Boring AI is better AI for finance: The killer feature of finance AI agents isn’t autonomy, it’s determinism

The tech world is buzzing about fully autonomous AI “coworkers” that can handle any task. But finance leaders have a different priority. They need AI they can trust, not AI that tries to do it all.

In practice, predictability beats autonomy. As AI expert Jiquan Ngiam puts it, “reliable, deterministic systems will be way more desirable” than flashy agents that aim to “do it all”.

In other words, an AI that consistently executes a few tasks flawlessly is far more valuable than one that theoretically handles many tasks but erratically. And, when it comes to corporate finance, boring precision wins over unpredictable creativity. Boring AI is better AI for finance.

CFOs fear hallucinations more than missing features

No group understands the cost of inconsistency better than Chief Financial Officers. Modern generative AI can hallucinate, meaning it produces incorrect or fabricated information, and even a small error in financial output can have major consequences.

CFOs are, therefore, justifiably paranoid about any AI system that might “make things up” or vary its answers. They don’t lose sleep worrying that an AI isn’t autonomous enough; they worry it won’t be accurate and repeatable enough.

One of AI’s biggest challenges in finance is the trust and accuracy problem: A tool that occasionally outputs nonsense or different results on Monday vs. Tuesday is a non-starter. Consistency isn’t just a technical preference — it’s an adoption blocker. Finance teams demand outputs they can audit, explain, and rely on every time.

As one industry analysis noted, compliance teams favour deterministic AI models that produce “clear, structured and repeatable outputs” which auditors can verify. In short, if an AI’s answers can’t be predicted and justified, CFOs won’t put it anywhere near the books.

Why deterministic = better finance AI

At its core, the new wave of large language model (LLM) AI is probabilistic, not deterministic.

Ask an AI like ChatGPT the same question twice, and you might get two different responses. That non-determinism may be fine for a chatty assistant, but it’s unacceptable in financial operations.

CFOs are used to software that behaves predictably — given the same inputs, it produces the same outputs every time. They expect nothing less from AI.
Ngiam goes on to draw a parallel between a car and unreliable AI agents, saying:

If your car only works five out of seven days… you’re not going to buy that car!

In finance, AI that works 100% of the time on a narrow task is infinitely more useful than one that works 80% of the time on a broad task. High accuracy (think >90%, ideally 100% on defined processes) isn’t just a nice-to-have; it’s mandatory.

Crucially, traditional rule-based systems were deterministic — they never deviated unless explicitly reprogrammed. AI developers in finance are now finding ways to combine the flexibility of AI with the guardrails of deterministic logic. For example, hybrid approaches and “explainable AI” techniques are emerging to ensure that you can trace and verify every AI’s decision.

The bottom line? An AI agent that is boringly consistent will beat a “creative” one in the finance department every time.

Narrow focus, high ROI

There’s a reason many CFOs are investing in narrow AI use cases first. The best bang-for-buck often comes from automating highly repeatable, well-defined tasks — the kind that make finance teams feel like robots, but which robots (or AI) can do extremely well.

Consider accounts payable processing: A mundane yet critical process. AI can now draft vendor bills and even predict the correct general ledger codes for each invoice, turning what used to be hours of manual coding into a near-touchless experience. That’s not sci-fi AGI; it’s “boring” AI fix to a boring task, and it saves companies countless hours.

Similarly, expense management tools now use AI to automatically categorise transactions and flag anomalies with well over 90% accuracy. These capabilities are constrained, but they’re delivering ROI today.

In fact, Lutra AI (an AI workflow startup) describes the current state of AI agents as Level 1: “existing capabilities with high reliability.” These Level 1 agents stick to “highly prescribed workflows” with no autonomous decision-making, and they “follow a set, repeatable structure”. Translation: they do exactly what they’re designed to do, and they do it every time. That might sound underwhelming compared to science-fiction AI, but for CFOs, it’s music to the ears – because it means the technology actually works consistently.

Unsurprisingly, these kinds of deterministic, narrow AI solutions are where companies are seeing immediate returns. Why? Because when you eliminate even a single tedious process (say, manual invoice matching) and you do it reliably, the value is concrete and measurable. As a result, finance leaders are prioritising “high-ROI wins” in these narrow domains before even thinking about chasing full AI autonomy.

Full autonomy = Research-grade (for now)

It’s important to separate the hype from reality. Yes, the idea of an AI coworker handling your quarterly close or independently optimising your budget sounds amazing. But the reality is that we’re not there yet when it comes to leaving finance to the bots.

Even the most advanced “agentic AI” prototypes today — think of experiments like AutoGPT that made headlines — are fragile, unpredictable, and expensive to run at scale.

As one report noted, few companies can rely on these autonomous agents in production “without heavy oversight”. Jiquan Ngiam flatly calls fully autonomous agents “research-grade” – more of a concept than a usable product. In other words, the self-directed AI finance assistant remains in the lab, not the boardroom.

Trying to leap to Level 3 autonomy (the kind that can “make decisions, click around UIs, and respond dynamically” without errors) is currently a recipe for frustration at best and disaster at worst. There’s a growing recognition that the holy grail of a do-everything AI agent must first pass through a phase of boring reliability.

In the meantime, the real innovation is happening in that middle ground: AI that can take some initiative but under strict constraints and human oversight. Think of it as a highly efficient junior accountant who never gets creative with the numbers. We shouldn’t expect an AI to replace the finance team’s judgment; instead, it will augment the team by handling the drudgery with precision. As Ngiam predicts, we’ll all effectively become “managers” to our AI, guiding it, checking it, and benefiting from its labour. That vision is far less glamorous than a fully self-driving finance function, but it’s far more realistic and trustworthy.

Controlled automation: the pragmatic path forward

For finance leaders evaluating AI solutions, the takeaway is clear: Insist on determinism and control. The best AI tools on the market come with guardrails and guarantees. They often advertise “out-of-the-box” functionality that works with minimal setup, because in finance, no one has the appetite for a long experimental integration. Successful finance AI isn’t a black box taking wild guesses; it’s a transparent box that does exactly what you expect, with an audit trail to prove it.

Finance, more than perhaps any other domain, runs on predictability, auditability, and compliance. AI solutions that respect those principles will earn CFOs’ trust and budget. Those that don’t — the “creative” loose cannons will be politely left on the shelf until they can behave.

In the end, the revolutionary idea in finance AI is a bit ironic: it’s not to disrupt everything, but to slot in AI in a way that feels almost mundane. The real win is an AI that makes finance operations quietly better, catching errors, saving time, and never dropping surprises.

As far as CFOs are concerned, boring AI that just works is infinitely preferable to bold AI that needs a babysitter. Deterministic, reliable agents may not make for viral demos on social media, but they’re going to be the ones that actually get adopted in the enterprise.

And when the quarter-close process finishes early with zero errors thanks to some unglamorous AI assistant, no one in the finance department will be complaining that the solution wasn’t “autonomous” enough. They’ll be too busy celebrating the efficiency (and that is the kind of AI revolution finance truly needs).

Learn more about AI agents in finance, their growing use cases, product capabilities, and more.