Industry & Manufacturing
Redesign how industrial companies engineer, plan, produce, maintain and serve — without compromising safety, quality or operational control.
Industrial companies are entering a new phase of AI adoption. Generative AI is moving into engineering and operations, industrial data is becoming usable in new ways, and digital twins, computer vision and physical AI are extending intelligence from the office to the factory floor.
But industrial transformation cannot be built around isolated copilots or disconnected pilots.
The opportunity is to determine where AI can improve decisions, reduce downtime, preserve technical knowledge and coordinate physical operations — while deterministic controls, engineering judgement and safety-critical authority remain firmly governed.
I help industrial and manufacturing organisations identify high-value AI opportunities, redesign workflows around AI and automation, and move from experimentation to scalable industrial capabilities.
Accelerate engineering without weakening technical rigour.
Connect demand, supply, capacity and constraints into better operating decisions.
Improve production precision, quality and worker support.
Reduce downtime and preserve critical technical expertise.
Extend industrial intelligence beyond the factory.
In industry, value comes from combining multiple forms of intelligence:
The goal is not to force every industrial problem into a chatbot.
It is to design the right combination of AI, deterministic systems, automation and human expertise around each operational problem.
Industrial AI should connect the digital and physical operating model — not create another layer of software beside it.
AI accelerates analysis. Engineers remain responsible for the design.
AI explores the options. Operations leaders choose the trade-offs.
AI organises the evidence. Quality and engineering teams determine the cause.
AI preserves and mobilises knowledge. Technicians remain responsible for safe intervention.
Many industrial AI use cases fail because the information needed by the model is fragmented across engineering, operations, quality, maintenance, supply and commercial systems.
A more powerful foundation is a connected industrial knowledge layer — often described as a digital thread — linking the product, process and asset across its lifecycle.
This can connect:
The objective is not to centralise every system into a single platform.
It is to make the right context available to the right workflow, with clear ownership and controls.
Remove friction around expertise. Do not remove expertise from the operation.
Map engineering, planning, production, quality, maintenance, supply and service workflows.
Identify where decisions depend on fragmented data, undocumented expertise, manual coordination or slow feedback loops.
Evaluate each opportunity across:
Avoid optimising a single task when it creates a bottleneck elsewhere.
Prioritise workflows where AI can improve the entire operating flow across functions and systems.
Define:
Design how AI connects to PLM, ERP, MES, QMS, CMMS/EAM, WMS, SCADA or operational data where appropriate, data platforms, document repositories, CRM and APIs.
Delivery happens with internal engineering, operations and technology teams and the appropriate industrial specialists. My role is operating-model design, workflow architecture, prioritisation and transformation leadership.
Measure throughput, quality, downtime, schedule adherence, inventory, service performance, safety and workforce impact.
Scale through common data products, interfaces, controls and reusable workflow patterns — without forcing every site into an identical operating reality.
Industrial AI can influence physical operations. Governance must therefore extend beyond model accuracy.
A knowledge assistant, a scheduling recommendation and a system capable of changing machine behaviour should not have the same level of autonomy.
AI searches, summarises, drafts and prepares.
An engineer, technician or operator reviews the output before action.
AI proposes an action or scenario with supporting evidence and constraints.
An authorised person chooses, adjusts or rejects it.
AI and automation execute bounded actions only where controls, safety cases and deterministic protections are explicitly designed.
Exceptions and unsafe states must fail safe and escalate.
Industrial AI governance should address:
If an AI-enabled industrial process cannot fail safely, it is not ready to run autonomously.
AI Opportunity Assessment
Engineering, planning, manufacturing, quality, maintenance, supply and service workflows
AI, agent, optimisation and automation opportunities
By operational impact, feasibility, data readiness, safety and complexity
Initial AI-native industrial workflows and control boundaries
An actionable AI Transformation Roadmap
The result is not a list of tools. It is a clear view of where AI can improve the industrial operating model, which controls must remain deterministic and human-led, and what to do first.
Discuss an AI Opportunity AssessmentAI transformation for Industry in Switzerland
I work at the intersection of executive leadership, finance, operations, operating-model transformation, technology and artificial intelligence.
My approach starts with the physical and business workflow — not with a specific AI vendor.
I combine strategic and financial discipline shaped by executive leadership experience with a practical approach to redesigning complex operating systems.
I start with how the organisation should operate, not with the tool it should buy.
Industrial Products · Machinery & Equipment · Precision Manufacturing · Chemicals · Industrial Technology · Advanced Manufacturing
