When companies introduce artificial intelligence (AI) into corporate finance work, they should first build an "explainable design" that can withstand audits and regulatory verification, rather than simply layering on widely used AI, an argument has been raised.

Generative AI tools are inherently "probabilistic". Financial data, however, requires "factuality" premised on standards, controls and accountability. A chatbot hallucinating while writing poetry may not matter much, but producing an incorrect financial risk profile can become an issue at the level of fiduciary duty. In board meetings or high-intensity audits, an explanation that "the algorithm said so" is not accepted.

On March 31 (local time), IT outlet TechRadar said chief investment officers (CIOs) and chief technology officers (CTOs) should design architectures for building financial intelligence on the premise of transparency, determinism and explainability.

First, enterprise-grade finance AI should not be a black box that only outputs results. It should be able to trace the basis for its conclusions down to the transaction level. When it detects anomalies, risk signals or exceptions, it should link transparently to an audit trail showing which transactions, contributing variables and applied logic led to that judgment. The information generated in this way needs to be delivered to experts so that human judgment is ultimately applied. With this "human-machine link", AI can operate by strengthening experts' capabilities rather than replacing them.

Data processing also needs to change. In the past, financial risk management relied on checking a sample of total transactions, often less than 1 percent, and extrapolating the results. In corporate environments rich in data, that approach is close to carelessness. In other words, the target should be a system that can process 100 percent of transactions before they are reflected in the general ledger.

To do this, companies should eliminate silos fragmented across enterprise resource planning (ERP), customer relationship management (CRM) and legacy databases, and build a controlled single source of data. It also called for using machine learning to organise and tag metadata in real time to reduce AI agents' unnecessary burden of interpreting data, and to shift from after-the-fact reporting to an always-on, real-time transaction verification system.

The first benefit cited is eliminating "EBITDA leakage". It said recurring errors such as duplicate invoices, price mismatches and contract non-compliance quietly erode profits. Gartner estimated that 3 to 8 percent of EBITDA disappears each year due to leakage and inefficiency. In TechRadar's own survey, more than 90 percent of chief financial officers (CFOs) agreed with that estimate, and 60 percent said AI is essential to prevent it. Automatically detecting errors at the point of occurrence can prevent losses before money is spent and help shift IT operations from a cost centre to a value-creation engine.

Even so, a gradual pilot is preferable to a full replacement. Rather than changing an entire department at once, it is important to begin trial operations at repetitive, data-intensive bottlenecks such as month-end reconciliations or accounts payable. At the same time, companies should set who is responsible for AI results and establish data quality, security and explainability standards from day one. If a supplier cannot explain how a model reaches its conclusions, it should be seen as not ready for the enterprise.

Finally, what matters in the competition to adopt AI is not "speed" but a "reliable foundation". In finance, trust is not an add-on function but the product itself, and speed without integrity can be acceleration in the wrong direction.