How do we empower Automotive OEMs & Tier‑1 suppliers with AI‑enablement to make engineering, quality and the supply chain resilient?

In the automotive sector, safety, traceability and integration into long validation cycles are central requirements. Unlike pure software companies, AI solutions here must be compatible with standards like ISO 26262 and integrate into existing CAE/PLM workflows. This influences training topics: in addition to prompting and model knowledge, we therefore teach traceability, data provenance and validation strategies.

Another difference is the heterogeneity of data: CAD models, test bench log data, production sensors and supplier documents exist in different formats. Enablement programs must therefore practically show how to connect data sources, perform gentle transformations and make them usable for AI workflows.

The stakeholder landscape in automotive is complex — OEMs, Tier‑1s, suppliers, test labs and regulators. Trainings must therefore map communication paths and decision processes so AI initiatives do not fail at organizational boundaries. We explicitly train cross‑functional collaboration with scenarios and role plays.

Finally, expectations are high: AI projects are expected to deliver both innovation advantage and robust, reproducible results. Our enablement curricula are structured to achieve quick proofs‑of‑value while simultaneously building long‑term operational capabilities.

Executive Workshops focus on strategic prioritization: which AI use cases deliver the highest short‑term impact? We work with leaders on decision criteria, KPI definitions and roadmaps that combine technical feasibility, regulatory requirements and business value.

A core component is the risk and governance discussion. Leaders must understand which governance structures are necessary to operate models responsibly, what audit trails look like and which roles are needed for model reviews, data quality and compliance.

We demonstrate concrete industry examples — such as chatbot automation for HR or predictive quality prototypes — and derive operational decision options from them. The workshop ends with a prioritized list of pilot projects, budget frameworks and responsibilities.

Important is the transfer into the organization: we provide templates for investment cases, executive reporting and a rollable roadmap so what is decided at board level is translated into measurable projects.

Department Bootcamps are practically oriented and work with real data and concrete problems from the department. In the Engineering Bootcamp we focus on copilot workflows, code and script generation, and integration into CAD/PLM pipelines. Participants learn how to structure prompts, evaluate results and embed them into existing toolchains.

For quality teams we teach methods for anomaly detection, root‑cause analysis and the operationalization of predictive models in the manufacturing environment. Key topics are model validation, threshold setting and implementing alerts in production systems.

In the Supply Chain Bootcamp we cover forecasting, scenario simulation and resilience strategies. A practical part shows how to integrate supplier data, quantify failure probabilities and build decision support for buy/make/delay decisions.

All bootcamps include an integration section: how are results versioned, how is the hand‑off to MLOps/IT handled, and which KPIs are tracked so the department can iterate independently?

The AI Builder Track is aimed at professionals without deep ML backgrounds who are expected to actively create or maintain AI‑driven solutions. The goal is to turn them from pure users into productive 'creator‑operators' who can refine prompts, build simple pipelines and evaluate model outputs.

Contents include prompting techniques, data preprocessing, evaluation metrics, and basic principles of model architectures and MLOps. The focus is on hands‑on exercises with company‑relevant datasets and on creating reusable prompt templates.

Participants also learn how to collaborate with IT and data teams to productionize prototypes. This reduces dependence on specialized data‑science teams and increases the speed of implementing use cases.

The track is particularly suitable for developers, quality engineers, process managers and power users from business units who are expected to take responsibility for AI projects.

The most important lever is On‑the‑Job Coaching: trainers and engineers continue to work directly with teams after the seminar on real tasks. This ensures knowledge is applied and adapted immediately. We accompany first deployments, help with debugging and measure the relevant KPIs early to enable adjustments.

Second, we build internal structures: playbooks, enterprise prompting frameworks and communities of practice ensure knowledge does not remain in individuals. These elements make best practices repeatable and scalable.

Third, we define metrics for success measurement — e.g. time saved on documentation, reduction of test cycles, or decrease in scrap — and report regularly to stakeholders. This creates transparency and strengthens acceptance.

Finally, we support rollout plans and train‑the‑trainer formats so the company builds internal capacity to sustain enablement long term.

Governance is an integral part of every enablement path. We start with role and responsibility definitions: who reviews models, who confirms data provenance, and who is responsible for lifecycle management? This clarity is anchored in trainings and documented in playbooks.

Practical tools include audit checklists, model cards and test suites that we develop together with teams. In workshops we train review processes, bias checks and robustness tests so models are verifiable before going live.

Regulatory requirements and safety standards (e.g. functional safety) are worked through in scenarios. Participants learn how to document AI outputs to support audits and certifications — a must in automotive contexts.

Finally, we implement governance dashboards and reporting templates that give compliance owners and business stakeholders clear insights into model performance, risks and mitigation measures.