Why do companies in industrial automation & robotics need a clear AI strategy?

Identifying high-value use cases begins on two levels: operational problem understanding and economic impact. First, we conduct interviews with production, maintenance, quality and engineering to capture recurring pain points and manual workflows. This qualitative analysis is combined with data exploration — e.g. signal availability, failure frequency and cost per failure — so we can identify tangible levers.

In the next step we prioritize use cases across several dimensions: technical feasibility, implementation effort, potential financial benefit and risks to production or safety. A simple but effective lever can be predictive maintenance on a critical pump; a more complex use case would be adaptive robotic control on a takt line.

Our use-case discovery approach often includes workshops with 20+ departments to break down silos and reveal hidden potential. We create standardized templates for business cases that include not only ROI but also implementation time, data gaps and compliance checks. This produces prioritized backlogs that can immediately be converted into PoCs.

Practically, it makes sense to start with one or two quick-win pilots that deliver a proof in days to a few weeks. Our AI PoC offering is built exactly for this purpose: rapid proof of technical feasibility and clear inputs for roadmap planning.

Edge AI is often a prerequisite in industrial automation, not a nice-to-have: latency, bandwidth constraints and security requirements make local inference necessary. Integration starts with an inventory of the control architecture: which PLCs, industrial PCs or gateways are present, and which real-time interfaces do they support?

Technically, we rely on compact, deterministic inference pipelines: quantized models, optimized runtime libraries and real-time scheduling. It is important to define clear interfaces to PLCs — for example via OPC UA, Profinet or native IO modules — so that AI outputs are reported back to actuators or control systems deterministically and on defined cycles.

Edge solutions also need operationalization: update processes, security certificates and monitoring are essential. We recommend a canary-release pattern where models initially run on non-critical cells before a full rollout. In parallel we build telemetry for performance metrics and drift detection.

On the organizational level, close collaboration between automation engineers, IT/OT teams and data scientists is decisive. Only then do robust edge architectures emerge that meet safety requirements while delivering productive value.

Functional safety combined with AI requires a two-pronged approach: technical measures to reduce risk and organizational measures for governance. Technically, ML modules must be designed with deterministic fallbacks: if a model is not validated or uncertainty is high, the system must fall back to a safe state.

We implement monitoring for model confidence, drift detection and health checks that continuously verify whether models operate within validated operating ranges. For safety-relevant paths, classic certifiable control logic remains the primary control instance; AI can be used complementarily for diagnosis or recommendation, not as the sole safety barrier unless appropriate certifications are achievable.

Organizationally, our AI Governance Framework defines roles, responsibilities and review processes, including versioning, test protocols and audit trails. These processes simplify collaboration with certification bodies and provide traceability for approvals and internal audits.

Finally, compliance is not a one-off project: regular re-validations, documented training data pools and change management are required to meet safety and compliance requirements in the long term.

A robust data architecture separates raw data ingestion, preparation and the model lifecycle clearly. In production this starts with edge collectors that capture sensor data with timestamps and context (machine, workpiece, shift). These raw data are locally preprocessed and selectively forwarded to central data stores for further analysis.

An important element is a metadata schema that represents context: device types, calibration states, control software versions and process parameters. Without consistent metadata, models remain vulnerable to drift and misinterpretation. We recommend a data governance setup with clear ownership rules and data quality metrics.

For modelling, feature stores and versioned training datasets are recommended so experiments are reproducible and models can be retrained later. Data pipelines should also include automated tests and anomaly checks so poor training data are detected early.

When data protection or IP protection are a concern, hybrid architectures and data-mesh concepts support decentralized data usage with central governance. This way valuable production knowledge remains usable without increasing security or compliance risks.

Engineering copilots aim to support engineers in parameterization, fault diagnosis and documentation. Integration starts with identifying repetitive knowledge tasks: frequent error messages, standard troubleshooting or commissioning checklists are particularly suitable for assistance systems.

Technically, copilots are offered as microservices that are contextually embedded into existing tools — e.g. in MES, PLM or ticketing systems. It is important to connect them to corporate knowledge bases and log data so the assistant can provide concrete, site-specific recommendations instead of generic advice.

Security and compliance aspects are central: copilots must have role-based access and changes must be traceably documented. We also implement feedback loops that allow engineers to rate the quality of recommendations so the system continuously improves.

Organizationally, introducing copilots requires accompanying training and adaptation of SOPs. A successful rollout starts with clear KPIs (e.g. faster fault resolution, reduced setup times) and iterative improvements based on real usage.

ROI depends heavily on the use case: predictive maintenance or visual inspection often deliver measurable savings in the short term (3–12 months), while deep autonomous control solutions require longer development and validation cycles (12–36 months). Prioritization is crucial: quick wins frequently finance the more expensive strategic initiatives.

Timing-wise a staged approach makes sense: after a 4–8 week scoping and readiness assessment, PoC phases typically last 1–3 months and deliver technical feasibility and initial KPI improvements. If successful, pilot and rollout phases follow, which can take 3–12 months depending on scope.

From a business perspective, business cases should include not only direct cost savings but also improved OEE, shorter setup times and reduced failure risk. We model conservative and optimistic scenarios to enable robust investment decisions.

Our experience shows that with a clear MVP focus and strong governance structures, many companies achieve substantial effects within a year. For rapid validation we deliberately use our AI PoC offering, which provides technical proof at limited cost.

An AI Readiness Assessment evaluates data availability, infrastructure, skillsets and governance. We examine sensor landscapes, data storage, network topology, edge and cloud components as well as organizational readiness for AI projects. The goal is to uncover concrete data gaps and technical risks.

Practically, we run workshops with production, OT, IT and operations, analyze historical logs and measure data quality. The result is a priority catalog: which machines or processes are most suitable, which infrastructure updates are required and which organizational measures should come first.

On the technical side we recommend concrete measures like edge gateways, time synchronization, labeling strategies and feature-store setup. On the governance side we define roles, testing standards and an initial model risk register so pilots can be conducted safely and transparently.

The assessment delivers a roadmap with estimated efforts and business cases — a solid basis to invest deliberately in PoCs (e.g. our €9,900 AI PoC offering) and subsequently in scalable solutions.