Case Study 3
Modernizing Engineering Services: AI-configured Processes for a Global Engineering Services Provider

Configuring intelligent processes for speed,

accuracy, and control

Opportunity

The Architecture, Engineering, and Construction (AEC) industry faces a myriad challenges in its business functions, primarily revolving around inefficiencies in repetitive tasks, resource-intensive manual processes, and the difficulty of managing project hierarchies. These issues often lead to time sinks, cost overruns, and accuracy concerns that can hinder operational efficiency and profitability.

Solution

To address these challenges, a leading engineering services provider adopted AI-driven solutions to automate, optimize, and elevate the efficiency of recurring business functions. Powered by Infosys-developed AI assets, these actions were aimed at creating scalable, adaptive, and accurate processes. Key solutions implemented included:

AI-driven knowledge search
A generative AI-powered search engine allowed employees to quickly retrieve context-specific information from any document, with role-based security for controlled access. Integration with chatbots provided a seamless experience.

Opportunity win prediction
Used historical data to predict business win probabilities. The AI model identified key factors, enabling customer-focused strategies and boosting sales efficiency. A pricing benchmark feature provided actionable insights for the sales team.

Supplier invoice processing
Leveraging Infosys Oracle DU AI, suppliers could submit and process invoices with zero manual effort. This AI-driven automation reduced errors and streamlined workflows.

DSO improvement with analytics
Pre-packaged analytics offered deep insights into receivables and collections. AI-driven forecasts improved cash flow and optimized Days Sales Outstanding (DSO).

Automated invoice matching
Improved invoice-to-PO matching accuracy using ML-based adaptive learning. Key features included automated invoice data extraction from emails and streamlined accounting processes.

Outcomes

The adoption of AI-powered solutions produced substantial operational improvements for the company, delivering measurable value across key business objectives:

Enhanced process efficiency
Supplier invoice processing automation significantly reduced manual effort and generated substantial time and cost savings.

Streamlined cash flow management
AI-driven DSO optimization and forecasts improved revenue productivity and strengthened the overall financial outlook.

Scalability and accuracy
Adaptive learning systems for invoice matching achieved a 95%+ success rate in PO matching, reducing manual errors and increasing scalability.

Key Learning

AI and ML technologies reinforced the client’s financial framework by automating invoicing, improving PO matching accuracy, and optimizing cash flow with predictive insights. This reinforced efficiency into operational scaffolding, reducing manual inefficiencies, enhancing scalability, and delivering measurable business value.