How Do You Scale a Finance Team as the Business Grows?

To scale Finance as a company grows past milestones like $100 million in revenue, build the foundation first: standardize your processes, get to a single source of truth, and choose technology for the business you have. Scale is a day-one design decision, not a later fix.

These were the central points from a OneStream webinar with Om Kapoor (Senior Product Marketing Manager, OneStream) and Chris Ortega (Founder and CEO, Fresh FP&A) on how Finance changes across people, process, and technology as a business scales.

Watch the full conversation on demand.

Key takeaways

• Build a Finance foundation before growth forces the issue. The processes and tools that carry a startup rarely survive past $100 million.
• Finance must shift from tactical work to strategic analysis, because it rarely headcount rarely scales with the growth.
• Don’t automate a broken process. Fix and standardise first, then automate.
• A single source of truth is the foundation of scalable Finance.
• Choose technology for the business you have, not the demo. The same rule applies to AI: start with practical use cases.

Strong processes are what let a Finance team scale, because they set the framework that carries a company through milestones like an acquisition or a fundraise. Most growing companies run on processes that don’t scale: manual, heavily customised, or stitched together by several Excel “Franken-sheets”. These break the moment the team grows, or an investor asks for reporting the general ledger was never built to produce.

As Chris Ortega puts it: “Processes are the scale framework of every business. If you learn the processes of a publicly traded company, you can apply that to a private or a scale-up company.”

The fix is to build scalable, repeatable processes early, before growth forces the issue.

As companies scale, leadership increasingly sees Finance as the engine of growth rather than a support function. CEOs expect the Finance team to guide the business through its most complex periods, not simply report on results after the fact.

Ortega puts the expectation higher than the common benchmark: “I think it’s a higher percentage of CEOs, closer to 90% than seven in ten, who see Finance as the growth engine.”

For Finance leaders, that is both the opportunity and the problem. You are expected to lead on forecasting and analysis while still solving the tactical work, data wrangling, reconciliation, and process cleanup that keeps the team from higher-value work.

Choose technology for the business you have, not the one in the demo. Identify your bottlenecks first, then find the tool that solves them. A unified platform helps Finance to keep data consistent, automate tactical work, and free up time for strategic focus.

For example, Arcade Beauty replaced Hyperion with OneStream’s CPM Express, went live in about three to four months across 12 production sites, and cut its close from ten days to three by centralizing its data onto one platform.

Ortega warns against the opposite instinct, which he calls “Lamborghini syndrome”: “You see that nice fancy Lamborghini, but you’ve failed to understand the pains, frustrations, and challenges of your business. You bought a Lamborghini and you’re running it in a school zone.”

AI works best when it augments the Finance team rather than replacing it. Used well, it takes over tactical, low-value work like organizing data, so the team can spend its time on decisions.

Ortega frames where AI fits using what he calls the decision cycle: “Processes drive data. Data becomes information. Information is shared as knowledge to make a business decision. AI can automate the step from process to information. People are what matter in the knowledge and decision phases.”

He also urges Finance teams to press their vendors before buying: “Show me behind the scenes. Show me the proprietary algorithms you built that make it your AI.” A lot of what is sold as AI is just a back end to a general-purpose model, and you should know the difference.

Finance should bring IT in from the start, because that partnership is what keeps tools like AI governed rather than sprawling. Most organizations already have shadow AI running without governance.

Ortega's practical advice to this end is to, “Build data governance and an AI sandbox with IT. Centralize the licensing and token usage. Show that finance and IT can partner together.”

Bringing in IT (along with the CTO, chief data officer, and CIO) as you adopt more tools turns scattered usage into a framework the whole business can rely on.

Scaling well means moving people away from manual work and toward strategic work. Look honestly at where the team’s hours go, then reduce the time lost to low-value tasks through automation and better data processes.

According to Om Kapoor, the scale of the problem is that around 23% of Finance teams spend most of their time on analysis, and 28% spend as much time correcting data as analysing it.

The first step is to build the foundation now: assess your baselines, standardise your processes, unify your data, and bring IT and the wider business in as partners. As Ortega advises, in closing, “Build your foundations now. Look at the baselines of where you are, and the opportunities you can act on.”

At what point does a Finance team need to change how it works?

Usually around major growth milestones, such as passing $100 million in revenue, an acquisition, or a fundraise. The processes, people, and tools that worked at startup stage tend to break under that complexity.

What is “Lamborghini syndrome” in Finance technology?

This is term Chris Ortega uses for buying an impressive tool before understanding the business problems it needs to solve, and thus ending up with powerful features you can’t use. The fix is to identify your bottlenecks first, then choose the technology.

Will AI replace Finance jobs?

No. The speakers’ view is that AI augments Finance by automating tactical, low-value work like data preparation, freeing people for the knowledge and decision-making work that AI can’t do.

What is a single source of truth in Finance?

One governed set of data that every system and team works from, so two systems can’t each defend a different version of the same number. It is the foundation that lets Finance scale decision-making with confidence.

Watch the full conversation on demand, or explore the Mid-Market Finance Hub for more on scaling Finance.