Finance professionals tend to focus on making sure the final report is accurate, but they often forget about the "friction cost" of collecting data at the operational level. I have run large ERP implementations and I believe the largest threat to the accuracy of finance is not an inaccurate algorithm but a workflow that is too rigid for the people performing the actual work. If a system has been developed solely to ensure compliance with accounting regulations without taking into consideration how that system will impact the operations of the organization, then employees performing the work will revert to maintaining "shadow finance," i.e., maintaining their own spreadsheets to get their work done. The result of this disconnect is a tremendous lag between what is happening on the floor and what is reflected in the financial statements. Genuine financial visibility is not achieved through tighter controls but rather by making it easier for an operations manager to record a transaction. If a fintech solution does not solve a real-world problem for the person recording the transaction, it is exceptionally unlikely that the data will be entered correctly or in a timely manner. It is easy to forget that every data point represents a person trying to achieve 12 conflicting priorities. Therefore, when we build systems that honor their time and limitations, we not only develop better operations but we also provide the financial clarity that decision-makers need to make informed decisions.
In hospitality, I've learned that "better" financial tools don't fix weak financial habits. Finance teams often assume if reporting is accurate and timely, the business will naturally make better decisions, but on the floor the real bottleneck is behavior: inconsistent cost coding, managers who don't understand the why behind targets, and teams who can't connect daily actions to weekly cash. Practically, I've found the biggest unlock is translating finance into operational triggers: a simple weekly cash cadence, a few non-negotiable KPIs tied to labor and yield, and clear guardrails for spend approvals. If fintech wants to matter to non-finance operators, it has to reduce decision friction in the moment (e.g., "can we staff this shift?" "can we comp this?" "can we buy this now?"), not just produce nicer dashboards after the fact.
One of the biggest things I've learned running a small business is that profit on paper doesn't always equal survival. Cash flow is everything. When your distributors suddenly raise prices, you might see a good profit on paper. But you could still struggle to pay for your next order. Finance professionals often don't experience this reality directly when using models and spreadsheets. The other thing I've learned is that pricing is never set and forget. In the real world, you need to act quickly when costs change. This could mean adjusting your shipping threshold or updating your inventory pricing overnight. A spreadsheet can show your margins, but it can't capture how fast things change when you run a business daily.