13. Why Managing AI Agents Is More Like Supervising Labor Than Using a Tool [Jonathan Su, Procurify]
episode
Product Impact Podcast | Secrets to unlocking the value of AI
30 min
2 speakers
4 chapters
transcribed 1 month ago
Transcript
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Transcript generated automatically by AI and may contain errors.
Why is managing an AI agent more like supervising labor than using a tool?
Welcome to the Product Impact Podcast. Our guest is Jonathan Su, Procurify's Chief Product and Technology Officer. In 2026, 97% of enterprises have deployed AI agents, but only 29% report significant ROI. Procurify's chief product officer, Jonathan Sue, has been inside that gap for the past year. And his read is that the problem isn't the model and it isn't the team. It's that most companies design their deployment around tasks instead of outcomes.
The deeper issue in here is structural. For decades, companies built layers of people to manage how information flows, track decisions, and move work through approval chains. That's exactly the work that agents are now being asked to absorb. And it's challenging when structures go directly against the foundation of these models. The workforce themselves, they were conditioned to hoard information. Gatekeeping was how you stayed relevant. AI requires the opposite open data, consolidated context, a single source of truth. So these teams redesigning around that shift aren't the ones with the best models. They're the ones that did the cultural reset before they touched the technology. And they're pulling ahead first because they need to understand the human layer before anything else.
So our guest today is in the middle of it. Jonathan Sue is the chief product officer at Procurify, the platform that thousands of finance teams use to approve, pay, and audit every dollar that moves through the business. He has a background in fintech and payments and is now leading Procurify's full shift to AI native product development.
In this conversation, we get into what an operating model designed around business outcomes looks like instead of tasks. How to measure AI ROI beyond the velocity numbers everyone defaults to. Why production grade is now 10 times harder than prototyping, even after the vibe coding made prototypes 10 times easier. The fact is, it's never been easier to build, but it's harder than ever to build production ready. And lastly, why your team's ability to adapt every four months now matters more than any software credential they hold. Here's our conversation.
Hi Jonathan, thank you so much for joining today. So Is the workforce actually ready to manage AI agents or are we skipping a step? Like managing an agent is fundamentally different from using a tool.
Yeah, I think you're absolutely right. Managing agents are different. You can think about what agents can do. They typically start with an intent or a goal and they can actually take actions. Interact with different systems, make decisions. And so much more than using a tool, it's sort of like supervising labor in a sense. And I do think many organizations are skipping a step. There's a lot of hype and there's a lot of push and Everyone is rushing to deploy agents, but oftentimes we think companies actually need to take a beat and focus on some of the basics first. That requires actually more than just experimentation. That typically means uh defining a proper operating model and My operating model, uh I'm talking about things like trust, governance, and control, uh, and making sure you have clear sense of data and context.
that are actually relevant for what the agents are designed to do. We've done surveys in the market with finance leaders on AI readiness and and we found in our survey that thirty five percent of the professionals actually say trust is the biggest factor in terms of I ready. And if you think about what companies actually need, right? They also need audit trail, visibility. And make sure that exceptions, permissions, and data access are appropriately governed. So our recommendation really is you start with the foundation, defining the right process and operating model, uh, and then designing the data and context that's unique to your organization for the purpose use case that you're really setting out to solve in the first place.
And and it often turns out that what's good for humans are what's good for agents. So it means making sure you have one set of sort of truth and a clear process definition.
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Chapters
4 chapters
1
Why is managing an AI agent more like supervising labor than using a tool?
0:00–17:38
2
Why do 35% of finance leaders say trust is the biggest barrier to AI agent adoption?
17:38–20:08
3
What operating‑model foundations (governance, audit trail, single source of truth) must be set before agents touch work?
20:08–22:51
4
How does the shift from information hoarding to open, consolidated data affect AI success?
22:51–30:09
Speakers
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