How do you prepare your chemical, pharmaceutical and process company in Berlin for the future with AI enablement?
Innovators at these companies trust us
Why act now?
The chemical, pharmaceutical and process industries are under pressure: regulatory requirements, complex laboratory processes and increasing safety demands require fast, precise and traceable solutions. Without targeted enablement, AI remains an experiment — with risks to compliance, safety and efficiency. What is needed are practical trainings and governance, not just abstract strategies.
Why we have the local expertise
Although our headquarters are in Stuttgart, we regularly travel to Berlin and work on site with clients. This presence allows us to train executives in person, facilitate department workshops and work with teams on everyday tasks using real data and processes. We don't claim to have an office in Berlin — we come to you and work directly within your structures.
Berlin is Germany's tech capital and a breeding ground for AI talent, startups and interdisciplinary research. Through regular assignments in the city we have developed a fine sense for local partner networks, recruitment dynamics and the typical IT landscapes that Berlin companies use. This knowledge flows directly into our enablement programs: examples from regional tech stacks and real use cases make trainings more relevant and applicable.
Our references
Our experience with manufacturing and process organizations is supported by projects such as the collaboration with Eberspächer: there we applied AI methods to analyze and reduce noise sources in manufacturing processes — an example of how sensitive production data can be put to practical use. Such projects convey methodological knowledge that can be transferred to laboratory and process environments.
With STIHL we accompanied several projects, including saw training and ProTools, which demonstrate how product-specific training solutions and digital learning platforms can be scaled over longer periods. For the chemical and pharmaceutical industries these experiences are especially relevant because they show how to didactically prepare complex specialist processes and convert them into digital learning and assistance systems.
In addition, we have worked on technology and consulting projects for clients like BOSCH, TDK and FMG — from go-to-market for display technology to AI-supported document analysis. These references demonstrate our ability to build technically sophisticated solutions while ensuring organizational adoption.
About Reruption
Reruption stands for a different consulting approach: we don't just appear as external experts, but work like co-founders with responsible ownership. Our Co-Preneur approach combines strategic clarity, fast engineering sprints and entrepreneurial action — exactly what companies need to not only test AI but make it productive.
Our enablement programs are pragmatic: executive workshops set the strategic objective, bootcamps bring teams to an operational level, and on-the-job coaching ensures that new skills are applied and scaled in everyday work. In Berlin we bring these elements directly into your departments — on the shop floor, in the lab or in the executive suite.
Interested in an executive workshop in Berlin?
We travel to Berlin regularly and run on-site workshops that equip leaders with clear goals and an implementation plan. Contact us for an initial exchange about objectives, timelines and KPIs.
What our Clients say
Hans Dohrmann
CEO at internetstores GmbH 2018-2021
This is the most systematic and transparent go-to-market strategy I have ever seen regarding corporate startups.
Kai Blisch
Director Venture Development at STIHL, 2018-2022
Extremely valuable is Reruption's strong focus on users, their needs, and the critical questioning of requirements. ... and last but not least, the collaboration is a great pleasure.
Marco Pfeiffer
Head of Business Center Digital & Smart Products at Festool, 2022-
Reruption systematically evaluated a new business model with us: we were particularly impressed by the ability to present even complex issues in a comprehensible way.
AI enablement for chemical, pharmaceutical & process industries in Berlin — a deep dive
The introduction of AI in highly regulated and process-oriented industries requires more than technical pilots: it needs structured competency development, clear governance and adapted tools that safely complement existing workflows. This is not about buzzwords, but about concrete capabilities — from correct prompt construction to interpreting model statements in the laboratory routine.
Market analysis and local context
Berlin is an ecosystem full of talent, research institutions and growing tech companies. For chemical, pharmaceutical and process firms this means access to developers, data scientists and product managers who know modern AI workflows. At the same time, many traditional companies lack the experience to integrate these specialists effectively: recruitment is possible, but without structured enablement programs potential quickly dissipates.
The demand for safe, auditable AI is growing. Regulatory requirements (e.g. documentation of decisions, verifiability of assistance systems) are high in Germany and the EU. Companies in Berlin have the advantage of being able to draw on local research and partners at short notice — the disadvantage is that without clear training strategies compliance risks arise.
Specific use cases and their requirements
Four use cases dominate the discussion in chemical, pharmaceutical and process industries: laboratory process documentation, safety copilots, intelligent knowledge search and secure internal models. Each variant requires its own enablement set: lab staff need precise prompting and an understanding of error margins; operations require robust, low-latency assistance systems; compliance teams need traceability and audit trails.
Takeaway: an executive workshop alone is not enough. What matters is a staged program: leadership at the strategic level, bootcamps for departments, an AI Builder track for technically interested specialists and governance training for risk & compliance. Only in this way are sustainably anchored capabilities created.
Implementation approach: from workshops to on-the-job coaching
Our modules are coordinated with one another. Executive workshops create the agenda and sponsorship; department bootcamps translate strategy into concrete tasks; the AI Builder track empowers "citizen builders" to build prototypes; prompting frameworks and playbooks provide repeatable patterns; on-the-job coaching ensures transfer into daily work.
In Berlin specifically this means: we come on site, work with lab and production staff in real shifts, test prompting methods on real documentation systems and integrate governance checks into existing SOPs. This pragmatic, hands-on approach reduces adoption barriers and increases acceptance among users.
Success factors and common pitfalls
Successful enablement depends on clear success measurements: which KPIs change because of the training? Typical metrics are time savings in documentation, reduction of errors in processes, response times to incidents and compliance incident rates. Without such measures trainings remain subjective and hard to evaluate.
Common mistakes are: (1) overly technical content for non-technical audiences, (2) missing governance anchoring, (3) no connection between workshops and real tasks. We address these mistakes with tailored learning paths, practice-oriented exercises and clear playbooks that can be embedded directly into SOPs.
ROI, timeline and scaling expectations
The range of ROI is wide: quickly measurable effects are time savings in documentation and improved knowledge retrieval — here we often see returns within 3–6 months. Long-term effects, such as cultural change and scalable internal communities of practice, require 9–18 months and ongoing coaching cycles.
Our approach: short, measurable pilot phases (PoC/bootcamp) with clear KPIs, followed by rollout phases that rely on on-the-job coaching and community building. This ensures that initial successes are scaled and institutionally anchored.
Technology stack and integration
For secure internal models we recommend a combination of locally hosted models for sensitive data and cloud-based services for rapid iteration. Important is a clear separation of development and production environments, along with monitoring for drift and performance. Prompting frameworks and playbooks live in internal knowledge platforms that are connected to document management and LIMS systems.
Integration also means: interfaces to DMS, MES and LIMS, structured data pipelines and established authentication paths (SSO, roles). In our trainings we demonstrate concrete integration steps and provide code snippets that domain experts can use immediately.
Team requirements and roles
Successful recipes require diverse roles: a C-level sponsor, product owners for AI projects, data engineers, domain experts from lab & production, compliance owners and trainers for operational implementation. Our bootcamps are designed to bring these roles together — teams often discover new internal champions in the process.
It is important that enablement work is not a one-off action: internal communities of practice and regular coaching cycles ensure that knowledge is not lost and new insights are quickly disseminated.
Change management and cultural aspects
Technology is only one part: culture decides whether AI becomes a tool or just remains a tool. In Berlin, where startups are accustomed to rapid iteration, there is often an openness to experimentation. In traditional process environments skepticism is greater — here transparent success stories, hands-on sessions and the involvement of works councils build trust.
Our trainings integrate communication plans, pilot storytelling and governance workshops so that process owners, works councils and compliance teams are actively involved. This creates not only knowledge but also trust in the solutions.
Ready for a pilot bootcamp?
Start with a focused department bootcamp or an AI Builder track to quickly deliver prototypes and achieve measurable effects. We support planning, execution and transfer into everyday work.
Key industries in Berlin
Over the past two decades Berlin has evolved from a creative metropolis into a genuine technology hub. The city attracts founders, developers and investors and forms a dense network of startups, research institutions and established digital firms. This climate also changes the way industrial companies approach innovation: collaborations between research and practice become easier and practical innovations are implemented faster.
The tech & startup scene is the pulsating heart of Berlin. Companies from all over Germany and Europe look here for talent, new ways of thinking and platforms. For the chemical and pharmaceutical industries this opens up opportunities: research partnerships, access to modern AI methods and faster recruitment of technical specialists. Such connections are particularly valuable for data-intensive tasks like laboratory documentation or knowledge management.
FinTech and e-commerce have shaped the local infrastructure for scalable data products. Systems for analyzing large transaction data, real-time monitoring and automated decision processes are widespread in Berlin — patterns that can also be adapted to process environments. In particular, experience with secure data pipelines and strict compliance processes from fintechs is transferable to pharmaceutical companies.
The creative industries bring a different element: storytelling, UX design and the ability to make complex content accessible. For enablement programs this is crucial: trainings and playbooks that are not only technically correct but also understandable and appealing promote acceptance and sustainable learning.
At the same time, Berlin has a number of specialized research institutes and universities that are active in life sciences, chemistry and data science. Collaborations with these institutions enable access to the latest research findings, pilot projects and talent pools — an advantage for companies that want to strengthen their AI capabilities institutionally.
The challenge for industrial companies in Berlin is to systematically use these diverse resources. Many firms benefit from local know-how but fail in operationalization: the gap between prototype and productive use often remains open. This is exactly where structured enablement comes in: it creates the link between research, technology and daily production.
Another point is regulation and compliance. In Germany transparency and traceability of decisions are central — this is especially true in pharma and chemistry. Berlin companies that rely on AI therefore must not only experiment but also create audit-ready processes and documentation. Enablement programs that connect governance and practice are particularly in demand here.
Finally, Berlin's working culture shapes the speed of innovation: flexible teams, flat hierarchies and a high willingness to adopt new ways of working. For companies in the process industry this means: with the right training formats agile working methods and AI tools can be established more quickly. This combination of talent, infrastructure and cultural openness makes Berlin an ideal location to scale AI enablement.
Interested in an executive workshop in Berlin?
We travel to Berlin regularly and run on-site workshops that equip leaders with clear goals and an implementation plan. Contact us for an initial exchange about objectives, timelines and KPIs.
Important players in Berlin
Zalando started as an online shoe retailer and has grown into one of Europe's largest e-commerce players. Zalando stands for data-driven product development and scales machine learning applications in areas such as personalization and supply chain optimization. The technical depth and talent density at Zalando contribute to the local expertise and act as a magnet for data scientists who also look for solutions in industrial projects.
Delivery Hero has shaped the delivery economy in Berlin and demonstrates how to process complex logistics data in real time. The operational excellence and focus on scalable systems provide transferable knowledge for process industries that need real-time monitoring and rapid decision support — from inventory control to incident response.
N26 has driven banking disruption in Berlin and placed a strong emphasis on security, compliance and user centricity. The experiences from fintech compliance, secure engineering and transparent audit pipelines are also relevant for pharma and chemical companies that must meet strict regulatory requirements.
HelloFresh shows how data-driven supply chains and production planning work in urban environments. The optimization of supply chains and the automation of routine decisions are concepts that can be transferred to process industry setups, particularly when it comes to material flows and just-in-time operations.
Trade Republic stands for the democratization of financial services and has also built expertise in handling scalable platforms and regulatory requirements. The local presence of such scale-ups creates an environment in which data literacy and governance are combined — a prerequisite for safe AI adoption in regulated industries.
Besides these big names there is a dense landscape of startups, research labs and consultancies. These actors often work together in ecosystems where proofs-of-concept are created quickly and can be scaled. For industrial companies in Berlin this is an advantage: they can find partners who take on specific parts of enablement, from UX design to data engineering.
Universities and research institutions in Berlin contribute significantly to the talent pipeline. Collaborations between industry and research create bridges for internships, joint research projects and targeted further training programs. This is particularly useful for companies that want to address specific domains such as lab automation or process simulation.
Finally, the community culture in Berlin is a factor that favors enablement. Meetups, conferences and informal networks ensure that best practices are shared quickly. For companies this means: those who actively participate can learn faster, avoid mistakes and adapt successful patterns. We leverage this local dynamic in our programs by linking practical cases to local examples and initiating networks for long-term communities of practice.
Ready for a pilot bootcamp?
Start with a focused department bootcamp or an AI Builder track to quickly deliver prototypes and achieve measurable effects. We support planning, execution and transfer into everyday work.
Frequently Asked Questions
Initial, measurable results can often be seen within 6–12 weeks when the program is focused on a clearly defined use case — for example accelerating laboratory process documentation or introducing a knowledge search tool. In this time we typically deliver a prototype, conduct user tests and measure KPIs such as time savings or error reduction.
The key is focus: if leadership sets clear goals and frees a team for the pilot, a bootcamp plus on-the-job coaching can achieve significant improvements. In Berlin companies additionally benefit from access to local talent and partners that enable rapid iterations.
Longer-term effects — such as cultural changes, the formation of internal communities of practice or the establishment of governance processes — take 6–18 months. These phases include repeated trainings, community events and the consolidation of playbooks into SOPs. The real benefit arises when initial successes are scaled and institutionalized.
Practical advice: start with a clearly measurable pilot use case, set KPIs and immediately plan the steps to scale. This prevents early successes from remaining isolated and not being rolled out widely.
Compliance and auditability must be anchored in enablement programs from the outset. This starts with selecting suitable models and architectures (locally hosted vs. cloud), through strict data access policies to clear documentation standards for all model decisions. In workshops we explicitly train teams in setting up such audit trails.
We teach concrete techniques: structured logging mechanisms, versioning of models and data, as well as test suites for performance and bias. This includes playbooks on how to interpret outputs, which metadata must be captured and how decision paths can be documented traceably — essential in clinical settings or clinical development processes.
Another aspect is governance: clear roles (model owner, data steward, compliance officer) and approval processes prevent models from running unchecked in production systems. Our governance trainings are aimed at these roles and provide practical templates aligned with German and European regulations.
In Berlin, companies can additionally leverage local expertise from fintechs and scale-ups that have already established strict compliance processes. We support the knowledge transfer and adaptation of these patterns to the specific requirements of chemical and pharmaceutical sectors.
Employees in labs and on the shop floor need a mix of basic technical education, methodological understanding and practical application competence. Concretely this means: a fundamental understanding of AI concepts, the ability to use assistance tools and the competence to assess and question outputs.
Our bootcamps are designed to impart these skills in a practice-oriented way. Lab staff learn, for example, correct prompting techniques for searching experiment documentation, interpreting model uncertainties and how to detect measurement errors. Production workers practice with safety copilots to support quick decisions in case of deviations.
Also important is training in digital skills: working with electronic lab notebooks, understanding data quality and simple data annotation techniques. These skills enable teams to create high-quality training data for internal models and thus sustainably improve model quality.
A practical tip: Rely on
Acceptance is built through transparency, participation and visible benefits. Skepticism often arises from fears about job security or loss of control. A successful approach is to involve stakeholders early: works councils, team leaders and end users should participate in workshops and jointly work on real problem solutions.
In our programs we use practical demonstrations and small, safe pilots so teams can see how tools make their work easier — for example fewer documentation-related errors or faster information retrieval. These tangible successes are more convincing than abstract promises. At the same time we provide transparent rules for data usage and a clear separation between assistance functions and automated decisions.
Communication strategy is crucial: understandable case studies, clear benefits for each affected role and continuous feedback loops reduce fears. We assist in creating communication plans that explain benefits, risks and safeguards clearly and comprehensibly.
Long term, participation pays off: when teams help construct playbooks and prompting standards, solutions are not only accepted but actively improved. This creates ownership and increases the sustainability of AI initiatives.
Prompting frameworks and playbooks are the core of operational AI application: they translate technical knowledge into repeatable work instructions. In labs playbooks help to make consistent requests to knowledge systems; in production they serve as checklists for handling assisted decisions. Without such standards AI use remains heterogeneous and error-prone.
Our frameworks are practice-oriented: they contain templates for typical questions, rules for dealing with uncertainties, and escalation paths when a system does not provide a reliable answer. These artifacts are tested together in department bootcamps and linked to real SOPs.
Another advantage: playbooks are training material. New employees can learn based on real scenarios how to use AI tools correctly. This creates quick onboarding effects and a lower error rate when using new tools.
For Berlin companies it makes sense to build playbooks modularly and connect them to internal knowledge platforms. This creates living documents that can be continuously updated — a decisive factor for long-term scalability.
Communities of practice are the instrument to turn individual learning into organizational know-how. They don't emerge automatically but require active maintenance: regular meetings, shared success stories, mentoring formats and clear responsibilities. In our enablement programs we define structures for these communities and support the first 6–12 months operationally.
In practice we recommend a hybrid model: regular short brown-bag sessions combined with deeper working groups that work on concrete topics (e.g. prompt optimization for lab reports). Communities should also have light governance roles: a community lead, a tech facilitator and domain experts from the business units.
Another success factor is visibility: successes and learnings should be actively communicated — internally via newsletters or town halls, externally via studies or conferences. In Berlin there are many events and networks that promote this visibility and at the same time enable exchange with external experts.
In the long term this creates a self-sustaining cycle: members pass on knowledge, best practices spread within the company, and the community becomes the internal source of knowledge that drives new projects and reduces risks.
Contact Us!
Contact Directly
Philipp M. W. Hoffmann
Founder & Partner
Address
Reruption GmbH
Falkertstraße 2
70176 Stuttgart
Contact
Phone