What most AI strategies in finance get dead wrong

But AI is driving efficiency like never before, right? Right — but AI in finance isn’t like marketing or sales. Data is not your only asset; control is. And when AI ignores that, implementations stall or, worse, erode trust.
At Payhawk, we’ve seen the limits of fragmented automation first-hand. That’s why our new Finance Orchestration model is built around higher-freedom, policy-bounded AI: AI Agents that can plan, act, and recover while staying inside audit-ready controls. It’s early days, but the shift is clear: finance teams are asking for reliability, not experimentation.

The tension between hype and accountability runs through every conversation within AI in finance. Beneath the excitement, a handful of persistent myths still distort how leaders think about automation, trust, and scale. They sound plausible, even visionary, but they quietly pull teams away from what really compounds value: Orchestration that balances autonomy with control.

Let’s unpack the five biggest ones:

Myth 1: Agents must be fully autonomous to be useful

Many finance executives quietly imagine that true AI value lies in reaching a “hands-off” state — a controller that closes the books without human touch, or an agent that approves spend automatically. It sounds bold, but it’s the wrong goal.

The biggest returns in finance automation don’t come from full autonomy; they come from what engineers call “intermediate autonomy” — where the system proposes, plans, and executes steps but stays inside the company’s existing control structure. An agent that drafts expense codes, flags anomalies, or routes a purchase for approval can save days of processing time while remaining entirely accountable.

In other words, useful before autonomous. The challenge is not to replace humans, but to free them from mechanical steps while retaining policy-bounded control. Think of it as the difference between a self-driving car and a car with adaptive cruise control. The second is not futuristic; it’s practical, safe, and already on the road.

Myth 2: Trust equals accuracy

Ask a CFO what would make them comfortable delegating real work to an AI system, and they often reply: “When I can trust it.” Press further, and “trust” usually means accuracy — the absence of hallucinations or errors. But in finance, trust has always meant more than that. Accuracy without explainability isn’t control; it’s still risk, just harder to spot.

True trust in finance is procedural. It’s built from traceability (what data was used), transparency (how the conclusion was reached), and recoverability (what happens when something goes wrong). A model that produces a 99% accurate forecast but can’t show its reasoning will never survive an audit.

As AI moves deeper into financial workflows, trust must become an operating capability: designed, measured, and reported like any other performance metric. Just as we have service-level agreements for uptime, finance leaders will soon have trust-level agreements for explainability and oversight. The companies that build those first will move fastest.

Myth 3: Bigger models beat process redesign

The past year has trained every executive to equate model size with progress. Each new release promises more context, better reasoning, and fewer hallucinations. Yet when those models are dropped into messy processes, they behave like expensive interns — eager but confused.

In finance, process quality still determines AI performance. A model can’t fix what the workflow doesn’t define: who approves what, under which policy, using which data. A broken handoff between procurement and accounts payable will remain broken, no matter how large the parameter count.

The most successful teams start not with the model, but with the decision graph — mapping the inputs, checks, and escalation paths that make a financial judgment robust. Once that skeleton is clear, the agent can execute repeatable steps safely. The lesson is counterintuitive in an age of model maximalism: the real frontier isn’t algorithmic, it’s organisational.

Myth 4: Humans become redundant once agents arrive

For all the fear about automation, the reality inside finance functions is the opposite: people are not disappearing; they are being moved up the cognitive ladder. As agents handle routine work, think coding, matching, reconciling, humans are left with the higher-order tasks that AI cannot yet do — exercising judgment, negotiating trade-offs, designing scenarios.

In well-run finance teams, accounts payable specialists are becoming spend control designers; FP&A analysts are turning into scenario strategists; and controllers are emerging as control-system architects who design how autonomous processes operate within policy. The division of labour is changing, but the need for human oversight is deepening.

The more powerful the system, the more valuable the human context becomes. And keeping the human in the loop isn’t optional — it’s what ensures that automation serves strategy, not the other way around. The greatest irony is that the more technology has entered finance, the more human the function has become.

Myth 5: The goal of an AI strategy is efficiency

Efficiency is the easiest story to sell — faster reconciliations, fewer manual entries, lower headcount. But it’s also the least transformative.

Finance has chased efficiency for decades; what AI makes possible now is decision advantage: acting faster, with more foresight, and less risk.

The real leverage of intelligent systems lies not in cutting time, but in expanding capacity for judgment. When agents surface anomalies before they become exceptions, when forecasts update themselves after every transaction, or when policy violations are caught in real time, finance gains the space to think — not just process.

That’s why the most advanced teams measure AI progress in decision quality, not cost per transaction. They ask: Are we seeing problems sooner? Are we making choices with more context? Are we freeing people to focus on what truly requires human judgment?

The key measure of AI maturity isn’t how much headcount it saves, but how much human judgment capacity it creates.

From demos to discipline

Most finance teams aren’t lacking in technology; they’re navigating how to apply it with discipline. The challenge now isn’t just building automations but governing how those systems make and escalate decisions.

The next wave of progress will depend less on bigger models and more on better governance: defining what good looks like, measuring how systems behave, and designing them to fail safely.

That is what orchestration really means. It’s not automation without humans; it’s automation that knows when to involve them. Only then will finance stop treating AI as a novelty and start running it as infrastructure.

What’s likely to matter most in finance going forward is how intelligence and control mature together, i.e. how systems not only act but learn to respect the boundaries of their action.

Learn more about how Payhawk’s AI agents help orchestrate finance with control and simplicity.