Enterprises are building dedicated AI governance roles. Most scale-ups are deploying agents without anyone formally in charge of questioning them.
A concept called the "FP&A Agent Manager" is gaining traction in enterprise finance circles.
The idea, documented in joint research from Harvard Business School and Salesforce, is that as AI agents take over more of the analytical work in FP&A — forecasting, variance analysis, scenario modelling — someone needs to own the responsibility of interrogating those outputs. Not building the model. Not maintaining the infrastructure. Specifically: questioning whether the agent's assumptions still hold against commercial reality, and stopping it when they don't.
The research argues that the most effective people for this role come from domain expertise, not technical backgrounds. Finance people, not engineers.
It's a compelling argument.
It's also designed for companies with the headcount to create a new function.
When a large enterprise reads about the FP&A Agent Manager role, the conversation goes something like: who do we put in this position, and what does the job description look like?
When a scale-up reads the same thing, the honest answer is: we don't have that person. We have one Head of Finance, possibly a financial analyst, and a fractional CFO partner we call once a week.
That's not a criticism. It's the reality of building a finance function at pace.
But the governance need doesn't disappear because the headcount isn't there.
In fact, for scale-ups, the risk is higher — not lower. Fewer people means fewer informal checks. No one is going to walk past a colleague's screen and notice something looks off. There's no second analyst to cross-reference against. The AI agent produces a clean, fast, well-formatted output, and it goes directly into the board deck.
In 2021, Zillow shut down its algorithmic home-buying business and took over $500 million in write-downs.
The pricing model kept bidding aggressively as the housing market turned — operating on assumptions about resale value that were no longer valid. Nobody whose explicit job it was to interrogate those assumptions intervened in time.
Zillow was not a small company. They had analysts. They had data teams. What they lacked was a clearly defined responsibility to challenge the model when commercial reality started diverging from its assumptions.
Scale-ups have less buffer than Zillow did.
An AI agent running variance analysis or producing a forecast for a board presentation will not pause because something changed in the market last month. It will not hesitate because the revenue recognition logic shifted after the last pricing update. It will produce a result — fast, formatted, and stated with complete confidence — based on whatever it was given.
The output will look more credible than a spreadsheet. It won't necessarily be more accurate.
The enterprise version of this problem is a job description.
The scale-up version is a question of design: which existing person owns this, and have they been told?
In most scale-ups, the right answer is the Head of Finance — or, where one is in place, a fractional CFO with the domain expertise to act as that critical layer. Not someone who understands how the model works internally, but someone with enough commercial and financial context to know when an output doesn't make sense — and the authority to push back on it.
For scale-ups that don't yet have that person internally, this is exactly the kind of responsibility a fractional CFO partner can absorb from day one — without the cost of a full-time hire and without leaving the governance gap open while you scale.
The role requires four things, none of which are new to an experienced finance professional:
None of this requires a new hire. It requires a clear assignment of responsibility before the agent goes live — not after.
In our first article, we mapped the data quality problem that most scale-ups carry into their growth phase: fragmented financial logic, inconsistent KPI definitions, metrics that mean one thing in Finance and something else in Operations.
In the follow-up, we connected that to agentic AI: the same infrastructure gaps that create reporting friction in a human-operated finance function become risk multipliers when an agent inherits them. The agent doesn't hesitate. It acts.
The Financial Single Source of Truth is the data layer — the foundation the agent inherits on day one.
The governance layer is what happens above it: someone whose explicit responsibility is to challenge what the agent produces, catch when its assumptions have drifted from reality, and decide when to stop trusting the output.
Most scale-ups preparing for AI adoption have started thinking about the data layer. Very few have thought about who owns the governance layer.
That's the gap that tends to close itself — usually at a moment when it's already too late to design it properly.
Before any AI agent goes live in your finance function, one question needs a clear answer:
Who is responsible for questioning this output, and do they have the authority to act on what they find?
Not "who built it." Not "who maintains the infrastructure." Who challenges it.
In a scale-up, that person probably already exists. They just haven't been told that this is now part of their job.
The companies that get agentic AI right in FP&A won't be the ones with the most sophisticated models. They'll be the ones that decided, before anything was deployed, exactly who was responsible for thinking critically about what the model produces.
That decision is not a technical one. It's a design choice. And it needs to happen before the agent is running, not after the first board deck goes out with a number nobody can explain.
If you don't have someone whose job it is to challenge your AI outputs — or if you're not sure whether that gap exists — that's usually the first signal that it does.
We work with a CFA-qualified fractional CFO partner who can take on exactly this responsibility. If it's relevant to your context, we're happy to explore what that looks like.
Discuss Your Context ↗