How do you equip your automation team with AI capabilities for next-decade robotics manufacturing?

First results can become visible very quickly, often within a few weeks after the start of a focused program. For well-defined use cases such as visual quality inspection or anomaly detection in sensor streams, a PoC combined with accompanying bootcamps can deliver measurable improvements early on. What matters is that we jointly define concrete metrics — e.g. scrap rate, MTTR or detection accuracy — before starting any training.

Our methodology combines executive alignment to ensure goals and KPIs are set with hands-on bootcamps for operational teams. This ensures insights are not merely theoretical but immediately tested in pilot environments. Typically, technical implementations follow the workshops and we coach them in parallel so learning and implementation cycles remain short.

Long-term effects arise from establishing internal communities, playbooks and governance rules. These structures make successes scalable: a pilot project leads to a rollout, accompanied by continuous training and on-the-job coaching.

Practical example: in production projects we supported, visual inspection PoCs reduced scrap rates measurably in the first operating weeks. The combined effect of technology and employee enablement was decisive — not the technology alone.

Safety and compliance are integral parts of our enablement programs. In robotics and automation environments these are not downstream topics but core requirements: models must not bypass safety functions or trigger unpredictable actions. Our training combines technical measures (e.g. fail-safe strategies, deterministic test pipelines) with governance requirements (audit trails, model approvals).

Practically, this means we run safety workshops that map standards and certification requirements onto the concrete solution. In parallel we show how on-device checks, watchdogs and redundancy mechanisms can be integrated into models so that the machine remains stable even in the event of model errors.

Compliance training covers data management principles, pseudonymization, logging and change management. We teach responsible model update processes, including testing on staging, canary releases and rollback procedures so regulatory requirements are continuously met.

In summary: our training ensures teams not only build models but also transfer them into production-near systems in a compliant, auditable and safe manner.

One of the most common hurdles in industrial automation is the separation between OT (Operational Technology) and IT. Our enablement approach addresses this gap deliberately: in department bootcamps and joint workshops we bring both worlds to the table, define common goals and create linguistic and technical bridges.

Technically, we explain OT-specific requirements such as determinism, fieldbus topologies and time-sensitive networking and link these to IT principles like CI/CD, model management and cloud integration. Practical exercises demonstrate how models run on gateways and edge devices and how telemetry is securely transferred to central monitoring systems.

Organizationally, we support forming cross-functional teams with clear responsibilities and implementing interface processes (e.g. release processes for models). This creates sustainable collaborations that allow AI initiatives to be scaled safely and efficiently.

The result is a functioning process in which OT knowledge and IT methods complement — rather than oppose — each other.

The AI Builder Track is aimed at practitioners without a classic data-science background who want to design, test and operate solutions. Content ranges from machine learning fundamentals to practical prompting techniques and relevant know-how for edge deployments and model monitoring.

Concrete modules include: understanding data and feature engineering for sensor data, an introduction to computer vision for quality inspection, simple model workflows with no-/low-code tools, and hands-on exercises for integrating models into existing testing and control processes. Playbooks and templates that can be used immediately in pilot projects are an important component.

In the robotics context we also show how to use simulation environments to test models safely and how to establish feedback loops between field and model training. The goal is that participants can initiate concrete prototypes after the track and operationalize them together with IT teams.

The AI Builder Track is practice-oriented and aims to turn domain users into makers who can initiate and oversee projects — without having to become full-stack ML engineers.

Engineering Copilots can accelerate development processes by generating boilerplate code, suggesting test cases or assisting with debugging tasks. We support the conception, integration and training of such copilots specifically for embedded and robotics stacks.

Our approach combines executive alignment to set expectations with technical workshops for developers in which we define datasets, prompting strategies and security policies. Key aspects are data quality, context specificity and the inclusion of domain knowledge so the copilot acts accurately and reliably.

We also address integration into CI/CD pipelines, rights and role models, and monitoring for output quality. We train teams on how to evaluate, verify and transfer copilot outputs into production code, including auditability and rollback mechanisms.

In the end there is a copilot that complements — not replaces — real developer work and that is integrated into existing development processes safely and efficiently.

Internal AI Communities of Practice are the backbone of sustainable capability development: they enable continuous knowledge transfer, peer learning and the spread of best practices across departmental boundaries. In the automation industry they foster exchange on concrete implementation questions, safety checks and field experience.

We help build such communities by defining roles (moderators, mentors, subject matter experts), establishing learning formats (joint code reviews, lightning talks, brown-bag sessions) and providing technical resources (e.g. templates, playbooks and internal repositories). These structures ensure that once-built knowledge is not lost but continuously expanded.

It is also important to connect with lean and continuous-improvement initiatives on the shopfloor: communities are the channel to translate AI insights directly into process improvements. This creates a culture that allows experiments, controls risks and scales successes.

In short: Communities of Practice are the mechanism that turns enablement into institutional capability — and thus the prerequisite for long-term, self-sustaining success.