Case Study 1
The Workflow Advantage of Generative AI for a Global Tax and Advisory Leader

Improving operational clarity and control through intelligent automation

THE CORE BUSINESS CHALLENGE

The client, a global tax and advisory leader, required an advanced AI-based solution to streamline operations, reduce manual effort, and improve productivity across various business and technical functions. Their goal was to integrate AI into their workflows to reduce costs, optimize performance, and create scalable solutions while maintaining robust governance and security.

Enhance Productivity across Workflows: Existing processes involved heavy manual intervention, making scaling and efficiency improvements difficult.
Achieve Cost Optimization across Services: The firm sought to reduce spend on managed services and drive efficiencies in several areas like testing, infrastructure, and application support.
Enable Innovation in Business Functions: AI was needed to build unique functionalities, automate workflows, and connect diverse data sources for actionable insights.

Adopted a Modular AI Framework: Solutions like LangGraph and LangFlow were introduced for data loading, querying, and LLM observability.
Automated Labor-intensive Tasks: Processes such as risk assessment, termination evidence collection, and registration validation were refined with generative AI enhancements.
Scaled AI Capabilities Globally: Proprietary models were integrated into day-to-day operations, enabling consistent AI use cases across global teams.
Implemented Configuration-driven Architecture: Real-time APIs and dynamic configuration layers ensured adaptability and operational ease in managing diverse data repositories.

HOW WE SOLVED IT

To address these challenges, the firm adopted a Gen AI-first approach and implemented several targeted AI solutions across their operations. This approach centered on the deployment of scalable AI models, agentic workflows, and AI-powered platforms designed to transform and standardize their workflows.

AI hubs and personas were created to deliver productivity gains across IT services, while tools like GitHub Copilot streamlined development and testing. Additionally, they implemented RAG-as-a-Service (RaaS), a proprietary platform for accelerated AI adoption, which unified and indexed data repositories for real-time insights and automation.

RESULTS AND VALUE

The adoption of generative AI solutions delivered measurable efficiency and cost benefits while setting a strong foundation for innovation. The approach not only reduced manual intervention but also introduced automation that supported operational excellence on a global scale.

Cost Savings across Services: Realized a 20% to 50% reduction in costs across managed projects, infrastructure services, testing, and application support services.
Time-to-market Improvements: AI-powered solutions accelerated processes, reducing manual effort and time required for audits, compliance, and support tasks.
Standardized Global Workflows: The scalable tools ensured consistent deployment of AI solutions across the organization.

AI IN ACTION

The implementation highlighted diverse applications of AI, including:

Enhanced document workflows with solutions for data redaction, template generation, and autonomous analysis of complex input formats.
Agentic workflows for managing unstructured data, automating audit evidence matching, and handling classification processes like bank reconciliation.
Synthetic data generation tools to enable global testing environments that closely replicate real-world scenarios.

The integrated AI-first approach has enabled sustained efficiency, reinforced by governance and a responsible AI framework, further advancing the client’s readiness for future innovations.