German public research transfer offices are piloting AI largely for language and content tasks, but envision broader process and decision-support uses; adoption is fragmented and often misaligned with established transfer workflows.

Main Finding

Adoption of AI — especially large language models (LLMs) — in German public non‑university knowledge and technology transfer (KTT) units is growing but fragmented. A small set of broadly used, entry‑level language‑centric applications (content preparation and routine language work) coexist with a larger portfolio of aspirational or pilot use cases aimed at process support, information discovery/triage, evidence synthesis, and decision‑oriented workflow automation. The paper organizes these into a comparative AI use‑case matrix mapped onto a modular KTT reference model (from research to spin‑off management).

Key Points

• Empirical base: 12 semi‑structured expert interviews across major German public non‑university research institutions.
• Two-tiered taxonomy: (1) use cases already in use (including pilots), and (2) desired / high‑potential use cases.
• Widely adopted entry uses: language‑centric knowledge work (drafting, translation, summarization) and content preparation for outreach.
• Aspirational/pilot uses: automated information discovery and triage, evidence synthesis and literature review support, process orchestration, decision support and workflow automation across KTT stages.
• Mapping: use cases structured along a modular KTT reference model covering activities from research outputs to spin‑off creation and commercialization.
• Current state: experimentation is common, but adoption is rarely coherent or systematically aligned with core transfer workstreams.
• Governance and capability gaps: fragmentation reflects uneven technical capacity, data access, evaluation practices, and uncertainty about legal/ethical and IP implications.
• Output deliverable: a comparative matrix consolidating actual and potential AI uses, intended to inform practice and future evaluation.

Data & Methods

• Method: Inductive qualitative analysis of 12 semi‑structured expert interviews.
• Sample: Experts from major German public non‑university research institutions involved in KTT/technology transfer.
• Analysis steps:
  • Elicited concrete AI use cases and categorized them by current status (in use, pilot, aspirational).
  • Mapped use cases onto a modular KTT reference model spanning research production to spin‑off management.
  • Constructed a comparative AI use‑case matrix to highlight diffusion patterns and gaps.
• Limitations: small, qualitative sample focused on German public non‑university sector; findings are exploratory and aimed at hypothesis generation rather than causal inference.

Implications for AI Economics

• Productivity and task composition: Adoption is concentrated in routine, language‑intensive tasks, implying near‑term productivity gains via task automation/complementarity rather than wholesale labor displacement. Economic models should treat KTT staff as complementing LLMs for higher‑order transfer activities.
• Diffusion dynamics: Fragmented, institution‑specific experimentation suggests adoption follows a patchwork diffusion path. Economic analyses should incorporate heterogeneity in capabilities, governance, and data endowments across institutions.
• Returns to scale and network effects: Centralized or shared AI tooling and data infrastructure across institutions could generate economies of scale and positive spillovers (e.g., standardized corpora, shared prompt libraries), affecting the aggregate social return to investment in AI for KTT.
• Investment and allocation: Evidence of many high‑potential but unimplemented use cases signals an investment gap (skills, evaluation frameworks, integration). Cost–benefit analysis and evaluation metrics (time saved, deal throughput, spin‑off success rates) are needed to prioritize deployment.
• Market structure and commercialization: Faster evidence synthesis, triage, and decision support can shorten commercialization cycles and potentially increase spin‑off formation; this may alter funding needs and the timing of private sector engagement with public research.
• Policy and regulation: Legal, IP, and data governance issues are bottlenecks; policy interventions (clear guidance on IP, data sharing frameworks, procurement support) can materially influence adoption and economic outcomes.
• Research agenda: Quantify economic impacts (productivity, time to market, number/quality of spin‑offs), model adoption under uncertainty and heterogeneous institutions, and evaluate centralized vs decentralized provisioning of AI capabilities for public research transfer.

Recommendations (practical for economists and policymakers) - Standardize evaluation metrics for AI pilots in KTT (e.g., time per task, quality-adjusted outputs, conversion rates to commercialization). - Invest in shared infrastructure (secure data platforms, common toolkits) to capture scale effects and reduce redundant experimentation. - Support cross‑institutional knowledge sharing (best practices, reusable prompts, governance templates) to reduce fragmentation. - Fund targeted pilots focusing on high‑value, decision‑oriented workflow automation and measure economic returns. - Incorporate legal/IP clarity and upskilling programs into funding for AI adoption to address non‑technical adoption barriers.