The Finance Myths That No Longer Hold Up

The way finance teams work has changed quickly. What used to be a largely budget and reporting function is now expected to support faster decisions, have broader business visibility, and have tighter cross-functional alignment.

That shift is why some of the old assumptions about finance no longer match the reality of what high-performing organizations need from finance today.

Here are a few myths I think finance leaders need to let go of.

Myth: FP&A Owns the Budget, Accounting Closes The Books

Reality: Modern finance teams are far more interconnected than that.

FP&A is not just building forecasts. FP&A is increasingly focused on understanding what’s driving performance. What levers move EBIT? Which investments are paying off? What risks are emerging before they hit the forecast? The role is increasingly centered around scenario planning and decision support.

At the same time, accounting plays a much bigger role too. Revenue recognition policies impact pricing flexibility. Deal structures affect downstream financial outcomes. Accounting increasingly influences operational decisions, from pricing to commissions, not just historical reporting.

You can’t build a reliable forecast without strong accounting. Both teams increasingly rely on shared operational context to understand how business decisions flow through the financials. The strongest finance organizations don’t work in silos. They align around shared business outcomes.

Myth: Governance Slows You Down

Reality: Strong Foundations Enable Better and Faster Decisions

When finance teams don’t trust the underlying data, decision-making slows. Teams spend more time validating information than acting on it. And once confidence in the numbers starts to erode, decision-making does too. 

Clear ownership, trusted data, and reliable standardized processes create the foundation needed to scale planning, reporting, and even AI tools effectively. Without that foundation, organizations risk making faster decisions, but not necessarily better ones. 

This becomes even more important as AI gets embedded into finance workflows. The teams moving fastest are usually the ones that invested in strong operational foundations early.

Myth: AI Is Going To Solve Everything

Reality: AI will help finance teams scale, but it won’t replace judgment.

Finance has  a very high bar for accuracy, controls and accountability. Outputs can’t just sound reasonable. Teams need to understand where information came from, whether the logic holds up, and how the results and decisions impact the broader business.

The real opportunity with AI is to improve decision-making, reduce repetitive work, and give teams better visibility without weakening accountability. The focus is shifting toward helping teams operate more effectively at scale.

The work still requires judgment. It still requires people who understand the business, understand the numbers, and can decide whether something makes sense.

The Bigger Shift Happening In Finance

Finance teams today are expected to operate far beyond their traditional functional boundaries.

Technical expertise still matters. But communication, adaptability, business context, and cross-functional collaboration are becoming just as important.

The organizations adapting fastest understand that finance transformation is as much about operating model change as technology adoption. It’s about helping shape what happens next.

What I’m Reading

AI Pricing: When the Cash Register Is a Slot Machine
As AI pricing models evolve, many companies are introducing credits, usage metrics, and dynamic consumption models. But as Zuora Founder and CEO, Tien Tzuo points out, customers can’t budget against uncertainty. The article explores why trust, predictability, and clear value exchange remain critical even as AI monetization becomes more sophisticated. 

AI Credits: From Hype to a Concrete Blueprint
Zuora’s Michael Mansard takes a practical look at one of the biggest questions in AI monetization: when do AI credits make sense? The article explores why companies are increasingly adopting credit-based models, what successful implementations have in common, and the financial and operational considerations that come with them.