Applying AI in Project Procurement

From the AI IQ Blog by Paul Boudreau
Technology offers an incredible opportunity to improve project performance. This blog shares the latest research and how organizations are implementing AI into their project methodology. Come with an open mind, increase your knowledge, share your concerns, and become a project manager with new skills to offer an organization.

Project procurement management is critical to the success of many projects. There are three important areas where artificial intelligence (AI) technology will change current practices: the contract statement of work, vendor selection, and tracking procurement progress.

The contract statement of work (SOW) takes on additional significance when outsourcing work to a vendor. The SOW is the basis for requesting bids, evaluating potential vendors, and finalizing a contractual agreement to deliver the SOW contents. Therefore, the SOW needs to be accurate and comprehensive. Natural language processing (NLP) is an essential tool to provide this requirement. NLP uses documents from similar projects and the wording in the SOW to find errors or omissions. NLP performs entity recognition to extract objectives, deliverables, and stakeholders. Sentiment analysis is performed to analyze the tone or content of communication and ensure no issues or conflicts. NLP identifies and extracts keywords critical to project success, then compares that data to find omissions.

The vendor selection process is one of the most challenging areas to maintain ethical behavior. NLP software can remove human bias, similar to current software that scans a candidate’s resume to determine how well they meet job requirements. In addition, seller bid submissions will be automatically checked and ranked based on the selection criteria.

To track vendor progress, the performing organization needs to observe vendor behavior and, if possible, access vendor project data. Robotic process automation (RPA) software creates reports and determines the most critical metrics. A machine learning algorithm detects anomalies and predicts future performance. Unsupervised learning performs clustering of data, compares trends in performance, and highlights variances that may result in unacceptable deviations. This allows both the vendor and project manager to be proactive to avoid any deterioration in the probability of project success.

The future of project procurement will be based on applying AI technology.