AI consulting with a sharper answer than the last — for Manufacturing

Manufacturing in DACH and Europe more broadly is dominated by mid-market specialists — the Hidden Champions, the Mittelstand machine builders, the contract manufacturers, the Tier-1 and Tier-2 automotive suppliers. These businesses are extraordinarily good at their physical product and consistently undersupplied on software. ERPs are old (SAP ECC, Navision, ProAlpha), MES systems are partial or absent, customer-facing tooling barely exists, and CRMs are spreadsheets in 60% of cases. The decision-makers are pragmatic and ROI-driven — a manufacturer will sign for a piece of software the day they can see what it will save on the shop floor, and not before. Long-term partnerships matter; vendor-hopping is rare.

• ▸Start from where ROI is most measurable — typically quality, demand forecasting, or service-ticket triage. Avoid moonshot use cases first.
• ▸Workforce impact has to be addressed honestly — manufacturing workforces are unionised in much of Europe; AI initiatives without a workforce-conversation fail politically.
• ▸CSRD reporting is the unexpected near-term lever — many manufacturers can use AI to compress the data-collection burden of sustainability reporting.
• ▸Data maturity is the gating factor — most manufacturing AI strategies wisely begin with 4–8 weeks of data engineering investment.
• ▸Map the dealer/distributor network into the AI roadmap — dealer-side AI (lead routing, RFQ assistance) is often higher-ROI than internal AI.
• ▸IP and trade-secret containment — when we use external LLMs we configure for no-training, EU residency, and clear data flows; this is non-negotiable for mid-market manufacturers with proprietary process know-how.

For a Mittelstand industrial-equipment manufacturer, our AI strategy engagement surfaced a 14M EUR annualised opportunity across demand forecasting, after-sales service automation, and CSRD reporting compression — with a 9-month phased plan the board approved end-to-end.