Managing an AI agent isn't using a tool — it's supervising labor. Most companies skipped that step. In Procurify's recent survey of finance leaders, 35% said trust — not model capability — is the single biggest factor in whether their organization can actually deploy agents. The teams already shipping report 63% ROI from time savings and 60% from improved data accuracy, but only after they did the unglamorous work first: defined the operating model, baked in governance and audit trails, and consolidated their data into a single source of truth. Frontier models keep commoditizing generic intelligence. The value is moving up the stack — to the workflow, the context, and the data your company actually runs on. Procurement has sat in the middle of every enterprise's audit trail for decades — budgets, contracts, suppliers, approvals, compliance, payments. It's the use case AI vendors have been quietly building toward, because if you can make procurement feel less clunky, you've solved governance for the rest of the business. We sat down with Procurify's Chief Product & Technology Officer Jonathan Su to understand what an AI-native operating model actually looks like, why production-grade is now ten times harder than prototype, and what shifts when the bottleneck in your team moves from execution to judgment. In this episode: Why 35% of finance leaders say trust — not model capability — is the biggest factor in whether agents actually shipThe operating model most companies skip: governance, audit trail, single source of truth — before the agent touches workWhat AI ROI actually looks like — 63% time savings, 60% better data accuracy, plus the business KPIs that prove itWhy value is moving up the stack as frontier models commoditize generic intelligence — workflow, context, data, distributionHow procurement teams redesign workflows around agents instead of tacking AI on top of an already broken processThe hire that beats 20 years of experience: grit, taste, judgment, and the ability to learn in 4-month cycles "Managing an agent is more than just using a tool. It's sort of like supervising labor." — Jonathan, Procurify "The cost of producing something is dramatically lower, but the bottleneck shifts to judgment, craftsmanship, and taste. Just because you could do something doesn't mean you should." — Jonathan, Procurify We built productimpactpod.com to be your AI product insights and strategic playbook hub. Check it out. Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us. About Jonathan: Jonathan is Chief Product Officer at Procurify, where he leads product strategy and AI initiatives across the company's spend management platform. He has spent his career in payments, fintech, and enterprise software, and now leads Procurify's transition to an AI-native product organization. Procurify serves finance teams managing budgets, approvals, invoicing, and payments — the workflows where governance and AI agents have to coexist. Procurify: https://www.procurify.comJonathan on LinkedIn: https://www.linkedin.com/in/jonathanhaosu Hosted by: Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/ Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com This episode was brought to you by: PH1 (https://ph1.ca) — a strategy & research consultancy specialized in delivering evidence about the highest value use cases and customer profiles. AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
