By Paul Lodder, VP, Accounting Product Strategy at Dext.
While many accountants and bookkeepers actively exploring agentic AI, others are still working to separate practical value from the noise. The technology is evolving at a rapid speed, but there is still an underlying, industry-wide pressure to justify the hype. The question for accountants now is how do you turn ambition around agentic AI into commercial value.
- Audited Data as a Prerequisite: An agentic AI system must have a clear focus on ongoing data health—‘clean’ data is not a high enough bar. This means ensuring data isn’t only accurate at one point in time, but complete, standardized and regularly maintained. Errors will naturally occur, but without processes to correct and reconcile them, agents will simply automate and scale those weaknesses across every output. You must prioritize labelled, high-trust financial datasets that embed real bookkeeping judgment and maintain consistency over time. Ultimately, an AI agent performing tax categorization or variance analysis is only as reliable as the guidance it is using. When data health is strong, insights are generated that become actionable and support better decision making.
- Transparency Beyond the Black Box – The defining characteristic of an agent is its ability to apply reasoning on your behalf. However, most AI systems function as black boxes. When an agent moves funds or categorizes a complex transaction, the ‘why’ is often buried in a hidden layer of weights and biases. We need systems that convert ‘habitual’ human knowledge into explicit guidance. For example, imagine an agent flagging a complex VAT treatment on an international invoice. Instead of merely choosing a value, the system provides a hover-over explanation: “I have categorized this as X because your standing instruction for EU-based software services is Y, and this vendor matches the Z criteria.” This level of granular justification makes the system explainable. It allows the accountant to see the logic, edit the underlying guidance, and ensure the agent stays aligned with evolving regulations.
- Accuracy at Scale – Finance is a zero-tolerance environment for ‘close enough’. While Large Language Models are impressive generalists, they are often too ‘creative’ for the rigid requirements of a balance sheet. To achieve true accuracy, the value of specialist Small Language Models (SLMs) can’t be underestimated. These models are leaner, faster, and – crucially – trained on narrow, domain-specific financial logic. Because an SLM isn’t distracted by the vast, irrelevant data of the general internet, it is less prone to hallucinations that plagues larger models. This specialization allows for accuracy at a massive scale.
- The Rise of the Chief Assurance Officer – As workflows become increasingly autonomous, the role of the accountants and bookkeepers will not diminish; they’ll elevate — the better the agents become at processing, the greater the demand for professional oversight. We are witnessing the rise of accountants and bookkeepers becoming ‘Assurance Officers’. They will become the ultimate auditor, who still leverage AI to cross-examine its work, but deliver the final approval to ensure there’s a robust level of accountability, explainability and trust.
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