“A primary task of management […] in the decades ahead will be to make knowledge productive” — Peter F. Drucker (1973)*

From research to results: a record of how our own frontier AI-data R&D in information systems has been applied to solve business problems and even productize solutions:
     [A]  Applied AI and other super-apps
     [B]  Data products and factories
     [C]  Data ecosystems

[A]  Applied AI/ super-apps
From 1990s multi-agent and complex adaptive systems inspired by John Holland**, agent decision-making has evolved from rules to neural networks and genetic algorithms, while analytics has progressed from machine learning and support vector machines to transformers and retrieval-augmented generation/ RAG-based data optimization for foundation models.

–  2026, LLM output relevance: Schema- and LLM-guided authoring for non-expert usability in robotic service descriptions. European Robotics Forum (ERF), Stavanger (NOR), forthcoming; Verified access control for LLM-based knowledge graph querying. 17^th International Conf on Information, Process, Knowledge Mgmt (eKNOW), Nice (FRA), link
–  Data 4: Dataspace super-apps – Compendium
–  SIM 2: Agents (are back)
–  SIM 1: From impossible to probable – Compendium
–  DAx Mktg 6: Mitigating bias in AI
–  2000 book: “Dynamics of emergent structures in digital interactive services: Organisational analysis, and Complex Adaptive System modeling and simulation” (150 pages, 1000 lines of code), link

[B]  Data products/ digital twins
“Data or die”: Drucker anticipated it early, describing the computer as “a moron” (1967) *** and data as “information’s ore” (1992)+. Even for today’s most advanced AI and LLMs, data and its relevant information content remains the conditio sine qua non: no cats in the training data, no cats in the results. Our research contributes to enriching this fuel for analytics and now also addresses data prep and provisioning for next-gen foundation models or Yann LeCun’s “world models” (Turing award winning AI pioneer, 2025)++.

–  2025-present, RAG Optimization: The impact of chunking strategies on domain-specific information retrieval in RAG systems, link
–  2025, Data Semantics: RODEOS – Robotic Data Ecosystem Semantic Model: Bridging siloed robot systems, link
–  Data 3: Digital products/ digital twins – Compendium
–  2024, Data Quality: Guggenberger, T. M., M. Altendeitering, and C. Schlueter Langdon. 2024. Design principles for quality scoring – Coping with information asymmetry of data products. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS): 4526-4535, link
–  2020, Data Factory: Schlueter Langdon, C., and R. Sikora. 2020. Creating a data factory for data products. In: Lang, K. R., J. J. Xu et al. (eds). Smart Business: Technology and data enabled innovative business models and practices. Springer Nature, Switzerland: 43-55, link
–  2019, Data Refining: Sikora R., and C. Schlueter Langdon. 2019. Marketing to “minorities”: Mitigating class imbalance problems with majority voting ensemble learning. Frontiers of Marketing Data Science Journal (Fall): 27-33, link
–  2019, Data Product: Crosby, L., and C. Schlueter Langdon. 2019. Data is a product – Managers who conceptualize data as a product can maximize its multi-functional potential. Marketing News, American Marketing Association (April 24), link

[C]  Data ecosystems with dataspace tech
Dataspace technology is the “Internet for data”: a decentralized, peer-to-peer architecture for trusted and sovereign data transactions, built on open-source software and international standards such as ISO. By enabling secure exchange of sensitive data across organizations, it creates the foundation for cross-company agentic AI and new data ecosystems.

–  2026: README: CEO-2-pager
–  2025: “Dataspaces as meta organizations”, top 3 most-read paper, link
–  Data 1: Ecosystems 2.0: Built on data – Compendium
–  2007-13: IS capabilities for relational value (Saraf et al. 2013, 2007)
–  2003-06: IS capabilities innovation: Push-pull architectures, integration-flexibility trade-off (SL 2006 JDM, SL 2003 ISEB), Web services software (SL 2003 IEEE Comput.)

* Drucker, P. 1973. Management: Tasks, responsibilities, practices. Harper & Row: New York, NY (p. 32)
** ACM. 2015. John H. Holland 1929–2015. Communications of the ACM (October), link
*** Drucker, P. 1967. The manager and the moron. McKinsey Quarterly (December 1), link
+ Drucker, P. 1992. Be data literate, know what to know. The Wall Street Journal (December 1), A16
++ Heikkilä, M. 2026. Computer scientist Yann LeCun: ‘Intelligence really is about learning’. Financial Times (January 2)