The challenge
Our client had set an ambitious target: to unlock significant efficiency gains through AI across the organization. An initial assessment had identified savings potential in excess of $50 million annually, creating strong momentum for large-scale AI adoption. However, while a high-level AI strategy, target operating model and dedicated AI unit were already in place, a fundamental question remained unanswered. Which initiatives would actually deliver the expected business impact, and was the organization prepared to implement them at scale?
The client required a pragmatic roadmap linking strategic ambition with operational reality. This meant identifying the most valuable AI opportunities across the business, validating their impact, prioritizing investments, and understanding the technology, data, governance and organizational capabilities required to realize the benefits.
The approach
Together with the client, Eraneos applied its process-driven AI Transformation Framework, turning top-down objectives into a bottom-up, execution-ready transformation roadmap.
We began by assessing the organization’s current AI, data and technology capabilities, reviewing the maturity of its operating model, governance structures, technology landscape and available resources. This provided a realistic view of the company’s ability to scale AI and highlighted capability gaps that needed to be addressed.
To establish a robust fact base, we mapped and analyzed more than 30 end-to-end business processes across the organization. Existing AI initiatives and over 300 previously collected use case ideas were consolidated, validated and systematically linked to business processes. This created an enterprise-wide heatmap of AI opportunities, revealing both areas of high potential and business domains with limited AI coverage.
"By combining process expertise, AI innovation and rigorous business-value assessment, the organization gained a clear path to more than $50 million in efficiency gains, supported by approximately 30 prioritized use cases in the first year alone."
We then selected processes with significant AI opportunity gaps, high automation potential, or substantial operational cost and manual effort. In a series of workshops, we first assessed process inefficiencies and improvement opportunities before applying our AI Design Sprint methodology to identify AI and automation potential. Using AI capability cards, stakeholders translated process pain points and improvement ideas into concrete use cases with measurable business impact.
The workshops generated more than 130 additional AI and automation use cases, complementing over 300 previously identified ideas. All use cases were consolidated into a single enterprise-wide portfolio and assessed based on business value, feasibility, implementation effort, dependencies, required technology foundations and organizational capabilities. This created a transparent fact base for prioritization and long-term planning.
The result
The project delivered a board-ready AI transformation roadmap providing a clear and actionable path for the next five years. Executive ambitions were translated into a quantified value creation plan, supported by validated use cases, implementation priorities and a realistic transformation sequence.
A detailed business case was developed to demonstrate how the targeted savings could be achieved by 2030. The roadmap incorporated expected business benefits, required investments, implementation lead times, organizational adoption curves and dependencies between initiatives, creating a realistic path from strategy to value realization.
From a portfolio of more than 430 assessed use cases, approximately 30 high-priority initiatives were selected for the first year of implementation. For these initiatives, detailed solution designs and project charters were developed, providing the client with a concrete starting point for execution and benefits tracking.
Beyond the use case portfolio itself, the program clarified requirements for the future AI operating model, governance structures, technology landscape and workforce capabilities. Business units gained transparency on implementation timing, expected value realization and required organizational changes, enabling better planning, investment decisions and resource allocation across the organization.
Following completion of the roadmap, the client transitioned directly into large-scale implementation. What began as an AI strategy initiative evolved into a structured enterprise-wide transformation program with a clear long-term vision and an actionable execution plan.
About the client
Our client is a leading European financial services company.
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