A recent survey of more than 100 financial leaders from investor-backed companies conducted by Consero Global found that cash flow optimisation is viewed as being just as important as revenue growth.
Cash flow discipline starts with visibility and visibility starts with how rigorously you instrument your business suggests Raj Lakhani, CFO of NetBrain Technologies. He explains that over the past year, the company has focused tightening the feedback loop between revenue recognition and collections, reducing friction in its renewal cycles and being more surgical about where it invests in customer acquisition versus where it optimises for retention economics.
“On the operational side, we have put in place several disciplines that individually seem modest but are collectively meaningful,” he says. “We established a purchasing committee to bring rigour to outgoing spend decisions. We introduced a structured review process for larger invoices to ensure billing goes out accurately and on time – because billing errors and delays are among the most underappreciated drains on working capital.”
NetBrain Technologies has also increased the cadence of dunning communications to delinquent customers and eliminated the practice of paying vendors ahead of terms.
“Payments now go out in accordance with negotiated due dates, not before,” adds Lakhani. “The result is a finance operation that is tighter, more deliberate and structurally better positioned to protect margin as we scale.”
The report noted that despite a sharp increase in AI adoption in finance departments, structural barriers such as data readiness have prevented full deployment. According to Lakhani, the reason is that most finance teams are further behind on this than they would like to admit and the gap is data hygiene rather than technology.
“Before you can extract meaningful intelligence from your financial operations, you need clean, consistent, well-governed data across your ERP, CRM and revenue systems,” he says. “We have been intentional about building that foundation, standardising how we capture and classify financial data so that when AI tools are applied, they are accelerating insight rather than amplifying noise.”
The immediate wins have been in forecasting accuracy and anomaly detection. The next step is moving from augmentation to automation, systematising those workflows so that extraction and analysis happen without manual intervention, freeing the team to focus entirely on judgment and decision-making.
“The longer-term opportunity, which I think most CFOs are still underestimating, is dynamic scenario modelling that responds to business conditions in near real-time rather than at the end of a quarter,” says Lakhani, who cautions against addressing finance operations scaling issues by adding headcount.
The CFOs who navigate this well are the ones who treat their finance function as a product, something to be designed, iterated on and made increasingly leverage-efficient over time, he says.
“Practically, that means three things. First, ruthlessly automate the transactional layer so your team’s cognitive capacity is reserved for judgment-intensive work. Second, build for transparency, not just accuracy. Boards and investors don’t just want the numbers; they want confidence in the narrative behind the numbers and that requires finance to be genuinely connected to the operating rhythm of the business, not a downstream recipient of it.”
Lakhani’s final recommendation is to invest early in the metrics architecture.
“The companies that scale well are the ones where the CFO has defined – at an early stage – exactly which numbers matter and built systems that surface those numbers without heroic effort every month,” he concludes. “Growth forgives a lot of sins in the short term. But if your finance infrastructure isn’t scaling with your ambition, you will eventually hit a wall that no amount of revenue can paper over.”
