Ardent Partners’ 17th annual State of Procurement report — CPO Rising 2022: The Data Revolution — is coming soon and I want to thank the 350+ procurement leaders who’ve taken our annual survey. I can’t believe that writing this report is something I have done for 17 years and running. To keep it interesting (for me the author and you the reader), we’re introducing an entirely new and modern style for this report. Watch this space for more details on the report’s final timing.
Ahead of the new report here are some Use Cases for Data and Intelligent Procurement.
Despite AI’s challenges and risks, there is ample potential to deliver significant, game-changing value to procurement organizations everywhere. Here are a few potential use cases.
- AI for Spend Analysis: With intelligent procurement systems, end users could conduct spend analysis via NLP, speaking directly to the system and requesting a data pull directly from back-end systems, rather than from a consolidated data pool. The system would then provide a detailed spend analysis report – in written or spoken form – with parameters and header-level information based on past user requests and data processing behaviors. In this way, users could bypass the traditional spend analysis process as a sub-component of strategic sourcing and have the data directly inform sourcing or procurement activities outside of a formal sourcing event.
- Guided Requisition to Purchase Order: Intelligent procurement tools could also guide end users through the requisition-to-PO process in a more elegant manner based on their role and past behavior. Not everyone in the procure-to-pay (P2P) chain has the same responsibilities, permissions, and needs, nor is everyone familiar with the tools. An AI-enabled P2P tool could take everything it knows about the end user (e.g., job title and role, business need, technical proficiency, etc.) and use that knowledge to shepherd the end user through the requisition-to-PO process. The result could be a faster, seamless process that results in a quicker buy and efficient payment process.
- Intelligent Category and Supplier Optimization: AI could transform the way that sourcing and procurement teams optimize their categories and suppliers. Intelligent procurement systems could take different data feeds from internal sources, like supplier management and accounts payable/finance, track sourcing, ordering, and buying behaviors, and perform category and supplier rationalization. Based on the patterns it detects and the efficiencies and enhancements it uncovers, the system could then optimize categories and suppliers in the background while end users manage the supplier relationships and risks out front.
- Predictive Purchasing: Like intelligent spend analysis, AI could be used to analyze third-party data feeds, like market intelligence and risk reports or social media feeds, and predict market behaviors. Based on those predictions, intelligent procurement systems could provide end users with assorted recommendations. End users could also empower these intelligent systems to make automated purchases based on likely near-term events. End users would retain ultimate control and the flexibility to conduct spot buys, but could also allow an intelligent procurement tool to automatically purchase routine goods and services that are of a lower risk profile.
- Intelligent Supply Risk Management: Procurement practitioners could take a similarly intelligent approach to supply risk management. Intelligent risk analysis and management programs could automatically collect and analyze internal and external data streams and provide instant, “in-context” alerting, reporting, and recommend-ations for risk avoidance or mitigation. The programs, themselves, could be programmed to take precautionary actions vis-à-vis sudden or developing risks, like calling up second- or third-tier suppliers, suggesting alternative categories or routes, or supporting predictive purchasing based on expected market conditions.
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