We help Swiss and European mid-sized manufacturers build the literacy, governance, and operating model to turn AI into structural advantage — aligned with the workforce, not imposed on it.
Manufacturing is the sector where AI productivity gains are most concrete and most measurable — and most unevenly delivered.
Research by the World Economic Forum and McKinsey (2023) found that "lighthouse" manufacturers deploying AI and advanced analytics at scale achieve productivity improvements of 10–30% in targeted processes, along with quality gains, reduced energy use, and faster new-product ramp-up. The applications are well-established: predictive maintenance, quality inspection, demand forecasting, supply chain optimisation, yield improvement, and increasingly AI-assisted product design.
The challenge is not whether AI works in manufacturing. It is whether a given manufacturer has the literacy, governance, operating model, and workforce engagement to capture the gains.
For Swiss and European mid-sized manufacturers, the opportunity is sharpened by a specific pressure: European industrial productivity has grown roughly half as fast as US productivity since 2019, per Eurostat and the 2024 Draghi Report. AI is one of the few levers capable of closing that gap — but only if adoption is done well.
Most mid-sized manufacturers have multiple disconnected AI pilots and no coherent view of which ones move operational KPIs. Boards and executive teams need shared frameworks for prioritising use cases against real margin impact, not against analyst hype.
AI in manufacturing intersects with worker safety, product safety, supply-chain due diligence, and increasingly cyber-resilience obligations. Governance needs to handle all of them without duplicating frameworks.
Swiss and European manufacturing operates with strong workforce representation — works councils, social partners, high union density in some sectors. ILO research (2023) shows that AI adoption projects that engage worker representation from design phase onwards have adoption rates roughly twice as high as top-down deployments. Ignoring this is not a cultural mistake — it is a strategic one.
When suppliers use AI, the manufacturer inherits governance risk and competitive risk. Most Boards have no visibility into this. The CSDDD and emerging supply-chain rules are making it material.
The productivity gains come from redesigning how work is done — not from layering AI on top of unchanged processes. Most manufacturers underinvest in this.
We build shared vocabulary across the Board, the CEO, the COO, heads of operations, and technical leadership — calibrated for people who think in throughput, yield, and OEE, not tech metaphors.
Operations, supply chain, quality, commercial, and HR each face different AI decisions. We build role-specific training pathways.
AI in production environments interacts with worker safety, quality assurance, and health and safety regulation. We design governance that fits this operational reality.
We help executives map where AI is used across suppliers and partners, identify governance risk, and build the oversight the CSDDD and related rules will require.
We work with executive teams to identify the AI applications that genuinely move throughput, yield, quality, maintenance cost, and energy consumption — the metrics that make the P&L difference.
We redesign the processes AI touches. Predictive maintenance changes maintenance planning. Quality inspection AI changes the line. AI demand forecasting changes S&OP. Redesigning the work is where the productivity gains live.
We work with executive teams and — where applicable — with works councils to co-design AI adoption strategies that succeed with the workforce, not around it. We build training pathways for operators, technicians, supervisors, and planners so AI augments their capability rather than replacing it.
In B2B manufacturing, AI can enhance customer experience — service reliability, lead times, forecasting accuracy, aftermarket offerings. We help executives identify where AI can strengthen customer relationships, not just cut internal cost.
Our approach is calibrated for companies that need AI to move operational KPIs and margin — not to demonstrate AI leadership to analysts.
horizontal obligations plus high-risk classifications where AI touches safety
Swiss SUVA, EU OSHA
for workforce and customer data
CSDDD and related obligations
cyber resilience for essential services (where manufacturing qualifies)
The organisations that absorb AI successfully combine strategic clarity, data discipline and organisational redesign.
AI-by-default is a forced adoption model that introduces ethical, financial, and governance risk faster than organizations can manage it.
Three hyperscaler outages disrupted millions of users in 2025. AI is amplifying both the potential — and the fragility — of the cloud.
Turn AI from scattered pilots into structural advantage — on the shop floor, across the supply chain, and with the workforce that runs the business.
