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September 17, 2026European Journal of Information SystemsOpen Access

Scaling data management capabilities for enterprise AI: a maturity model for the banking industry

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Authors

NBN. BaumLMLea Mueller-FortmannABAlexander Benlian

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Overview

Longitudinal clinical case study reveals five interdependent capability domains enabling enterprise artificial intelligence in banking, highlighting the central role of cultural readiness.

Key Points

  • Investigate how financial institutions transition from legacy, fragmented systems to cloud-enabled data environments capable of supporting enterprise-scale artificial intelligence.
  • Conducted a longitudinal embedded clinical case study tracking three sequential transformation phases within a German bank.
  • Analyzed executive processes of experimentation, negotiation, and learning to understand how organizations institutionalize interdependent data management capabilities.
  • Identified five mutually reinforcing capability domains: Technology and Infrastructure, Data Governance and Quality, Cultural and Organizational Shifts, Regulatory Compliance and Risk Management, and AI Enablement.
  • Formulated a grounded maturity model detailing diagnostic signals, minimum viable actions, and readiness indicators for scaling trustworthy AI under strict regulatory constraints.

Cite This Study

Baum et al. (2026) studied this question.

synapsesocial.com/papers/6aabb75d5f706d05830e65bahttps://doi.org/10.1080/0960085x.2026.2723258
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