AI Agents Transform Procurement with Automation and Efficiency

The Future of Procurement with AI

AI agents are poised to transform traditional procurement processes by automating decision-making, improving estimate precision, and reducing operational individualization. By converting past orders into data and replacing specialized knowledge with AI processing, these agents streamline operations while enabling purchasing managers to focus on strategic tasks like demand prediction and supplier relations.

Step 1: Building Business Infrastructure

(1) Reading and Digitization of Estimates with AI-OCR

Many companies still rely on manual data entry for estimates and invoices, leading to errors and inconsistencies. AI-OCR technology automates the conversion of handwritten and printed text into digital data, extracting key information like supplier names, product details, and prices. Combined with large language models (LLMs), this method significantly improves accuracy and efficiency.

(2) Deduplication Using Generative AI

Inconsistent notation across suppliers and departments complicates data integration. Generative AI addresses this by identifying similarities in differing names and standardizing formats. For example, it can recognize "Hexagonal Bolt M10" and "Hexagonal M10 Bolt" as the same item, streamlining searches and analysis.

Step 2: Streamlining Procurement with AI Agents

(1) Analysis of Request Content

AI agents reference digitized transaction data to quickly identify potential suppliers, eliminating the need for specialized knowledge and reducing decision-making time.

(2) Exchange of Estimates

Automated communication between AI agents streamlines the estimate request process, reducing errors and business risks associated with manual handling.

(3) Estimate Analysis

AI-OCR automates the extraction and analysis of estimate information, saving hours of manual labor and ensuring consistency across varying formats.

(4-1) Fair Price Adjustment

AI agents use market trends and predictive modeling to determine appropriate prices, addressing challenges posed by soaring material costs and limited expert availability.

(4-2) Supplier Information Collection

Automated collection of supplier data prevents individualization and enables real-time risk detection, improving overall procurement efficiency.

(5) Output Preparation

AI-generated reports present supplier information in an easy-to-compare format, facilitating optimal decision-making and reducing risks of overpricing and delays.

Conclusion

The integration of AI agents into procurement processes promises significant efficiency gains, cost reductions, and risk mitigation. By automating routine tasks and enhancing decision-making with data-driven insights, companies can achieve a more streamlined and sophisticated procurement operation.