In this interview, Visma's CFO, Stian Grindheim, discusses how the finance function is scaling alongside Visma's growth.
INSIGHTS: An interview with Stian Grindheim, Chief Financial Officer at Visma
When Stian Grindheim joined Visma in 2014 as a 24-year-old management trainee, Group Finance was a two-person team, and Visma was still largely a Nordic company generating around €700 million in revenue, with less than half of that derived from software. Today, Visma operates in 28 markets, serves 2.5 million customers, and reported revenue of €2.8 billion in 2025, with a Saas & cloud share of 90%.
Stian is now CFO, and the finance function he helped build sits at the centre of how the group makes decisions, allocates capital, and positions itself for what comes next. That trajectory makes him the perfect narrator for Visma’s story: someone who has lived every stage of the company’s scaling from the inside, building the finance function around it as it grew.
In this interview, we sit down with Stian to trace that journey - from the lesson that surprised him most early in his career, to how Visma is using AI and its deliberately decentralised model to keep the finance function ahead of the business it serves.
You've spent your entire career at Visma, from management trainee to CFO. What has that journey looked like from the inside?
It genuinely hasn't felt like one job. The Visma I joined in 2014 was a very different company compared to what we are today. Back then, we were primarily a Nordic business, and software was only part of what we did - we had around €300 million in software revenue. Today, that figure is tenfold and generated across 28 markets. Every single year has brought something new to learn, a new stage to adapt to. Scaling the finance function through all of that has been one of the most exciting things I could have imagined doing.
My path started in Group Finance during the traineeship. Even though I spent time in marketing and was part of an acquisition in Denmark, I quickly found that finance was where I wanted to build. From there, I moved into the Group Controller role and built the team up from what was, at that point, a very small operation.
And even then, the guiding principle was always the same: how do you make sure you're putting your resources - capital and people - where they'll generate the most return? That question has stayed constant even as everything around it changed.
What's the one thing you assumed about the CFO role that turned out to be more complex than you expected?
Early on, a big focus was on making sure we had all the data so we could make data-driven decisions. I think I assumed that collecting data comprehensively was the hard part - that once you had it, better decisions would follow naturally.
What I learned is that's only half the job. If you really want an organisation to be fact-based, you need to make sure that the people actually making decisions have the relevant data at their fingertips - and just the relevant data, not an information overload. Getting that right has been one of the most important and rewarding parts of the role, and shaped everything we built from there. The goal was never to centralise information. It was to get the right signal to the right person quickly enough to be useful. And that very quickly became a question of speed.
How does speed factor in?
Facts and data, when you're running a business, are like produce - they're best when fresh. If you spend the first two weeks of a month understanding how the previous month turned out, you've lost the time you needed to act on it.
That's why we've invested so heavily in getting our financial close as fast as possible. We typically have our soft close - the most critical data in preliminary form - by the fourth working day of the month. Every day you pull that forward is another day the organisation spends executing rather than waiting.
The other thing I’d add is that the real value of a strong finance function isn't producing financial statements. It's being a genuine business partner - spotting trends early, ideally through leading indicators before they hit the P&L. That urgency is amplified by our decentralised structure. When you have dozens of units making decisions across different markets, the data has to move faster than the business does - or the autonomy you've built becomes a liability rather than a strength.
How is AI changing what that infrastructure can do?
The biggest impact has been in quality assurance during the reporting process and in automating routine insight generation. The constraint was always that the people you most need doing analysis are the same people spending their time making sure the numbers are right. AI has substantially changed that equation - it frees up capacity for the work that actually drives decisions.
This also means that the competencies you need in a finance team are shifting - there's more of a premium on people who can interrogate a model, interpret an output, and ask whether the answer makes sense. That's a more interesting job than what it replaces.
And does that same logic extend to what you're building for customers?
Yes, as many of the SMBs we serve are managing real complexity - regulations, cash flow, reporting requirements - with limited resources. What AI allows us to do, especially when you combine it with the proprietary data and deep local knowledge inside Visma's systems, is give a small business owner the kind of insight that previously only large companies with dedicated finance teams could access.
It's the same principle we apply internally: get the right insight to the right person at the right moment. The opportunity to do that for millions of customers across Europe and Latin America is one of the most exciting things in front of us.
Visma's decentralised model runs through a lot of what you've described. Why is that an advantage?
Two things in particular. The first is agility - we have the muscle of a large group but we're made up of smaller units that can adapt quickly. The second is what it does for experimentation. In a decentralised structure you can run experiments in parallel, replicate what works across the group, and kill what doesn't - fast. You can make mistakes once and get things right many times over. That compounding cadence is where a lot of Visma's operational edge has been built.
It also means the people closest to the customer make the most important decisions about how to serve them. Sitting in Oslo or Silicon Valley thinking you can design the best possible product for a French accounting office is unrealistic. You need to be on the ground, locally present. Our model is built around that reality, and so is the finance function - we push FP&A capability deep into every unit because the insight needs to live where the decisions are made.
And all of this ultimately comes back to return on investment?
That's exactly it. We talk a lot about being data-driven and fact-based, and all of that is real. But what you're actually solving for is investing your resources - financial capital and management capacity, because both are genuinely scarce - where they generate the highest return. The reporting speed, the FP&A capability, the AI layer, the culture of continuous improvement: all of it exists to make better calls, faster, about where to invest next.
A company growing across 28 markets cannot rely on instinct to allocate capital well. You need the infrastructure to see what is working, challenge what isn't, and move with conviction. Building that infrastructure has been the work of the last decade. And we're constantly improving it.
