In many B2B factories, “digital transformation” has long been more of a boardroom buzzword than a true operational lever. Yet the situation is changing fast: shrinking margins, skills shortages, customer pressure on lead times and quality, and growing risks of supply chain disruption. The industrial players performing best are those that have structured their transformation around a unified data backbone, predictive AI, and process automation, enabling them to manage production, quality, and the supply chain almost in real time.
The new foundation: a unified industrial data platform (and the legacy challenge)
In many factories, data is fragmented: ERP on one side, MES on another, local Excel files, quality systems, maintenance, SCADA, and so on. The first building block is to create a unified data foundation: an industrial data lake or data warehouse fed by the ERP, MES, IoT sensors, the WMS, and the CMMS.
The view from the shop floor: building this foundation does not mean replacing your existing systems. The major challenge is coexistence. In a mechanical components factory, we saw a project stall because IT wanted to standardize everything. It was only by accepting to connect 20-year-old machines via dedicated IoT gateways, while keeping the legacy ERP, that the full “order → production → shipment” flow finally became visible in real time.
This foundation makes it possible to correlate process data (machine parameters, environmental conditions) with quality non-conformities and breakdowns. For a marketing or product decision-maker, this is not just an IT topic: it determines the ability to demonstrate gains (OEE, scrap rates, lead times) and to package the offering as a data-driven solution for customers.
L’IA prédictive : passer de la réaction à l’anticipation
Once the data is structured, AI use cases become tangible across three areas: maintenance, quality, and supply chain.
- Predictive quality: detecting process anomalies that lead to non-conformities, with automatic adjustment of parameters.
- Predictive quality: detecting process anomalies that lead to non-conformities, with automatic adjustment of parameters.
- Intelligent supply chain: AI-driven demand forecasting and digital twins to simulate planning scenarios.
For a solutions provider, the challenge is to position products not as “tools” but as levers to reduce downtime, scrap, and inventory, backed by quantified KPIs.
Why 70% of Industry 4.0 projects fail to deliver on their promise
It is time to be honest: most projects never achieve the expected business impact. Here are the classic mistakes we observe:
- The “data lake graveyard”: millions of data points collected but never used by the business because the project was driven by IT without a clear business owner.
- The “gadget AI”: predictive models that look impressive on paper but fail to integrate into an operator’s daily workflow, where there is no time to consult a complex dashboard.
- Feature-driven marketing: offerings sold as “platforms” without any measurable economic promise. If you cannot explain how many cents you save per unit produced, you are not really selling anything.Feature-driven marketing: offerings sold as “platforms” without any measurable economic promise. If you cannot explain how many cents you save per unit produced, you are not really selling anything.
Process automation: connecting the shop floor, systems, and the customer
Digital transformation becomes tangible through workflow automation. On the factory side, this means connected MES systems that automatically trigger production orders. On the B2B commercial side, it opens the door to digital self-service (customer portals, automated quotations) built on real-time data (availability, lead times).
For a product director, this enables new offering models: availability-based contracts, pay-per-use schemes, or remote monitoring services with SLAs based on actual data.
Marketing & product implications: from technical messaging to performance
For high-tech players (software, IoT, equipment), this transformation changes everything:
- Positioning: shifting from a “features” narrative (sensors, APIs) to a promise of factory performance and resilience.
- Product thinking: prioritizing integrations (ERP, MES, WMS) in the roadmap. A feature that is not tied to an industrial KPI is a feature that will not be sold.
- Human dimension: technology is meaningless without alignment between silos (IT vs Operations) and upskilling of teams. Your product must empower operators, not add another layer of constraint.
An actionable roadmap for decision-makers
- Clarify the data/AI narrative: how does your solution fit into the existing foundation, and which indicators (OEE, TRS, scrap rate) does it impact?
- Document two or three flagship use cases: with simple figures (x% less downtime) based on real proofs of concept.
- Equip the message for different stakeholders: plant management (performance), IT (security), finance (ROI).
The key question going forward
If your offering disappeared tomorrow, what would concretely stop working in your customer’s factory? Which industrial indicators would no longer be monitored, and what would be the immediate financial impact? If the answer is unclear, your offering is not yet the “digital backbone” it should be.
