By Nishant Nair, Founder and CEO, RecVue.
Sustained margin pressure, elevated capital costs, and rising board expectations mark the start of 2026, so it’s easy to understand why more finance teams are turning to AI. The hope is for smoother processes, more accurate, timely decision-making, and less manual work. But as AI adoption continues to rise, strategic outcomes have yet to keep pace with market-condition demands.
A new study of CFOs and revenue leaders found that automation and AI use are increasing across key areas in the office of the CFO. Teams are relying on it to flag billing anomalies (78%), automate financial controls (80%), and simplify configurations (81%). And while cash conversion gains have improved, progress isn’t enough in today’s mixed-signal market. More than half (53%) report only modest gains.
To unpack this problematic reality, it’s important to understand where cash delays are actually occurring. It isn’t where you think.
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Cash delays start early
The primary sources of cash conversion cycle (CCC) friction aren’t found in collections or accounts receivable processes. CFOs and revenue leaders say most delays are instead found upstream, long before an invoice is issued or payment is pursued, where revenue is structured, executed, and governed.
Half of all disputes take 10 days or more to resolve; 7% of invoices contain errors; and more than one-third of suppliers’ on-time payments fall below 89%. Forecasting errors exceed 5% for two-thirds of the study’s respondents. These pain points add days or even weeks to cash realization, and they stem from a fragmented revenue architecture that pushes friction downstream.
AI amplifies what already exists
To increase revenue, most organizations now rely on hybrid monetization, including subscriptions, usage-based billing, and outcome-based pricing. These models are great for meeting customers where they are, but they also add complexity and lower overall cash visibility.
Today, many use no-code tools for bundling and discounts, and they apply AI to standalone initiatives such as detection, reporting, controls, and monitoring. Performance lags, however, as these singular efforts don’t address the overarching (and increasingly complex) revenue structure. What looks like a collections problem is actually a structural one.
Errors and delays are prevalent when revenue systems fail to share contract terms, pricing rules, usage events, billing, or renewals. When you apply AI to a single, select area rather than the whole, you’re ultimately optimizing a broken system. Applying AI to a fragmented revenue architecture made up of siloed data only highlights problems. It doesn’t fix them.
Rather than deploying AI to optimize a select process, CFOs will improve cash performance by using AI to unify revenue systems and govern the structure.
Thoughts on getting started
A small number of organizations consistently outperform on cash. What are they doing differently? We asked and, in short, they have modernized their revenue structure for a more proactive posture:
- They treat cash as a first-class operational output, not a by-product of AR.
- They validate revenue before invoices reach customers.
- They address/resolve issues before disputes start.
- They unify contract, pricing, and billing logic.
These steps are possible with AI applied to revenue architecture differentiation, and each can have a direct impact on CCC performance.
AI-powered revenue systems
The next phase of CCC improvement won’t come from adopting more AI point solutions because the root of sub-optimal cash conversion is the lack of a unified revenue architecture. Apply AI across how you structure, execute, and govern your revenue, and you will see strong cash performance in even the most tenuous market.
Nishant Nair is Founder and CEO of RecVue, a global leader in enterprise revenue management
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