The AI reality check for finance: why experimentation is over and execution has begun

Finance's audit demands: Trust, data and governance can bridge the gap

by · TechRadar

Opinion By Philippe Omer-Decugis Published 27 July 2026

(Image credit: Getty Images)

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For much of the past two years, artificial intelligence in finance has been defined by experimentation and exploration. Organizations across sectors have been testing tools, running pilots and identifying potential use cases ranging from automated reporting through to more advanced forecasting and analysis.

In most cases, however, the emphasis has been on understanding what AI might eventually enable rather than embedding it into core financial operations.

Philippe Omer-Decugis

General Manager for EMEA & SVP Sales at BlackLine.

This phase is now beginning to change as finance leaders shift their attention from experimentation to execution.

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The conversation is becoming less about theoretical potential and more about measurable value. CFOs are shifting their expectations from immediate, direct cost-reduction to the broader enabling capabilities of AI - specifically, how it can help the business build resilience, navigate macro volatility in real time, and protect the integrity of financial results.

From experimentation to embedded capability

What is becoming increasingly clear is that AI is no longer being treated as a standalone innovation initiative. Instead, it is being absorbed into the broader financial technology landscape, embedded within existing enterprise systems, workflows and controls.

Rather than introducing entirely new tools and interfaces, organizations are prioritizing integration into platforms they already rely on, utilizing their independent system of financial control to govern these workflows alongside their ERPs. This reflects a broader recognition that AI delivers the most sustainable value when it is operationalized within established processes rather than layered on top of them.

At the same time, the reality of adoption is proving more nuanced than early expectations suggested. Many organizations have embraced generic, probabilistic productivity assistants to support basic administrative tasks, delivering incremental gains without transforming how finance operates.

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