Sage CTO: Finance Teams Reject AI Not Because Models Are Inaccurate, But Because the Reasoning Process Is Invisible
Sage Chief Technology Officer Aaron Harris argues that the bottleneck for AI adoption in finance has shifted from model accuracy to trust infrastructure. A joint survey by Sage and IDC shows that 70% of CFOs would veto AI output even with 99% accuracy if they cannot see the reasoning process; financial leaders spend over 12 hours per week validating AI outputs. Stuart Langer, Sage’s Product Lead, notes that while CFOs can accept a fixed monthly fee, they cannot tolerate volatility where bills jump from $2,000 to $24,000 and then drop to zero.
Harris pointed out at the Sage Future for Partners conference that a year ago, the challenge for AI applications in finance was getting models accurate enough to be trusted. Now, the challenge is everything surrounding the model: the systems that enable agents to operate safely, auditable reasoning chains, and stable costs acceptable to CFOs. He breaks trust down into four specific conditions—data security, ethical construction, human control, and competent, predictable execution—and emphasizes that these conditions are all indispensable.
This judgment is supported by research data commissioned by Sage from IDC. Financial leaders currently spend more than 12 hours a week validating AI-generated outputs, with 28% of that time consumed by repeated verification and skepticism. Crucially, 70% of CFOs stated they would veto an AI-generated output even if it were 99% accurate, provided they could not see the reasoning behind it. Harris believes this is not CFO resistance to AI, but a clear-eyed recognition of the cost of errors—in the fields of finance and accounting, the cost of missing trust is real and quantifiable.
Cost volatility is another overlooked dimension of trust. Stuart Langer, Senior ERP and Financial & Operational Systems Product Leader at Sage, explains that CFOs are not unable to pay, but cannot stand variability. If the cost is a fixed $2,000 per month, they might dislike it but can accept it; however, $24,000 one month, zero the next, and $24,000 again the following month creates unpredictability, which is the real problem. Reporting author Mark Chillingworth suggests that Sage’s proprietary model, trained on over 15 million invoices, shares similarities with the industry's focus on Small Language Models (SLMs)—the latter being valued by digital decision-makers precisely to reduce the unpredictability of token costs associated with general Large Language Models.