Upon completion of this course, participants will be able to:

• Define key AI concepts (ML, NLP, RPA) and their specific applications within the procurement lifecycle.
• Understand how **Cognitive Spend Analysis** and AI can uncover hidden savings and risks in data.
• Evaluate and select appropriate AI/ML tools for tasks such as contract review and risk monitoring.
• Design and manage an **AI implementation project** from pilot to scale-up within procurement.
• Apply AI to improve demand forecasting, anomaly detection, and fraud prevention.
• Utilize **Natural Language Processing (NLP)** for automated extraction of key contract terms.
• Understand the ethical considerations and data privacy challenges related to using AI in sourcing.
• Outline the necessary data infrastructure and governance required to support AI/ML initiatives.

• Procurement and Sourcing Directors/Managers
• Procurement Technology and Digital Transformation Leads
• Senior Procurement Analysts and Data Scientists
• IT and Enterprise Architecture Staff supporting SCM/P2P systems
• Internal Audit and Risk Professionals focused on digital controls
• Category Managers seeking advanced analytical tools

• Case study analysis of a company's successful AI deployment in cognitive spend analysis.
• Group activity: designing a business case and pilot plan for an AI application (e.g., automated contract review).
• Workshop on data governance requirements for supporting machine learning models.
• Individual exercise: mapping a procurement process and identifying optimal RPA/AI automation points.
• Discussions on the future role of the buyer in an increasingly automated procurement environment.

Unit 1: Foundations of AI and ML in Procurement

• Key definitions: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning
• Understanding **RPA (Robotic Process Automation)** for transactional efficiency
• The evolution from basic analytics to prescriptive AI in sourcing decisions

• Identifying high-value AI applications (e.g., supplier segmentation, fraud detection)
• Quantifying the ROI of AI implementation in reducing labor costs and generating savings

Unit 2: AI in Spend Analysis and Demand Forecasting

• Using ML for automated data cleansing, classification, and taxonomy standardization
• AI algorithms for identifying non-compliant spending and **"maverick"** purchases

• Applying ML models for more accurate, automated demand forecasting
• Using AI for anomaly detection in spending patterns and invoice processing

Unit 3: AI in Sourcing and Supplier Risk Management

• AI tools for generating negotiation intelligence (predictive supplier behavior)
• Using AI to automate supplier discovery and initial qualification screening

• AI models that predict supplier failure (financial, operational) based on diverse data sources
• NLP and text analysis for monitoring real-time media and geopolitical risk signals

Unit 4: AI in Contract and P2P Automation

• Utilizing **Natural Language Processing (NLP)** for automated contract term extraction
• AI tools for identifying missing clauses and ensuring contract compliance post-award

• RPA implementation for requisition creation, PO dispatch, and invoice matching
• AI-driven chatbots and virtual assistants for internal user support

Unit 5: Implementation, Governance, and Ethics

• Designing the data infrastructure and API integration required for AI tools
• Best practices for pilot projects, stakeholder alignment, and scaling solutions

• Addressing bias in AI models and ensuring fairness in supplier selection
• Compliance with data privacy regulations (e.g., GDPR) when sharing data for AI training