The role of AI in research and developmentis often underestimated in the current digitalization strategies of many companies. The latest Bitkom study, “Artificial Intelligence 2025, ” paints a clear picture of the digital transformation in Germany: While customer service (88%) and marketing (57%) have already deeply integrated AI solutions into their processes, research and development (R&D) is stagnating at a usage rate of just 21%.
To outsiders, this may seem like nothing more than a lag. To experts in the food industry, however, it is a symptom of a deeper, structural challenge. But why is the AI revolution stopping right at the laboratory door?
The statistical data highlights the technological divide within corporate structures: While customer- and market-facing departments are already integrating artificial intelligence across the board, the laboratory environment lags far behind.
Customer Service & Marketing as Pioneers: With AI adoption rates of 88% and 57%, respectively, these departments are already largely digitized.
The R&D Gap: Research and development has stagnated at just 21%—nearly three-quarters of laboratories are not yet tapping into the potential of modern data tools.
The result: Innovation potential often stops at the laboratory door, as the core of product development remains disconnected from company-wide AI strategies.
The reason for its widespread adoption in marketing is simple: generative AI excels at processing unstructured data (text, images). A promotional email or social media post can tolerate statistical inaccuracies.
In R&D, on the other hand, we deal with highly structured, interdependent data. A formula is not a text document; it is a complex network of chemical parameters, physical properties, regulatory limits, and business metrics.
Those who want to use AI here often run into three technical hurdles:
This infrastructure deficit leads to a massive misallocation of valuable resources. We observe a critical imbalance across the industry: the vast majority of expert capacity is tied up in routine administrative processes, while often only a marginal amount of time remains for actual value-adding innovation.
Manually typing out specifications, manually verifying declarations of conformity, and laboriously searching for raw material data are not technical tasks—they are system failures. If 79% of companies still manage these processes manually, this represents a massive loss in innovation speed (time-to-market).
While marketing is already securing market share with AI support, the engine room (product development) is struggling with tools that are no longer sufficient for the demands of 2026.
Companies that are among the top 21% of AI pioneers in R&D have realized that it’s not about using “chatbots.” It’s about creating a digital infrastructure that scales domain expertise.
The introduction of an AI-powered worksuite like UMYNO enables the transition from basic data management to R&D excellence:
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The Bitkom figures are a wake-up call. AI in marketing may boost visibility, but it is AI in R&D that ensures a product’s long-term viability.
Those who continue to neglect the digitization of R&D risk having their innovative capacity stifled by administrative overhead. It is time to close this blind spot and free the company’s brightest minds from the drudgery of maintaining spreadsheets. This is the only way to create a true competitive advantage.
