The AI governance gap: why AI is moving faster than the rules meant to control it | Tech Radar

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The AI governance gap: why AI is moving faster than the rules meant to control it

Regulators struggle to keep pace with rapidly advancing AI

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The potential around AI in finance is tangible right now and with it comes both opportunity and risk.

Vendors are promising faster closes, automated reporting and real-time insights. Finance leaders are under pressure to modernize and keep pace with the AI revolution.

Across the mid-market, AI is being embedded into financial workflows at a pace that is outrunning the governance structures needed to make it safe.

That gap is where a new category of risk is emerging, one that for professionals working closely with finance teams are increasingly calling "black box AI." It is yet another challenge the industry needs to address head-on.

Black box AI refers to any finance deployment where outputs cannot be traced or governed. It shows up as unverifiable recommendations, automation that bypasses normal approval workflows, or analytics tools that surface a headline figure with no clear path back to the underlying transaction.

If everyone is rushing to board the AI ship why are so few workflows secure?

AI is reshaping the finance industry, but governance concerns remain front of mind for CFOs

The outputs look credible on the surface, but when an auditor asks what has changed, who approved it and why, that’s when finance teams run into problems.

Finance teams have always operated on a simple principle: if you cannot trace it, you cannot trust it, this mantra has not changed in the age of AI. What has changed is that the technology can now either reinforce that discipline or undermine it.

Which outcome occurs comes down to how AI is built and what your provider is willing to be held accountable for.

Assistive versus autonomous: A distinction that matters

Not all AI carries the same risk profile. The distinction for finance leaders is between AI that assists in a controlled environment and AI that acts autonomously.

Assistive AI flags issues and directs decisions through existing approval structures. It identifies an exception, spots a coding anomaly, or highlights an unusual movement, and then puts a human in the driving seat for decision-making. The finance professional can see the source transaction, the rule behind the flag, and the approval record. Every action the AI makes also leaves a clear audit trail.

How CIOs Can Implement AI with Real Financial Intelligence

Autonomous AI, by contrast, makes decisions silently. It allocates, codes, or adjusts without a visible audit trail, leaving teams unable to satisfy even basic reporting requirements. It may make things look faster but it is also quietly undermining the integrity of the numbers.

The question every finance leader should be asking their technology provider is simple: if something changes in our reporting, can we see exactly what happened, who approved it and why? If the answer is unclear or non-existent, then that is a problem for the finance team to address.

Month-end close is where well-governed AI delivers the clearest practical return on investment, and it is worth being precise about what that looks like.

The real win is not AI-generated narratives or automated reporting. It is catching issues earlier in the process before the stakeholder board packs go out, before the pressure peaks and before the rework begins. When AI surfaces exceptions at the right moment and directs them through channels, teams can close faster and with greater confidence in what they are signing off.

For organizations operating across multiple entities or currencies, this becomes even more valuable. AI can flag unusual movements at subsidiary level before they surface as problems at group consolidation but only when the underlying model is governed properly and the exception trail is intact.

There is a version of AI in finance that makes teams more effective, then there is a version that makes things look faster while quietly compromising the controls that finance exists to uphold.

The industry needs to be more precise about what earns the label ‘finance-grade AI.’ Reporting or analytics tools only deserves that description when a professional can analyze from a headline variance figure down to the underlying transactions and from there to the audit-trail evidence that can explain that headline variance number.

Anything that breaks that process - however sophisticated it appears - is not fit for purpose in a governed finance function.

Finance leaders cannot afford to adopt technology simply because it is what everyone else is doing. The right question is not whether AI can automate a process, but whether it can do so in a way that keeps the finance team genuinely in control of their numbers with a ‘human in the loop’.

That standard should be a non-negotiable and it should be first on the checklist before any AI deployment goes live.

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