The Industry Landscape: The Urgent Need for AI-driven Knowledge Management
For global enterprises, seamless knowledge access is critical to operational efficiency and compliance. This is especially true for Finance and Accounting (F&A) functions, where large-scale process documentation – such as Standard Operating Procedures (SOPs) and Detailed Process Manuals (DPMs) – is essential for process adherence. However, inefficient document management and knowledge silos create significant barriers to productivity and accuracy.
To address these challenges, companies are increasingly turning to Artificial Intelligence (AI)-driven solutions that accelerate contextualized tknowledge retrieval and enable employees to focus on high-value tasks.
The Client Challenge: Overcoming Complexity in F&A Document Management
The client managed an extensive repository of 300+ DPMs spanning more than 40 sub-processes, with each manual averaging 50 pages. The limitations of traditional document management created:
1. Prolonged Query Resolution: Agents had to manually navigate through SharePoint’s complex search functionality, leading to extended Average Handling Time (AHT).
2. Increased Risk of Errors and Compliance Issues: Manual handling increased the risk of document mismanagement, including accidental deletions, leading to process inconsistencies and non-compliance.
The Solution: Intelligent Knowledge Management with Gen AI & Analytics
WNS deployed a Gen AI-powered Virtual SME, leveraging cloud, Natural Language Processing (NLP), analytics and Machine Learning (ML) to automate document extraction and retrieval and streamline access to critical process manuals.
The solution incorporated:
- Cloud-based Scalability: The solution was deployed on a scalable cloud platform, ensuring high availability, fast query resolution and secure document management.
- Automated Document Ingestion and Pre-Processing: The system ingested and pre-processed documents from multiple sources, eliminating manual efforts and errors caused by outdated or misplaced files.
- AI-led Intelligent Chunking and Embedding: Large documents were segmented into logical sections and transformed into vector representations for faster, context-aware retrieval, eliminating prolonged query resolution and inconsistent service quality caused by manual searches.
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ML and Model Training for Intelligent Knowledge Processing:
- The system leveraged ML frameworks to train and fine-tune models responsible for document chunking, embedding and indexing.
- This ensured efficient handling of diverse document types, allowing agents to retrieve precise, context-aware responses even as process documents evolved.
- The continuous training of models enhanced AI’s ability to interpret queries, ensuring highly relevant search results and improved response accuracy.
- Vector Database for High-Speed Search: A scalable, high-performance vector database stored vectorized chunks of the documents, ensuring fast and accurate similarity-based searches.
- Natural Language Processing (NLP) Engine: The NLP engine processed user queries in natural language, converting them into a format compatible with the vector database to fetch relevant information from the embedded document chunks.
- Azure OpenAI Large Language Model (LLM) Integration: AI-generated responses provided instant, accurate and context-aware answers, reducing manual interpretation errors.
- Advanced Analytics for Continuous Optimization: To ensure ongoing efficiency, accuracy and compliance, the solution integrated advanced analytics to track system performance, optimize AI responses and monitor document changes.
- Response Accuracy & Confidence Scoring: The system continuously tracked response accuracy and assigned confidence scores, helping identify areas that needed refinement to improve precision.
- Performance Analytics: Real-time tracking of response time, query handling time and knowledge retrieval efficacy ensured quick query resolution and process efficiency.
- Document Change & Compliance Tracking: Regular monitoring of document updates ensured agents always accessed the most up-to-date and compliant information. This minimized errors from outdated knowledge and improved adherence to business policies.
- API Development and Seamless Integration with Client Systems:
- Application Programming Interface (API) frameworks were utilized to integrate the Virtual SME with the client’s existing platforms, including SharePoint and other enterprise systems.
- These APIs enabled agents to query documents directly from their existing workflows, removing the need for platform switching and enhancing adoption rates.
- Intuitive User Interface (UI): A user-friendly UI helped agents input queries, view responses and access links to relevant documents for verification and review.
The Outcome: Productive, Compliant and Scalable F&A Knowledge Management
The solution delivered immediate measurable improvements in knowledge management and retrieval, impacting operational and compliance efficiency. Key outcomes within the first two months of implementation included:
percent reduction
in AHT, resulting in
improved productivity
Increased
DPM compliance
8.25/10 user satisfaction
rating from agents in a qualitative
survey on ease-of-use, functionality,
performance and user experience
Furthermore, early results indicate promising developments in critical areas such as:
Consistent Service Quality:
Standardized responses are improving accuracy and reliability, strengthening customer trust.
Stronger Knowledge Retention:
The platform is mitigating the impact of attrition by preserving institutional knowledge and simplifying onboarding for new agents.
Optimized Cost Efficiency:
Reduced handling times, lower training overheads and fewer errors are expected to contribute to cost savings and improved profitability.
Enhanced Scalability:
The API-driven architecture has enabled seamless integration with additional systems, positioning the client for future growth.