Why do the chemical, pharmaceutical and process industries need an AI strategy?

Identifying high-value use cases begins with a focused discovery: we talk to stakeholders from lab, production, quality and EHS, analyze existing data sources and evaluate operational pain points. The goal is not to pursue every idea, but to find those use cases that deliver measurable impact in the short term and are scalable.

Methodically, we combine value scoring (cost-saving potential, efficiency gains, risk reduction) with feasibility analyses (data availability, integration effort, compliance risk). Typical candidates in the process industry are often lab documentation, predictive maintenance, anomaly detection for process variables and safety copilots — use cases that both reduce costs and increase safety.

Another important filter is regulation: GxP-relevant applications must include special tests, auditability and validation strategies from the start. We prioritize use cases so that quick value realization is linked to a clear path to regulatory compliance.

Finally, for selected use cases we create a short technical validation (feasibility check) that describes model options, required training data and initial success criteria. Only projects with clear KPIs and a realistic data basis make it to the pilot phase.

Regulatory requirements are not a side issue; they determine architecture and process design. Our approach starts with a compliance map: which data is GxP-relevant? Which decisions require audit trails? Which validation standards apply? Only then do we design models and pipelines.

Technically, we implement versioned data pipelines, traceable model training runs and documented test suites. Models are validated with clear acceptance criteria, including performance monitoring in production. All steps are documented to support regulatory inspections.

Additionally, we build an AI governance framework that defines roles, responsibilities and review processes. Change control, model rollback strategies and regular risk assessments are part of the standard processes — this reduces audit risk and increases operational safety.

Finally, we advise on the organizational setup: who at the plant is the owner for model reviews? How are SOPs adapted? We support the creation of GxP-compliant SOP templates for AI-supported processes so regulatory requirements can be implemented operationally.

Security and data privacy are central in laboratory and process environments. We start with a risk analysis: which data is sensitive, where does data exchange occur and which systems need special hardening? Based on that we define access concepts, encryption standards and protocols for data transfers.

On the technical level we rely on segmented data lakes, role-based access control and encrypted storage both in transit and at rest. For models we apply principles like model encryption, access logging and strict key management processes to prevent unauthorized use.

We also recommend using secure, locally hosted models (on-prem or in certified VPCs) for GxP-relevant applications to minimize data leaving the premises. For use cases that require external models or LLMs, we define clear data masking and PII-filtering layers.

Security is operationalized through regular penetration tests, incident response plans and governance meetings. Security is not a one-off project but an ongoing operational focus in our roadmap and governance work.

The time to first measurable results depends heavily on the use case and the data situation. For well-defined documentation or knowledge search use cases, clients often see significant improvements within 8–12 weeks: reduced search times, less duplicate work and immediately measurable efficiency gains.

For process optimizations or safety copilots we usually need 3–6 months to develop robust models, test them under real conditions and implement initial operational adjustments. Fully integrated, GxP-compliant deployments can take 6–18 months, depending on regulatory effort and system integration.

Typical KPIs are: reduction of search and reporting times in the lab, decrease in process failures, number of prevented safety incidents, prediction accuracy and total cost of ownership (TCO) per use case. We define these KPIs in the pilot phase together with stakeholders and measure them continuously.

It is important that pilots are built to be scalable: success criteria must be not only technical but also economic — we model business cases that transparently reflect savings, efficiency gains and qualitative benefits.

In many cases the basic prerequisites are: consistent identifiers for equipment and batches, accessible histories from SCADA/PLC systems, structured lab data (LIMS) and a minimal data lake or data hub. Without these fundamentals AI performance remains limited.

In addition we recommend standardized interfaces (APIs), clear data modeling and a logging system for process and production data. For GxP-compliant scenarios, validated data recording processes and traceable research notebooks are also useful.

In regional clusters like the BW chemical cluster, network integration and edge computing are often relevant: many plants require low-latency analyses directly on site. We advise whether an on-prem or hybrid architecture is sensible, and define requirements for compute, storage and network infrastructure.

Finally, organizational readiness is decisive: appoint data stewards, secure operational access to subject-matter experts and create a small interdisciplinary team of process engineers, data engineers and compliance representatives to drive projects forward.

Change management is an integral part of our roadmaps. We start early with stakeholder interviews to manage expectations and identify champions. These champions are actively involved in pilot stations and later serve as multipliers in the plant.

Our adoption strategy includes practical training, hands-on workshops and supporting documentation. Instead of explaining abstract models, we show concrete workflows: how the safety copilot complements a shift instruction or how the knowledge search answers common lab questions in minutes.

We measure adoption through usage metrics, user feedback and qualitative interviews. Insights feed directly into iteration cycles so the system is perceived not as a foreign object but as a useful tool. Change is incremental: small, visible successes build trust for larger transformations.

In the long term we support the transition to operations with co-preneur teams that ensure an orderly handover: training the operations organization, defining support levels and a clear plan for further development and scaling.