“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 information systems science has been applied to solve business problems and even productize solutions.

Next AI … and other 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 next-generation large language model (LLM) performance.

–  2025-present, 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

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, 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 and economizing on this fuel for analytics and now also addresses data prep and provisioning for next-gen foundation models, or “world models,” as Yann LeCun has described them (Turing award winning AI pioneer, 2025)++.

–  2025, RAG Optimization: The impact of chunking strategies on domain-specific information retrieval in RAG 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

Data ecosystems enabled by dataspace tech
Dataspace technology is a Web3‑oriented, decentralized peer‑to‑peer data‑transaction system—an “Internet for data”—that enables trusted, sovereign data exchanges while respecting intellectual property, all built on open‑source software and international standards, such as ISO. In today’s digital age, dataspace technology is the decisive enabler that, for the first time, makes it possible to scale ecosystem concepts from paper into real‑world practice.

–  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: Push/ pull architectures and IS capabilities (SL 2006 JDM, SL 2003 ISEB), Web services software (SL 2003 IEEE Comput.)

Industry: AutoMobility transformation
Automotive remains one of the world’s most important industries and economic engines because of size, innovation and employment. It is a pioneer of applying new technology including autonomous driving, electrification, and Mobility-as-a-Service (MaaS).

– Auto 7: IAV’s ‘enterprise dataspace’ – Case study
– Auto 6: Sustainability with Catena-X – Case study
– Auto 5: Mobility super-app disruption – Case study
– Auto 4: Auto versus Mobility – Box versus triangle
– Auto 3: One future of auto is airlines
– Auto 2: Power shift to data
– Auto 1: Digital service shift and 7 gaps
Mobility
– DAx Mob2: Quantifying integrated mobility (2021-03-15)
– Mobility with IDS: Adding the “N” in NPM to RealLab HH (2020-10-12), link
– Metamorphosis of Auto into Mobility (2020-07-10), link
– DAx Mob1: Show me the money (2020-06-06)
– “Calculator” powered by ML: Mobility-as-a-Service (2017-07-04)

* 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)