Why does mechanical and plant engineering in Dortmund need targeted AI enablement?

Initial technical results can typically be seen within 4–8 weeks if the engagement starts with a clearly defined PoC. We usually begin with a use case that has low integration hurdles and high measurability, such as an initial spare parts forecast or a service chatbot for common fault cases. These PoCs validate technical feasibility and simultaneously provide first usage data.

Running in parallel with the PoC is an enablement track: executive workshops, department bootcamps and AI-Builder sessions ensure knowledge is built and the right stakeholders are involved. The combination of a technical prototype and parallel training ensures that results are not only delivered but also used.

Measurable operational impacts — for example reduced downtime or increased service efficiency — typically require 3–9 months, because they require integration into operational processes, data quality assurance and organizational adjustments. The exact timeframe depends on data availability, legacy IT and governance structures.

Practical tip: focus on quick wins for motivation and simultaneously on scalable architecture. Small, visible successes increase acceptance and create the basis for larger investments.

A successful enablement program requires a bundle of strategic, domain and technical roles. At leadership level, C-level or director enablement is crucial to secure budgets and priorities — this is exactly where our executive workshops focus. Operationally you need product owners or use-case owners who take responsibility for end-to-end implementation.

At the department level, domain experts from service, production, procurement and quality are the key knowledge providers. These colleagues make data interpretation and validation of model outputs possible. Our department bootcamps target these groups directly and create playbooks for daily use.

Technically you need data engineers who ensure data quality and pipelines, as well as developers or low-code engineers who integrate solutions. The AI Builder Track is specifically designed to transform domain experts into slightly technical creator roles so departments can build their own automations faster.

Not to be underestimated is the role of a change manager or community lead: someone who builds internal AI communities of practice, collects knowledge and makes successes visible. Without this role, learning initiatives often wither after the first project.

Governance, security & compliance are integral parts of our enablement measures. From the start we define rules for data access, model audits and responsibilities. In our trainings we show what a governance framework looks like in practice: who approves models, who reviews datasets and how are changes documented?

Technically we recommend clear data contracts, model versioning and audit logs for decisions based on AI. Our AI governance modules convey these practices in a hands-on way, not as abstract guidelines but as part of daily work, for example in service and production processes.

Data protection and industrial security requirements vary by use case. We work with your data protection officers and IT security teams to design solutions that are both compliant and operationally usable. For example, in sensitive cases we recommend locally or hybrid-hosted models with encrypted data pipelines.

Practical measure: create a governance checklist for each use case (data sources, responsibilities, risk assessment, monitoring). This checklist is developed in our workshops and forms the basis for safe scaling.

Data preparation starts with a data discovery: what data exists, in which format, what is its quality and how often is it updated? For spare parts forecasting, sensor logs, machine histories, maintenance reports and supplier information are relevant. Many companies discover at this stage that the needed data exists but is scattered and heterogeneous.

The second step is cleaning and standardization: unify timestamps, uniquely identify identical components and address missing values. Our bootcamps teach techniques for feature prioritization and show how to build pragmatic ETL pipelines so models run reliably and reproducibly.

For enterprise knowledge systems there is an additional focus on taxonomies, metadata and semantic linking. Value arises not only from model training but from structured content: manuals, service reports and CAD documents must be enriched so that search queries and assistance systems deliver consistent answers.

Our approach combines technical measures (data engineering practices, annotation workflows) with organizational steps (ownership, documentation standards). This creates a data foundation suitable for both initial PoCs and long-term production solutions.

We travel regularly to Dortmund and conduct our trainings on-site — always coordinated with shifts and production cycles. Our modules are designed to cause minimal downtime: executive workshops are compact, department bootcamps run as half- or full-day sessions, and on-the-job coaching is integrated into daily work.

On-the-job coaching means we work together with your teams on real data and real problems: we accompany the first model runs, help interpret results and support embedding them into existing processes. This ensures the learned skills are operationalized immediately and the transfer from the seminar room to practice is secured.

In parallel we promote building an internal community of practice: regular meetings, joint retrospectives and an internal showroom format where teams share successes and learnings. These formats are crucial so insights scale and are not limited to individual experts.

Logistically we focus on a pragmatic approach: clear agendas, prepared data snippets and defined outcomes for each session. This maximizes the value of your on-site engagement and minimizes interruptions to operations.

Costs vary greatly depending on scope: a standardized AI PoC at Reruption, for example, has a clearly defined offering (€9,900) — it serves quick technical validation of a use case. Enablement programs with multiple workshops, bootcamps, on-the-job coaching and community building are tailored and offered modularly.

Our pricing combines fixed prices for defined modules (e.g., executive workshop, department bootcamp) with time-and-materials elements for longer coaching phases. This enables budget predictability while allowing flexibility for adjustments during the project.

What matters is the ratio of investment to expected benefit: we work with you on concrete KPIs and a business case calculation so that investments are aligned with measurable savings or revenue increases. Often the first projects pay off within a few months through reduced downtime or more efficient service operations.

If desired, we prepare a Dortmund-specific, transparent cost and service overview that lists travel expenses, workshop packages and follow-up coaching. This gives you full control over budget and expected outcomes.