AI Agents in Finance: How to Build the Operating Foundation That Delivers ROI

There’s no shortage of conversation happening in finance circles about agentic AI that can route approvals, flag inconsistencies and act on organizational context, without human prompts. It’s an exciting time, but it’s also coming faster than many mid-market teams are planning for, which means results aren’t always rising in tandem with adoption.

That gap isn’t easily explained by which model a company chose or implementation costs. It comes down to the operating environment those agents were deployed into, and how few organizations set it up for success t before they started.

Procurify Chief Product and Technology Officer Jonathan Su has watched this pattern closely. In his conversation on the Product Impact Podcast, he described what separates the mid-market organizations seeing real returns.

“What’s good for agents is also what’s good for humans,” Su said. But the foundation comes first, which includes

• Building a single source of truth for spend data
• Documenting workflows consistently
• Embedding governance into the infrastructure from day one
• Structuring intake at the point of request

How to prepare finance operations for AI agents

1. A single source of truth for spend data

AI agents act on the data available to them. When purchasing data lives across multiple systems, email threads, and spreadsheets with no authoritative record, agents have nothing reliable to work from. Consolidating procurement software data into one governed system is the starting point.

2. Documented, consistent process workflows

Most mid-market finance teams operate with processes that have never been formally documented, or that have multiple informal versions depending on who handles a request. AI agents require defined, repeatable workflows. Documenting them through a structured procurement process often surfaces inconsistencies that were always present but invisible.

3. Governance and audit infrastructure built into the workflow

Every purchase request, approval decision, and exception needs to create a record in the system at the point it occurs, not reconstructed after the fact. That record is what makes AI-assisted decisions defensible to an auditor, a board, or a CFO reviewing exceptions.

4. Structured intake at the point of request

Intake is where the quality of downstream data is determined. When a purchase requisition captures the right information consistently, what is being requested, what budget it draws from, what approval path applies, and what policy governs it, every step downstream operates from a reliable shared context. When intake is informal, that context is lost before the process begins.

None of these requirements are unique to AI. They describe what good finance operations look like for the humans navigating them today.

Why AI agents fail without organizational change

Su described it directly: “The workforce was conditioned to hoard information. Gatekeeping was how you stayed relevant. AI requires the opposite: open data, consolidated context, a single source of truth.”

For decades, procurement was treated as an administrative function, not a strategic one. Process knowledge accumulated in individuals instead of systems because that was how influence worked. The person who knew which supplier could deliver in 48 hours, which approval could be escalated, which exceptions had been granted before, that person was valuable precisely because no system held what they knew.

The 2025 Deloitte Global CPO Survey found that even the highest-performing procurement organizations spend roughly two thirds of their time on non-strategic, transactional work, which signals an operating model still built around manual coordination rather than shared, systemized intelligence. According to McKinsey, companies that lead on digital and AI deliver up to 6x more value to their shareholders than those that do not, and that pattern holds across every industry they studied.

What separates those companies is not the technology they chose. It is the organizational foundation they built before the technology arrived. AI does not create that foundation. It amplifies whatever is already there.

So when an AI agent fails, the cause is frequently organizational: AI working exactly as designed inside an organization that has not changed how knowledge, data, decisions, and accountability flow.

Why velocity is the wrong measure for AI ROI in finance

Velocity is the most common way to measure AI ROI in finance and the least useful indicator of whether the technology is creating real value.

“At the end of the day, I would argue it’s still about your core business KPIs,” argued Su. “If you are actually producing quality output that helps move the needle, you should see that in your core business metrics.”

Procurify’s AI Readiness in Finance Report confirms this among finance leaders surveyed: 63% cite time savings and faster workflows as an early AI benefit, and 60% cite improved data accuracy. Notably, data accuracy ranked second, not first. That matters because it’s downstream of whether the underlying process and data are structured well enough for AI to act on reliably. An organization can automate quickly and still produce inaccurate data if the foundation is weak.

The table below shows how that distinction plays out in practice, drawing on the outcome metrics outlined by Su and what Procurify captures to enable each:

CFOs evaluating AI procurement vendors should look beyond throughput metrics and ask the questions that reveal whether the foundation is ready:

• How do AI agents catch budget overruns before month-end close?
• How does AI improve procurement decision quality, not just processing speed?
• What should an AI-ready procurement audit trail actually include?

What separates an AI demo from a production-ready finance system

“It’s never been easier to build, but it’s harder than ever to build production ready,” Su pointed out.

And it’s true: while prototyping costs have collapsed, it’s not any easier to build the governance architecture, audit infrastructure, and control logic that makes a system trustworthy at scale.

“There’s a huge difference between internal tooling or prototypes for market tests versus something that you want to put your brand behind,” Su said. “A production-grade, enterprise-grade system.”

The features that appear in a vendor demo are often straightforward to produce. The audit trail, permission structure, exception handling, and human interception points that make those features safe in a finance environment are not. Governance gaps tend to appear at the worst possible moment: during an audit, a budget reconciliation, or an exception the system was never designed to handle.

Deloitte CPO Survey reinforces this from the buyer side: data quality and governance and security concerns rank as the top two internal risks CPOs identify when implementing AI, above integration complexity, above talent gaps, above everything else.

Su described what separates the vendors that earn trust: “The ones that win will be the ones that can cut through the noise around what is the actual business value you’re providing, what is the outcome you can deliver, what is that core problem.” For CFOs, that translates to a specific evaluation question: is the AI capability being shown built on a governance architecture, or added to one after the fact?

Su sees Procurify’s approach as “designing with trust and governance from day one. Making sure [we] have a clear governance layer baked into [our] architecture and platform, and that there’s a clear audit trail and ways for humans to interject, and guardrails to make sure that the agents don’t do something out of line.”

The difference isn’t visible in a demo. It becomes visible when exceptions occur, when compliance requirements kick in, or when the organization needs to adjust how the system behaves.

Procurement infrastructure is critical to your AI strategy

For finance functions that have done the cultural work and are measuring the right outcomes, the procurement platform decision and the AI readiness decision converge. They are the same decision.

Intake is where this shows up most clearly. When every purchase request enters the system through a structured, policy-enforced flow, the commitment is captured at the point of request, not at the point of invoice. That means budget variance surfaces before month-end, not after. Exceptions get flagged with enough context for a human decision rather than discovered in a reconciliation.

The AI agent working downstream has a reliable record to act on because the operating model upstream was designed to produce one. That is what agentic procurement looks like when it functions as designed: AI operating on a foundation purpose-built to support it.

“The value is actually moving up the chain,” Su said. “It’s in the application, it’s in the context, it’s in the workflow, it’s in the data.”

Generic AI intelligence is being commoditized. What cannot be commoditized is the clean, governed procurement data that a well-structured finance operation produces every day. The compounding advantage is organizational, not technical, and the leaders pulling ahead started building it before it felt urgent.

See how Procurify’s AI-native procurement platform is built for the operating model AI agents require. Take a product tour.