guidance across the full AI spectrum

01 Evaluate AI investment decisions before capital is committed
02 Identify operational use cases aligned with existing systems
03 Distinguish between technological hype and engineering reality
04 Assess organisational readiness for implementation
05 Define structured and realistic implementation paths

Where this work has been applied

Examples of strategic advisory and implementation work across industrial and operational contexts.

Knowledge systems

Knowledge management systems

Designed AI driven systems to structure, classify, and retrieve knowledge across more than 100,000 internal documents, including competency mapping and lessons learned repositories for consulting and engineering organisations.

Predictive systems

Predictive and causal modelling

Developed predictive maintenance models for renewable energy systems including wind turbines and hydrogen electrolysers. Conducted causal analysis of equipment performance for optimisation in energy and pharmaceutical environments.

Executive advisory

Quarterly AI strategic updates

Ongoing advisory engagements with CEOs and their leadership teams, delivering quarterly on-site presentations that cover the latest AI developments, assess operational relevance, and provide hands-on guidance on what can realistically be implemented and how.

Operational safety

AI driven safety and monitoring systems

Implemented image-based AI systems for tracking container movements in shipping ports and detecting incidents in industrial environments, supporting safety management by identifying potential hazards before they become operational disruptions.

Selective, structured, and proportionate

1

Executive alignment call

A short conversation to assess whether structured AI advisory is relevant for your organisation.

20 minutes. No charge. Limited availability.

2

Industrial AI executive briefing

A focused advisory session with clear recommendations and a written executive summary.

60 minutes. Pre-session strategic intake. Written executive memo within 48 hours.

Dr. Ivan M. Neri

My academic background spans four countries: mechanical engineering in Mexico, an MBA in technology management and an MSc in production engineering in Germany, a PhD in supply chain management in Denmark, and advanced studies in artificial intelligence in Spain. I have worked in large scale industrial environments at Siemens, General Electric, and NXP Semiconductors, and have spent over two decades at the intersection of engineering, operations, and strategic decision making.

I lecture on artificial intelligence and conduct research at the AI Software and Safety Research Center at HCW University of Applied Sciences, Vienna. Through private advisory sessions and ongoing strategic updates, I support executives in making disciplined AI decisions aligned with their operating model.

My role is to reduce noise, not add to it. I do not represent vendors. I do not promote platforms. I help leaders see clearly what AI can do for their organisation, and what it cannot.

PhD Supply Chain Mgmt MSc Production Engineering MBA Technology Mgmt Mechanical Engineering AI Researcher University Lecturer Siemens · GE · NXP Vendor Neutral

If you are facing an AI decision, start here.

A short executive alignment conversation to determine whether structured advisory would create measurable value in your specific operating context.

Request an executive alignment call

No available slots? Connect directly on LinkedIn