# AI in procurement and supply chain: which tasks to automate first Updated October 2026 Across the 2.2 million US jobs in procurement and supply chain, the work scores 53 of 100 for AI exposure, weighted by how many people do each job. AI can take on about 952 hours a year per full-time person, 48% of a working year. The task that frees the most hours across the team is to prepare documents, about 141 hours a year for each person who does it. AI exposure, weighted by employment 53 out of 100 High exposure Hours AI can take on 952 hours a year per full-time person Task time AI can do today 32% AI speeds up another 55% ## Tasks to automate first The 15 tasks where AI can take on the most hours across procurement and supply chain: how exposed each task is, how much of the job it takes and how many people do that job. Hours are per person in the role that does the task. [How we score each task](https://www.aigentcy.com/ai-exposure/methodology/). | Task and who does it | AI exposure | | --- | --- | | Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials. Bills of lading and shipping orders are routine, template-driven documents. Done by shipping clerks. | 72% AI can do it 141 hours a year for each person in the role | | Compute amounts, such as space available, shipping, storage, or demurrage charges, using computer or price list. Charge and space calculations are arithmetic against rate tables. Done by shipping clerks. | 75% AI can do it 136 hours a year for each person in the role | | Record shipment data, such as weight, charges, space availability, damages, or discrepancies, for reporting, accounting, or recordkeeping purposes. Logging weights, charges and discrepancies is mostly structured data entry. Done by shipping clerks. | 65% AI can do it 125 hours a year for each person in the role | | Distribute production schedules or work orders to departments. Routing schedules and work orders is mostly digital distribution. Done by production clerks. | 74% AI can do it 129 hours a year for each person in the role | | Compile information, such as production rates and progress, materials inventories, materials used, or customer information, so that status reports can be completed. Pulling production and inventory data into status reports is routine. Done by production clerks. | 82% AI can do it 124 hours a year for each person in the role | ## What stays with people 13% of the task time across these roles needs a person, and the output of the rest still needs someone to check it. The tasks below take the most of that time. - **32%** AI can do it - **55%** AI helps - **13%** Needs a person - Pack, seal, label, or affix postage to prepare materials for shipping, using hand tools, power tools, or postage meter. Shipping clerks, 10% exposed - Deliver or route materials to departments using handtruck, conveyor, or sorting bins. Shipping clerks, 5% exposed - Perform managerial duties such as hiring and training employees and overseeing facility needs or requirements. Logisticians, 15% exposed - Protect and control proprietary materials. Logisticians, 20% exposed - Supervise the activities of workers engaged in receiving, storing, testing, and shipping products or materials. Logistics managers, 15% exposed ## Adoption path Start with the tasks where AI helps a person, then automate the routine ones, then give whole workflows to agents with a person approving the output. 1. ### Quick wins Buyers get supplier research, request-for-quote drafts and contract summaries from an approved assistant. Example: Review documents. AI helps with 58% of the time. [AI governance](https://www.aigentcy.com/services/ai-governance/) 2. ### Workflow automation Purchase orders, invoice matching and stock alerts run as automations between your ERP and your inbox. Example: Prepare documents. AI can do 72% of it. [Process automation](https://www.aigentcy.com/services/process-automation/) 3. ### Agents An agent compares supplier quotes, chases late deliveries and flags price changes for a buyer to approve. 14 of the 15 tasks on this page are ones AI can do end to end, the work an agent takes on. [Private AI](https://www.aigentcy.com/services/private-ai/) ## Roles in procurement and supply chain 16 roles, each linked to its task-by-task score. Of the roles with at least 2% of the jobs, dispatchers score highest at 65, inventory checkers lowest at 30. 1. [Shipping clerks, 37.6% of the team, score **47** out of 100](https://www.aigentcy.com/ai-exposure/shipping-clerks/) 2. [Production clerks, 18% of the team, score **63** out of 100](https://www.aigentcy.com/ai-exposure/production-clerks/) 3. [Logisticians, 11.6% of the team, score **53** out of 100](https://www.aigentcy.com/ai-exposure/logisticians/) 4. [Logistics managers, 10.2% of the team, score **51** out of 100](https://www.aigentcy.com/ai-exposure/logistics-managers/) 5. [Dispatchers, 9.3% of the team, score **65** out of 100](https://www.aigentcy.com/ai-exposure/dispatchers/) 6. [Freight agents, 4.5% of the team, score **49** out of 100](https://www.aigentcy.com/ai-exposure/freight-agents/) 7. [Purchasing managers, 3.9% of the team, score **51** out of 100](https://www.aigentcy.com/ai-exposure/purchasing-managers/) 8. [Procurement clerks, 2.6% of the team, score **65** out of 100](https://www.aigentcy.com/ai-exposure/procurement-clerks/) 9. [Inventory checkers, 2.5% of the team, score **30** out of 100](https://www.aigentcy.com/ai-exposure/inventory-checkers/) 10. [Agricultural buyers, Counted under a related job in US data, score **48** out of 100](https://www.aigentcy.com/ai-exposure/agricultural-buyers/) 11. [Buyers, Counted under a related job in US data, score **51** out of 100](https://www.aigentcy.com/ai-exposure/buyers/) 12. [Freight forwarders, Counted under a related job in US data, score **60** out of 100](https://www.aigentcy.com/ai-exposure/freight-forwarders/) 13. [Logistics analysts, Counted under a related job in US data, score **63** out of 100](https://www.aigentcy.com/ai-exposure/logistics-analysts/) 14. [Logistics engineers, Counted under a related job in US data, score **54** out of 100](https://www.aigentcy.com/ai-exposure/logistics-engineers/) 15. [Purchasing agents, Counted under a related job in US data, score **49** out of 100](https://www.aigentcy.com/ai-exposure/purchasing-agents/) 16. [Supply chain managers, Counted under a related job in US data, score **54** out of 100](https://www.aigentcy.com/ai-exposure/supply-chain-managers/) ## Questions about AI in procurement and supply chain Which tasks should a procurement team automate first? The first task to automate is to prepare documents (72% exposed). Next come the tasks to compute amounts (75%) and to record shipment data (65%). Together they free the most hours across the team. How many hours can AI save a procurement team? About 952 hours a year per full-time person, 48% of a 2,000-hour working year. For a team of 10 that is about 9,520 hours, the time of 4.8 full-time people. The figure is the share of task time AI can remove today, weighted by how many people do each job. Which roles in a procurement team are most exposed to AI? Of the roles with at least 2% of the jobs, dispatchers score highest, at 65 of 100, and inventory checkers lowest, at 30. Each role's page lists its tasks one by one. Will AI replace jobs in procurement and supply chain? AI can do 32% of the task time across these roles today. Another 55% gets faster with AI while a person stays in charge. 13% needs a person. One task that stays with people is to pack, seal, label. Where should a procurement team start with AI? With the quick wins: approved assistants for the tasks AI helps with, under a written AI policy. Then automate the 14 tasks on this page AI can do end to end. An AI audit maps the tasks in your team and ranks them the same way this page does. ## Next steps - [Case study: purchase orders and invoice checks](https://www.aigentcy.com/case-studies/wholesale-procurement-automation/) ### Find the tasks to automate first in your team We map the tasks in your procurement team, score each one the way this page does and build the automations that pay back first. [Book an AI audit](https://www.aigentcy.com/audit/) Occupation data from the O\*NET 30.3 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and modified by Aigentcy. O\*NET® is a trademark of USDOL/ETA. Wage, employment and projection data from the U.S. Bureau of Labor Statistics. EU occupation names from ESCO, European Commission. AI usage signals from the Anthropic Economic Index (CC BY) and Microsoft Research (CC BY 4.0). [Methodology and sources](https://www.aigentcy.com/ai-exposure/methodology/). Scores © Aigentcy. Quote them with a link to the page.