AI strategy and operating model in retail banking – from isolated initiatives to scalable AI governance

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A European retail bank faced the challenge of consolidating fragmented AI initiatives into a scalable overall strategy. Eve by Eraneos developed a comprehensive AI operating model – including clear governance, an AI Hub & Spoke model, and an investment framework of up to 3% of total revenues.

3

%

AI investment budget as share of total revenues

50

%

targeted efficiency gains from AI-native process redesign

Fragmented AI initiatives without governance

A European retail bank with a nationwide branch network was running multiple isolated AI initiatives without a clear vision or strategy. Responsibilities were fragmented and overarching governance was entirely absent. The goal was to consolidate these decentralized AI initiatives into a coherent AI strategy with a clear, scalable AI operating model and a significant AI investment budget of up to 3% of total revenues.

Four-step AI operating model design

Eve by Eraneos developed a tailored four-step approach:

  1. AI maturity analysis benchmarking internal capabilities against national and international peers and market potentials
  2. Definition of an ambitious vision for AI’s value contribution and derivation of strategic goals and action areas
  3. Design of a hybrid AI operating model (“Hub & Spoke”) focused on evolving into an AI-driven bank
  4. Planning the operationalization of the AI strategy to effectively realize growth and efficiency gains

"AI projects don't fail because of technology – they fail because of a lack of accountability. Our answer is an AI Hub & Spoke Model with clear governance that consolidates responsibilities and brings AI into core processes at scale."

Scalable AI hub & spoke transformation

The AI operating model developed by Eve by Eraneos enables the bank to pursue a structured, scalable AI transformation. Seven concrete action areas were defined:

  1. Establishing an AI organization: Design of an AI hub with decentralized business unit spokes – centralized infrastructure with decentralized knowledge building
  2. Integrating specialized roles: Establishment of expert roles (business & tech) in the AI hub as well as AI ambassadors in the business units
  3. Designing AI-native processes: Redesign of AI-native processes for critical business processes with the goal of achieving efficiency gains of up to 30–50%
  4. Standardizing use-case processes: Unification of processes with varying levels of detail and alignment depth depending on the complexity of the AI initiative
  5. Laying the foundations for agentic AI: Outlining the technology infrastructure and the integration of AI as the primary orchestrator for existing and new processes
  6. Deriving regulatory implications: Identification of regulatory requirements (including the EU AI Act) and their implications for the AI operating model and the integration of AI initiatives
  7. Launching a transformation & communication roadmap: Introduction of a company-wide communication and enablement program for the phased change management concept

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