Although in real-world application AI has so far been mostly limited in its scope to specific tasks based on predefined algorithms, within procurement, Gen AI is rapidly standing out as a key solution to enable better decision making, transparency and efficiency, right across the Source-to-Pay chain.
More specifically, in the world of procurement, AI is making an impact across three main business areas; these can be described as insights, documentation and collaboration.
For procurement to be effective and meet the many requirements of a business that may range from cost-effectiveness to sustainability and inclusivity, a great many insights are required. These insights may come from within the business or from the market and may take on many forms and formats making it very difficult for the human mind, and indeed even sophisticated processors, to integrate analyse and interpret.
AI’s speed and accuracy mean that it is now possible for procurement to draw on an unparallel breadth of data sources and provide real-time analysis to identify risk, manage supplier performance and predict potential disruptions that might lie ahead in the supply chain. AI can also support procurement and category managers with alternatives when these disruptions do occur or with ways to engage and improve relations with poorly performing suppliers.
Gen AI’s potential
Much of Gen AI’s potential lies in its ability to process and analyse vast amounts of data at speed. This in turn translates into the automation of many repetitive and low-value manual tasks that do nothing to help retain talent and actually cost the business a pretty penny. AI can support procurement with tasks such as bid comparisons, summaries, the creation of RFIs, RFQs and SOWs, even making suggestions for more successful negotiations. AI can in fact also help improve relationships with suppliers and the market by supporting collaboration through automatic alerting and compiling of necessary- but burdensome- documentation.
Gen AI can also streamline internal collaboration between procurement teams, internal stakeholders and suppliers, supporting, for example, management of defect and non-conformities, warehouse and inventory optimization as well as a range of other functions.
Successful AI implementation will rely on planning: starting an AI implementation with a clear roadmap is critical to sustainably accelerating progress throughout the business. In procurement, as with other functions, it is key to take an analytical approach that relies on a thorough review of which business processes could benefit from automation in the short term. These could be processes that are ripe for automation because they are simple but time consuming, or areas that need urgent improvement.
Equally important is creating the necessary internal conditions for the implementation to progress smoothly. Amongst others, these include data integration, risk tolerances definition, change management processes, cost and ROI evaluation.
Appointing an internal AI team lead early on in the process helps ensure that projects are steered effectively and ensure that there is a clear line of reporting once the system is up and running. The lead will help ensure that monitoring is ongoing and that outcomes are refined on an ongoing basis. In fact, AI is only as good as its data input, and it may emerge over time that data sources have to be tweaked.
Once key preparatory considerations have been addressed, it is possible for the procurement function to fully leverage AI in at least the following four areas of application:
1. AI-enabled supplier sourcing
Companies need to select the best suppliers to remain competitive and continue to deliver their best-in-class products or services. To achieve this, supplier selection based solely on cost competitiveness is clearly inefficient. Procurement software should provide users access to a vast global network of verified suppliers, while also aiding in decision-making through AI-driven recommendations that draw on a wealth of historical performance data, risk analysis, cost considerations, service levels, and more. Businesses can thus perform in just a few minutes the analysis that would take humans weeks to complete, while also receiving ever more tailored suggestions as the model learns their preferences.
2. Intelligent contract management
Managing a large number of contracts can be challenging, especially for companies that still use manual processes or have a vast supplier network. A way to simplify this challenge lies in leveraging advanced software solutions powered by AI and ML. These technologies allow for rapid extraction and analysis of contract data from various sources, significantly reducing the time spent on handling large volumes of documents and datasets. Key metadata and clauses can be thoroughly reviewed and automatically extracted in seconds. Additionally, the integration of generative AI enhances contract management by enabling quick summarization, drafting alternative clauses, and conducting risk assessments, ultimately streamlining and accelerating the entire contract creation and approval process.
3. Dynamic risk management
As the global supply chain continues to face unexpected and often dramatic obstacles such as the disruptions caused by the Red Sea crisis, the war in Sudan or the rail strikes in Canada, businesses that are able to manage risk more effectively than others will become increasingly competitive. AI can support businesses that need alternative routes and suppliers in case of a crisis by offering a comprehensive supplier management that facilitates risk prevention. It is in fact possible to evaluate supplier-related data in combination with data from end-to-end risk management software. This integration can provide companies real-time alerts on supply chain disruptions, while in-depth supplier analysis can support decision makers with rapid supplier alternatives.
4. Ethical requirements
Compliance with ESG standards and increasingly stringent sustainability requirements has also made it paramount for businesses to carefully assess their suppliers on data measuring inclusivity, diversity and carbon emissions to name a few. These combine with traditional metrics like supplier performance, quality, pricing, and delivery timelines to make an increasingly complex set of criteria for new procurement bids. Gen AI can help to swiftly and easily integrate ESG performance metrics sourced and refined from diverse information channels into decision making.
A vital tool
AI tools are clearly becoming increasingly vital for companies across various sectors. In procurement, these tools offer immense potential by driving process automation, enhancing agility, and preparing businesses to respond to emerging market requirements. Companies that promptly update their processes and strategically integrate AI will reap important efficiency gains, cost and time savings, and stronger competitive edge.
The key differentiator, however, will be how well companies align AI technology with their core business objectives; jumping on the AI bandwagon without a plan simply won’t cut the mustard. Organisations lacking a clear vision for AI implementation will struggle to keep up and will miss out on new levels of decision-making quality and analytical clarity available to strategic planners.
Author details: Simon Thompson, VP Sales Northern Europe at JAGGAER
