Creating Value from Data & AI: Strategy, Options, Prioritization Insights into research findings on strategic value creation from data
From the next major LLM release such as ChatGPT 5 to autonomous agents, everyone is talking about artificial intelligence (AI). However, the expected benefits often cannot be fully materialized in practice. In many companies, data and AI initiatives do not make it past the pilot phase, while others fail completely (Estrada 2025; Haefner et al. 2023) .An important reason is that the current enthusiasm often focuses too much on technologies and ignores fundamental business problems.
Ultimately, the goal of any data and AI initiative should be to create strategic business value that provides companies a competitive advantage (Grover et al. 2018). This requires a shift in perspective towards the fundamental data assets and the exploitation of their inherent potential, regardless of the analysis technologies used, such as business analytics, traditional or generative AI, following the maxim: AI as a means and data value creation as an end.
This blog post introduces the research area “Value Creation from Data and AI” at the Competence Center Future Financial Services, where we develop applicable tools that support companies to realize strategic value from data and AI.
Initial situation in practice and science
While data has often been referred to as the “new oil”, it has evolved from a mere byproduct of economic activity to a strategic asset of company-wide importance (Legner et al. 2020). Beyond the AI hype, there has been a consensus among executives for years that the value-creating use of data analytics can transform entire business models (Gartner 2018). Knowledge of the strategic business potential of data has long since arrived in practice; for example, 42% of IT professionals surveyed state that their companies use AI, while a further 40% are considering introducing it (IBM 2023). Accordingly, companies have been flooded with ideas for creating value from data and AI for some time. At the same time, decisions on their adoption are often subjective and trend-driven and ignore strategic perspectives, which means that projects get stuck in pilot phases and cannot develop their full potential (Estrada 2025; Haefner et al. 2023).
There has also been a lack of an integrated overall view in science to date. A comprehensive body of scientific literature has been dealing with partial fragments of value creation from data such as business analytics, data-driven business models and data monetization for decades (Kakuschke 2024). However, works that take an overarching perspective and combine strategic aspects with directly value-creating use cases remain underrepresented. Accordingly, we focus on value creation from data, which we define as the transformation of the potential value of data or information objects into actual value for organizations through their exchange or use (Kakuschke 2024).
Scientific approach and objective of the research stream Data Value Creation
The Competence Center Future Financial Services of the Business Engineering Institute St. Gallen pursues the development of scientific solutions for relevant practical problems as part of the consortium research approach according to Österle and Otto(2010) and the Design Science Research (DSR) paradigm according to Hevner(2007).
A cooperation between 12 companies from the financial industry in the DACH region and scientific representatives has set the common objective of supporting companies in the strategic creation of value from data and AI. Based on rigorous scientific methods such as systematic literature reviews, expert interviews, and focus group meetings, three solution artifacts are being created to achieve this goal (cf. graphic below). The artifacts have been designed in several cycles over the last three years and have already been presented, discussed, applied and refined with the practice partners in over 10 multi-day workshops lasting several days.
The research agenda addresses three core questions: How do organizations develop a data and AI strategy? What options do they have to create value from data? And how can a suitable use case portfolio be selected for implementation?
Artifact 1: Data strategy architecture
The data strategy architecture specifies which elements a data and AI strategy comprises and how it should be developed (workflow). The elements of the strategy are structured on the basis of three levels: Elements of strategic direction, fields of action including a coordinated use case portfolio, and elements of strategy implementation. The duality of data and AI strategies is central, which interlinks directly value-adding aspects (offensive orientation) such as innovative use cases with aspects relating to security, compliance and trust (defensive orientation).
In the future development of the artifact, the strategy modules will be detailed so that a comprehensive strategy canvas is available for our partners. The canvas will then be used and further refined with our partners.
Artifact 2: Data Value Creation Matrix and Ideation Patterns
The Data Value Creation Matrix (see this blog post) structures the scope for creating value from data and AI. The strategic value of data can be realized through a wide range of opportunities, such as increasing efficiency and quality in processes, product and service innovations to the exchange and direct monetization of data with customers, suppliers and other stakeholders (Kakuschke 2024). With the help of two axes: value objects (data vs. information) and value types (use internally vs. exchange externally), four quadrants are created in the matrix that support the strategic positioning of value creation from data for companies (see graphic below).
To improve the practical applicability of the model, we have detailed the quadrants into 10 options and a total of 30+ use case patterns (not shown in the figure). Ideation cards were designed on the basis of these use case patterns, with a detailed description for each pattern and at least two real use cases from the financial industry and beyond. With the help of these cards, companies can derive new use cases for themselves (ideation process) and identify future potential for value creation from data using a white spot analysis.
The Data Value Creation Matrix, including the ideation cards, has so far been tested and refined several times in focus group meetings and used directly by a large Swiss IT provider to identify new use cases. Further applications of the matrix are being planned.
Artifact 3: Assessment framework for data and AI use cases
The Assessment Framework (see this blog post) systematizes relevant criteria for the evaluation and prioritization of use cases (see figure below). Here, 38 criteria (not shown) are organized hierarchically in 11 groups across 4 dimensions. It is important that data and AI use cases are not evaluated solely based on their technological feasibility, but on the basis of a more comprehensive view that reflects their use in everyday business. Accordingly, the suitability of use cases must take into account whether they are fundamentally desirable for the implementing company (based on strategic fit and addressing a user problem or need), whether the case is technologically and organizationally feasible, whether the costs exceed the benefits and, finally, whether the case satisfies other parameters such as compliance, ethics and sustainability (Kakuschke et al. 2025). The Assessment Framework places a particular focus on the evaluation of use case portfolios, in which dependencies and synergies between individual use cases are taken into account.
The Assessment Framework has been tested several times in focus group meetings and has also been used by a Swiss bank and a Swiss IT provider to prioritize existing and planned data and AI use cases.
In addition, a clickable software prototype was developed on the basis of the model developed as part of the hackathon “BärnHäckt“, which supports companies in deciding on a suitable data and AI use case portfolio, taking into account the framework criteria, use case dependencies and the overall budget to be adhered to.
Benefits for practice and science
The three artifacts developed as part of the research area of value creation from data and AI are designed as independent tools but can be used together to achieve the greatest possible impact. Accordingly, the Data Strategy Architecture can be used to construct the target state for creating value from data, which provides the connection to the corporate and digital strategy of a company. The Data Value Creation Matrix visualizes the range of opportunities and structures strategic positioning options for value creation and use case ideation. The Assessment Framework helps to develop detailed use cases from initial ideas and supports structured investment decisions as part of an overarching use case portfolio.
Accordingly, data and its use are elevated to a strategic management perspective as part of our research program and different understandings of data value creation are harmonized. Instead of treating initiatives as isolated projects, we analyze them in a systemic context and combine the strategic perspective with the view of a use case portfolio to be implemented. In this way, we help companies to focus on the most effective data and AI initiatives as part of a coordinated strategy and ultimately increase the value to be derived from data.
The 3.5-year research project will enter its final phase in October 2025 with a research visit to the ARC Training Centre for Information Resilience (CIRES) at the University of Queensland, Australia. This research project will end with the submission of my dissertation and subsequent defense in the first half of 2026. At the same time, a new research project in the area of Data & AI will be launched at the Competence Center Future Financial Services.
If you are interested in further information on creating value from data & AI, please contact me at nick.kakuschke@bei-sg.ch
References
Estrada, S. 2025. “MIT Report: 95% of Generative AI Pilots at Companies Are Failing,” Fortune. (https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/, accessed August 26, 2025).
Gartner. 2018. “Gartner Survey of More Than 3,000 CIOs Reveals That Enterprises Are Entering the Third Era of IT,” Gartner, , October 16. (https://www.gartner.com/en/newsroom/press-releases/2018-10-16-gartner-survey-of-more-than-3000-cios-reveals-that-enterprises-are-entering-the-third-era-of-it, accessed October 24, 2024).
Grover, V., Chiang, R. H. L., Liang, T.-P., and Zhang, D. 2018. “Creating Strategic Business Value from Big Data Analytics: A Research Framework,” Journal of Management Information Systems (35:2), pp. 388-423. (https://doi.org/10.1080/07421222.2018.1451951).
Haefner, N., Parida, V., Gassmann, O., and Wincent, J. 2023. “Implementing and Scaling Artificial Intelligence: A Review, Framework, and Research Agenda,” Technological Forecasting and Social Change (197), p. 122878. (https://doi.org/10.1016/j.techfore.2023.122878).
Hevner, A. R. 2007. “A Three Cycle View of Design Science Research,” Scandinavian Journal of Information Systems (19:2), pp. 87-92.
IBM. 2023. “IBM Global AI Adoption Index – Enterprise Report,” IBM Newsroom, , September. (https://newsroom.ibm.com/2024-01-10-Data-Suggests-Growth-in-Enterprise-Adoption-of-AI-is-Due-to-Widespread-Deployment-by-Early-Adopters, accessed October 24, 2024).
Kakuschke, N. 2024. “Data Value Creation Matrix – Options for Organizations to Create Value from Data,” ECIS 2024 Proceedings (9). (https://aisel.aisnet.org/ecis2024/track07_busanalytics/track07_busanalytics/9).
Kakuschke, N., Legner, C., and Jung, R. 2025. “Creating Value from Data the Right Way – A Framework for Assessing Data Value Creation Use Cases,” Proceedings of the 58th Hawaii International Conference on System Sciences. (https://hdl.handle.net/10125/109535).
Legner, C., Pentek, T., and Otto, B. 2020. “Accumulating Design Knowledge with Reference Models: Insights from 12 Years’ Research into Data Management,” Journal of the Association for Information Systems (21:3). (https://doi.org/10.17705/1jais.00618).
Österle, H., and Otto, B. 2010. “Consortium Research,” Business & Information Systems Engineering (2:5), pp. 283-293. (https://doi.org/10.1007/s12599-010-0119-3).
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