CFOs Are Rethinking AI Investment Priorities in 2026

As boards urge C-suites to produce higher returns on enterprise AI investments and as AI pricing models evolve, CFOs are reshuffling priorities.

Finance leaders are pressing AI buyers throughout the organization to identify the total costs of their purchases and understand how to measure the benefits their AI solutions generate. This marks a major shift within enterprises that have formal AI deployment objectives but lack the necessary road map, planning, and execution to ensure AI investments produce measurable value.

Three CFO AI Actions that Extend Beyond Finance

CFOs are applying the same cost transparency, measurement discipline, and accountability to the finance function’s AI investments. A Protiviti report prescribes six actions CFOs should prioritize to perform finance’s AI housecleaning. Three of these points are equally relevant to IT and indirect tax leaders:

• The most valuable AI is often the AI you already own: Protiviti encourages CFOs to inventory AI tools and functionality that reside with solutions within the finance function and among the company’s ecosystem partners, including ERP vendors and tax automation providers. The paper emphasizes that currently available AI tools are often underutilized – and that generating AI value may not require additional investment right now. CFOs and CIOs who assess embedded ERP tools that support procurement and payments processes should keep in mind that similar AI functionality is embedded in tax automation solutions.
• Semi-autonomy is a prudent near-term target: Protiviti reports that relatively few organizations are currently ready for fully autonomous AI. That’s fine since companies can generate valuable returns from semi-autonomous agents while maintaining an effective control environment as they establish and refine human oversight. One of the paper’s central points is that finance leaders who prioritize discipline and resilience over implementation speed will be well-positioned to unlock AI’s full potential in the months ahead.

• Data quality is crucial: Inconsistent data quality features prominently among the AI-readiness gaps CFOs must close, according to Protiviti. This emphasis will sound familiar to indirect tax leaders who are well aware that tax data errors – and, by extension, audit risks – can mount without sufficient attention to master data management. This dynamic, which Vertex refers to as Defensibility Drift™, erodes trust, giving rise to a Compliance Confidence Gap™.

   

The report stresses that AI requires reliable source data, which represents one of the foundational enablers of Vertex’s AI platform. Another enabler is our high-quality expertise, which is present in our decades of tax knowledge, content and rules covering more than 20,000 jurisdictions worldwide. “A generic AI model can generate a paragraph about VAT in seconds,” writes Vertex Vice President of Technology Strategy Chris Zangrilli. “Whether it can consistently support tax decisions with the level of accuracy, transparency and context required in enterprise environments is a very different question.”