NORTH AMERICA – October 27, 2026 -- Finance leaders now answer for both AI trust and AI spending. In Deloitte's Finance Trends 2027 survey of more than 1,400 finance leaders, 48% oversee AI trust, covering the reliability, accuracy and explainability of AI output, and 48% manage AI spending and cost controls. More than two-thirds of the leaders holding these responsibilities took them on within the past three years.
Most organizations split the two duties by default. Cost went to a workstream built on usage dashboards and approval gates, and trust went to another built on governance frameworks and model policies. Alteryx argues that this split is the costly mistake.
CFOs report confident governance but limited visibility into AI cost
Deloitte's Q2 2026 CFO Signals survey shows the gap. 96% of North American CFOs said they were confident in their AI governance framework. Yet 43% cited insufficient visibility into AI tools or use, and 46% named cost uncertainty or transparency as their biggest internal AI concern.
Alteryx traces both problems to rules that models rebuild on every query
According to Alteryx, business rules such as entity scope, FX rates, intercompany eliminations and cost allocations sit in spreadsheets, close checklists and the heads of senior analysts. A model must reconstruct them from the context it receives each time a question is asked. Each reconstruction consumes tokens, so AI spend rises with adoption instead of leveling off. Each can also produce a slightly different answer and leaves no record of which rule was applied to which data.
PCAOB amendments and KPMG data tie traceability to cost control
PCAOB amendments on technology-assisted analysis apply to audits of fiscal years beginning on or after December 15, 2025. They give auditors explicit standards for judging the reliability of electronic information used as evidence. An AI-produced number that cannot be traced to its source data and the rule applied is difficult to present to an audit team.
KPMG's Q2 2026 Global AI Pulse links cost visibility to returns. Organizations with strong AI cost visibility, meaning cost-monitoring dashboards and cost reviews built into AI approvals, were five times as likely to report established ROI: 15% versus 3%.
Alteryx pitches a governed business logic layer between data and AI tools
The proposed fix moves business logic out of the model and into governed workflows. ERP and spreadsheet data is blended, cleaned and validated before any model sees it. Finance owns the variance logic and allocation methods, and the heavy analysis runs once in the workflow rather than on every request. Alteryx says this makes answers traceable and repeatable, makes cost more predictable, and lets one workflow serve every AI tool the enterprise adds.
Alteryx says existing finance staff can build these rules visually, without Python or other code, in the same terms they would use to walk a colleague through a process.