Why does manufacturing (metal, plastic, components) need a tailored AI strategy?

Identifying high-value use cases starts with two questions: where are the largest costs or quality losses occurring, and where are fast, reliable decisions missing at the shopfloor level? Our discovery phase scans production across 20+ departments, combining subjective inputs from specialists with quantitative data from MES, ERP and test rigs to produce a robust priority list.

We evaluate use cases along technical feasibility, economic impact and implementation effort. Technical factors include data availability, sensor types, latency requirements and integration points. Economic factors measure scrap reduction, OEE improvement, setup time reduction or savings in procurement. Only the interplay of these dimensions makes a use case truly high-value.

Practically, we work with short validation loops: a PoC delivers insights into data quality and baseline performance in days to weeks. Based on concrete KPIs we decide whether to scale a use case — this protects against bad investments and creates quick wins that generate internal support for larger projects.

Organizational context is also important: a high-impact use case that runs on critical infrastructure requires different governance and security measures. Our prioritization therefore also considers risk and compliance requirements so the selected use case not only delivers value but can be operated safely and sustainably.

Three data categories are particularly relevant for AI solutions in manufacturing: sensor data (vibration, acoustics, images), process and production data (MES, PLC logs, cycle times) and metadata (batch information, material master data, test reports). Success depends less on volume than on consistency and contextualization.

Data foundations are therefore the first technical lever: data modeling, time-series synchronization, data quality metrics and a pragmatic storage concept (edge buffering, targeted cloud pipelines) are necessary. We place special emphasis on simple, reproducible ETL pipelines and automatic validation routines so training data don't have to be painstakingly cleaned first.

Annotated examples are essential for image and sensor data. We rely on methods that work with manageable annotation effort — combined with active learning to generate labels efficiently. In many cases, an initial deployment in the pilot area is sufficient to systematically expand the data base and continuously improve models.

Data protection and access concepts are equally important: who is allowed to see production data, how long is data stored and how is separation between test and production data maintained? These questions are answered with a technical and organizational framework that we integrate into the roadmap.

The time to noticeable economic effect varies by use case: simple automations and quality checks can deliver better scrap rates or shorter inspection times within a few weeks, while complex predictive maintenance systems usually take months to produce stable forecasts. Typically, PoCs show first measurable results in 6–12 weeks.

We structure projects with an ROI focus: in prioritization we calculate conservatively from baselines and define clear KPI targets. Measures with short implementation time and high impact (e.g., inline visual inspection to reduce scrap) are placed early on the roadmap to generate short-term cash flow and increase acceptance within the company.

Economic success comes not only from direct savings but also from improved throughput times, reduced rework and fewer machine failures. These indirect effects are often substantial and are explicitly modeled and periodically validated in our business cases.

In the longer term (3–12 months), investments in data foundations and MLOps pay off as the cost-per-model-update decreases and new use cases can be scaled faster. The combination of quick pilot wins and strategic building of the technical base leads to sustainable, cumulative ROI.

Integration into OT environments requires caution: production availability, latency and security are top priorities. We clearly distinguish between read-only connections for data collection, edge inference for fast decisions and control interfaces that may only become active after tested approval processes.

Technically we rely on strict network segmentation, certified gateways and standardized protocols (OPC UA, MQTT). Edge modules run inference locally, minimizing latency and reducing data traffic. Interfaces to PLCs are generally implemented via read-only adapters or in conjunction with existing automation solutions to avoid the risk of unexpected interventions.

Another important aspect is the release and rollout procedure: models undergo checks in test environments, shadow modes and only then a controlled release into the productive control loop. We closely accompany these phases with automation and OT teams so that safety and compliance requirements are fully met.

Monitoring and fallback mechanisms are indispensable: model decisions are logged, performance drift is detected and automatic rollbacks or alerts are implemented. This keeps production safely controllable even if models technically fail or unexpected states occur.

A practical AI Governance Framework includes roles, processes and technical controls. Roles define who validates models, who takes data stewardship and who is responsible for operations. Processes govern model approval cycles, testing requirements and monitoring intervals. Technical controls ensure traceability, reproducibility and security.

Specifically in manufacturing, governance processes must include emergency plans, drift monitoring and test protocols for audits. Decisions that affect production or product quality should be versioned, documented and auditable. We also define clear SLAs for model performance and response times for alerts.

Data protection and IP protection are also part of governance. Production data often contains sensitive information about products or processes; therefore we define access concepts, pseudonymization steps and retention periods. In collaborations with suppliers, contractual data flows and usage restrictions are regulated.

Governance should be pragmatic: too much bureaucracy stifles innovation, too little control increases risk. Our approach is therefore modular: core requirements apply to all projects, additional controls are added project-specifically depending on risk and critical infrastructure.

AI changes job profiles but rarely replaces entire positions. Instead, the focus shifts: repetitive inspection and documentation tasks are automated, while work on higher-value tasks — analysis, process optimization, interventions on deviations — increases. This creates space for more productive, less monotonous activities.

For companies this means a concrete upskilling need: line staff need basic training to interpret AI-driven recommendations; maintenance staff must learn how models prioritize alerts; data owners must develop an understanding of data quality and annotation. We build change programs that deliver training content in a practical, role-based way.

Communication is crucial: employees must understand the purpose and limitations of AI solutions. Successful rollouts combine technical introduction with a clear value story: less rework, fewer disruptions, and support in complex decisions. This makes AI perceived as an enabler rather than a threat.

In the long term new roles also emerge: MLOps technicians, data stewards and production data analysts. For mid-sized companies we recommend concentric skill investments — a few internal experts plus external partner capacity — until the organization has built capabilities independently.

Costs vary widely with scope: an initial AI Readiness Assessment and use-case discovery are typically manageable and serve as a basis for decision-making. Proof-of-concepts for targeted applications can be implemented in the mid five-figure range, while company-wide platforms for data foundations and MLOps require higher upfront investments.

It is important to distinguish between one-time costs (assessments, architecture, initial model development) and ongoing costs (cloud/edge infrastructure, monitoring, model retraining, maintenance). Our roadmaps model both transparently and provide scenarios with conservative ROI forecasts so decision-makers can budget with confidence.

We recommend starting with pilot projects that quickly deliver measurable impact and thereby legitimize internal budget for expansion. Parallel investments in data foundations pay off across multiple use cases and strongly reduce the marginal cost of new projects.

In conclusion: budget planning should be iterative. We provide concrete budget paths in the AI strategy, including break-even scenarios and reserve positions for unforeseen integration efforts, ensuring financial transparency and controllability.