Use case

AI-ready data foundations for scalable financial operations

Overview

Value delivered

Reorganize customer and operational data around business needs and establish AI-ready data foundations that support scalable, efficient operations.

Challenges

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Customer, operational, and product data remain fragmented

Solution approach

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Building AI-ready data foundations

Results

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Scalable operations enabled by AI

Consult on building a data infrastructure for AI utilization

Challenges

Fragmented data structures limit AI adoption

Fragmented data

Customer, operational, and product information is fragmented across systems.

Limited AI adoption

Data meanings are not standardized, making AI adoption less effective.

Barriers to business expansion

Operations depend heavily on personnel.

To support business growth while improving productivity, many financial institutions are looking to increase operational efficiency through workforce optimization, business process standardization, and the use of AI agents. However, even when data warehouses and data lakes are in place, data often remains organized around source systems rather than customers or business operations. In addition, while data capturing business outcomes may exist, information describing the decision-making processes behind those outcomes is often missing. As a result, the underlying data structure is not well suited to supporting autonomous AI agents. To enable AI-driven automation and scalable growth, customer and operational data must be reorganized, and critical data gaps must be addressed.

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What we do

Redesigning business processes and building AI-ready data foundations

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Transformation approach to an AI-ready data foundation

Step 1
Data model redesign

Establish a common data model across customers, products, and operations

Step 2
Semantic foundation and transformation roadmap

Define the semantic layer and a phased roadmap

Step 3
Transforming into an AI-ready data platform

Support from strategy through execution

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Wayfinders helps organizations reorganize existing data architectures around business, customer, and operational domains and redesign target data models that support AI-enabled ways of working. This includes establishing a strategy for the semantic layer required for AI agents to operate autonomously and identifying the data needed to support decision-making processes. Based on business and operational priorities, a phased roadmap is developed to build AI-oriented data foundations over time. Using this roadmap as the foundation, existing data platforms are transformed into AI-driven data and AI platforms, with support spanning both planning and execution.

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Impact

Enabling scalable operations and sustainable growth

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Standardized operations and data

Standardization of operations and data flows
AI-enabled process automation

Reduced dependency on people

Reduced dependence on headcount growth
Stable business operations

Scalable business growth

Greater business scalability
Lower IT investment costs

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By centralizing and organizing customer and business master data together with the events generated from them, organizations can significantly simplify complex data flows that span multiple functions. In addition, establishing an event-driven process automation strategy based on data enables the development of practical and actionable AI adoption plans. The result is a scalable foundation that can flexibly accommodate new products, customers, and business requirements while maintaining stable processes and data structures. This supports greater business scalability and helps reduce IT investment costs over time.

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Agentic transformation

AI-ready data foundations are a critical enabler of Agentic Transformation. Discover how redesigning people, processes, systems, and data helps AI agents continuously deliver value across business operations.

Discover agentic transformation

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Contact us

Contact our consulting team

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