The Future of Procurement is Predictive: How AI is Transforming Procurement Processes

In today's fast-paced and highly competitive business environment, procurement teams face enormous pressure to deliver cost savings, manage risks, and account for factors previously unheard of, such as ESG considerations. However, traditional procurement methods are often reactive and lack the agility needed to keep up with changing market dynamics. This is where artificial intelligence (AI) can make a significant difference. By harnessing the power of AI, procurement teams can transition from reactive to proactive procurement, making informed decisions based on data-driven insights. In this article, we will explore the ways in which AI is revolutionizing procurement processes.

From Analysis to Category Strategies - Enhancing Strategic Procurement Processes with AI

Procurement involves several crucial activities, and spend analysis is undoubtedly one of the most critical among them. Along with purchase plans for the next period and supplier market analysis, spend analysis forms the foundation of procurement category strategy. However, the traditional process of spend analysis can be time-consuming and prone to errors, making it a challenging task for procurement teams.

Fortunately, machine learning and AI can help automate this process by analysing vast amounts of data from multiple sources. This includes historical spend data, existing contracts, production plans, supplier performance statistics, and supplier market insights. By analysing this data, organisations can identify future spending patterns and prepare strategic assumptions for the best organisation of procurement activities. This can lead to efficient cost organisation and risk mitigation, ultimately resulting in significant cost savings for the organisation. Moreover, if an organisation aims to manage ESG aspects with their supply base, AI can suggest the best approach by considering ESG criteria as well. This can help organisations plan their procurement activities in advance, enabling them to negotiate better deals with suppliers and improve their ESG performance.

After conducting a thorough analysis and gathering the necessary data, AI can assist in developing the procurement category strategy. Building on the foundation of previous analytical work, AI can take into account a poll of additional important factors, including: the category's position on the Kraljic matrix, the mix of products within the category, each product's cost structure, risk assessments, company preferences regarding individual suppliers, and the benefits and drawbacks of changing suppliers. Having processed this information, AI can suggest optimisation levers to be used to organise spend and predict the outcome.

Once the category strategy is verified, fine-tuned, and confirmed by the category manager, tenders that align with the strategy are structured and prepared for execution. If the strategy assumes the inclusion of new suppliers in tenders, AI can also help to identify them. The quotes received from these suppliers can then be simulated to determine where the price is likely to end up, providing valuable insights to the procurement team.

From Specifications to Contracts - Enhancing Tactical Procurement Processes with AI

Following the previously discussed improvements, machine learning and AI can also automate the process of generating and advancing specifications for requests for quotations. By suggesting which parameters to quote and weighting them, AI can help organisations to their requests for quotations more precisely, aiming at more accurate and competitive bids.

When it comes to supplier selection, AI can play a key role in identifying the best suppliers for a company's specific needs. By analysing various business considerations, machine learning models can recommend suppliers that offer the best value for money and even suggest prices for suppliers to bid on based on historical pricing or market research. This not only shortens the cycle time for sourcing projects but also ensures that the final price paid is fair and competitive.

Furthermore, AI can recommend the best negotiation scenario, including the agenda, sequence of points to be negotiated, arguments to be used, expected counterarguments, suggested discussion tactics, and in consequence the Most Desirable Outcome (MDO), Least Acceptable Agreement (LAA), and Best Alternative to Negotiated Agreement (BATNA). This can help category managers negotiate more effectively and achieve better outcomes.

Machine learning and AI can help automate the process of contract management as well. By analysing contracts and identifying key terms and clauses, AI can help ensure compliance with contractual obligations, reduce legal risks, and identify opportunities for cost savings. AI can also suggest actions to be taken when a contract ends or is terminated, such as recontacting the related purchase volume to other suppliers or tendering or refreshing the old contract with new conditions, depending on the cause of the contract end and business circumstances.

Certainly, it should be reminded that all the tactical procurement activities should be aligned with the procurement category strategies. This means that the selected suppliers and managed contracts related to a specific category of procurement should be consistent with the overall goals and objectives of the strategy developed for this category.

From Purchase Orders to Invoices - Enhancing Operational Procurement Processes with AI

The operational procurement processes are an area where machine learning has already delivered numerous benefits to large companies by handling vast amounts of data. However, there is still significant potential for expansion, particularly in the area of predictive capabilities.

Implementing artificial intelligence in procurement dashboards can enhance ongoing spend monitoring by providing specific insights and robust drill-down capabilities with predictive nature. This allows users to understand every relevant transaction and pinpoint where problems exist, not only when everything goes according to plan but also when significant disturbances arise from the supplier market or altered production plans. With the help of AI, procurement organisations can identify potential risks, mitigate them, and spend more effectively on an ongoing basis.

In addition to that, machine learning and AI can simultaneously benefit organisations in detecting fraudulent behaviour. By analysing transactional data, these technologies can identify indicators of fraudulent activity, such as duplicate invoices or unauthorized purchases, enabling organisations to prevent frauds and protect their financial well-being.

The implementation of artificial intelligence in procurement is rapidly transforming the way procurement organisations manage processes in this field. Through the utilization of AI technology, procurement teams can achieve greater agility and precision in decision-making related to spend, suppliers, and risks, even for tasks that were previously unmanageable due to the inability to process numerous factors combined with vast data sets. However, like any new technology, AI adoption in procurement presents challenges similar to those faced in other business areas. To successfully implement AI, organisations must focus on establishing a robust data foundation and creating adequate governance frameworks. Nevertheless, looking ahead, it is clear that the future of procurement is predictive, and AI will continue to play a crucial role in helping organisations attain their procurement objectives.