The Cash Flow Paradox: Why Finance Teams Need to Stop Flying Blind
Here’s the paradox facing finance teams today: despite having more financial data than ever before, most are operating with less actual visibility into their cash position than companies had decades ago.
Modern businesses generate thousands of data points daily through ERPs, CRMs, and dozens of SaaS applications. Finance teams have access to real-time revenue dashboards, automated journal entries, and sophisticated forecasting models. Yet when it comes to answering fundamental questions like “How much cash will we have in six weeks?” or “How much of our budget is actually available to spend?”, most teams are still piecing together estimates from spreadsheets, email threads, and educated guesses.
The paradox deepens when you consider that the very systems designed to provide clarity often create more opacity. Every new application adds another data silo. Every contract gets negotiated in one system, executed in another, and invoiced through a third. The result is a tech stack that’s expanding, and confidence that’s shrinking.
The Forecasting Blind Spot
Most finance teams are operating with a fundamental blind spot. They’re making million-dollar decisions based on averages, estimates, and spreadsheets that tell them what happened last quarter, not what’s coming down the pipeline.
“Cash flow is more of a data problem than a finance problem,” said Carlos Vega, CEO of Tesorio, during a recent webinar discussion with FinQuery’s Justin Smith. It’s a simple statement, but it cuts to the heart of why so many finance teams feel like they’re constantly playing catch-up instead of getting ahead.
The traditional approach to cash flow forecasting relies heavily on metrics like Days Sales Outstanding (DSO) — essentially using historical averages to predict future performance. But as Carlos pointed out, this is like using an indirect method cash flow statement. “If you're off, it’s like who knows why. A change in working capital means nothing.” Without detail, there’s no clear explanation or corrective path.
Forecasting with Granularity and Actionability
The alternative, as both leaders described, is a bottoms-up approach that starts with granular, transaction-level data. Instead of relying on DSO averages, this means building forecasts invoice by invoice, customer by customer, using machine learning models trained on actual payment behavior across millions of companies.
When the forecast is off, you know exactly which three customers didn’t pay which invoices, and more importantly, you know who to call.
This kind of granularity doesn’t just improve accuracy. It gives teams the power to act.
From Budgets to Available Spend
This approach becomes even more powerful when applied to the expense side of the equation.
Justin shared how his team has moved away from traditional budget conversations toward what he calls “available spend” discussions. Rather than telling department heads they have a $6 million budget for technology and programmatic spend, the conversation now centers on how much of that $6 million is actually available, versus already committed through existing contracts.
The shift requires rethinking how contracts flow through an organization. Traditionally, legal teams negotiate and execute contracts, then those agreements disappear into various systems until invoices start arriving months later. Finance teams find themselves chasing down contracts, trying to match invoices to agreements they may not even know exist. As Justin noted, “Invoices are frequently late, they're frequently incorrect, and they're also a source of fraud.”
Obliterate, Don’t Just Automate
Both leaders emphasized that the solution isn’t just better tools — it’s fundamentally rethinking finance processes.
Carlos referenced the classic “don’t automate, obliterate” philosophy. He argued that AI and modern data capabilities allow finance teams to move beyond task-based thinking and toward stakeholder-focused outcomes. Instead of building separate tools for collections, reconciliation, and reporting, the focus should be on getting clean data in one place, eliminating busywork, and enabling teams to focus on the relationships that drive business value.
Real Dollars, Real Impact
The practical impact of this approach showed up clearly in Justin’s own experience.
By having contract-level visibility into their collaboration software spending, his team was able to negotiate renewals three to four months in advance, ultimately saving $35,000 on a single application.
“We’ve been in a multi-year progression of cost optimization,” he explained. “In 2022, it was not hard to shave five or six points off because most businesses were carrying excess. That became harder in '23, harder in '24, and even harder in 2025.”
Breaking the Silos Between Revenue and Cost
What makes this approach particularly powerful is how it breaks down traditional silos between revenue and cost management.
When sales teams can see payment delays directly in Salesforce, and when finance teams can proactively manage contract renewals based on actual usage and performance data, the entire organization operates with better information and greater speed.
Visibility Is the Foundation for Growth
The ultimate goal isn’t just better reporting — it’s better decision-making.
As Carlos noted, “Revenue isn’t real until you get paid.” For finance teams looking to support strategic investments and growth initiatives, having confidence in both cash inflows and committed outflows becomes the foundation for everything else. Without that visibility, even profitable companies find themselves hesitant to make the investments that could accelerate their growth.
The message from both leaders was clear: finance teams that continue operating with partial visibility and spreadsheet-driven processes aren’t just inefficient — they’re fundamentally limited in their ability to support business growth. The data exists to build better systems. The question is whether finance teams will embrace the change required to access it.
Catch the full webinar recording here.