1199: The CFO’s Biggest Challenge Isn’t AI—It’s Leading Through It | John Kinzer (Interim CFO), OneStream

Equally important was another lesson from a mentor: always look 18 to 24 months ahead. If you’re no longer learning, growing, or able to see your next opportunity, it’s time to start thinking about what’s next, Kinzer tells us. That advice influenced several career decisions, including his move into the dot-com world, where he participated in his first IPO by helping write an S-1 and develop the company’s long-term financial model.

The same long-term mindset later shaped an even more difficult decision. After being passed over for his first CFO opportunity, Kinzer tells us he resisted the urge to react emotionally. Instead, he focused on developing new skills, particularly investor relations, believing the experience would prepare him for the future whether the opportunity came internally or elsewhere.

For Kinzer, career progression has never been about chasing titles. It’s been about continually putting himself in positions where learning comes first—and trusting that leadership opportunities eventually follow.

When you think about the agentic layer, we go after high-volume, labor-intensive areas like close, reconciliation, variance analysis, and forecasting—areas where there are rules-based processes that can be automated. But what makes finance uniquely demanding is that every action must be fully traceable, authentic, and mathematically defensible. This can’t be wrong. This is finance.

OneStream is built for that. We have four offerings on the agent side. We have a Financial Analyst agent that allows finance professionals to query all the data, even if they’re not power users, so they can get deeper insight into the business. We also have a Search agent that allows you to search across your documents without digging through folders, making it easy to find the information you need. We also have a Deep Analysis agent that goes through thousands of unstructured documents and delivers insights. That’s a game changer for both the audit and compliance side.

Then we have a Forecast agent that allows analysts to come in with the answers on the forecast—the variances to scenarios, the growth drivers, and all the things that would have taken them a lot of time to prepare. Now they have that work prepared so they can spend their time thinking about the answers and being the expert when all the questions come up about the forecast.

CFOTL: When it comes to that agentic layer, are the rules of resource allocation different? Have you had to look at those investments differently than other investments?

Kinzer: We really have leaned in here, and we have some really talented individuals. One of our top product leaders told us he’s a hundred times more efficient programming through AI, and he’s actually on the AI product side. So the pace of innovation and what we’re able to release has been incredible. At Splash, people were really excited about what we were releasing. You have to lean into that area.

That was also part of the thesis behind going private. As a public company, it’s really hard to make investments, make model changes, and do things like that in the public spotlight. Every quarter you’re explaining why something went up by a tick or another thing went down by a tick. In this world, pricing is changing all the time, and investments in developers and engineering talent are real investments.

With HG as a partner, it’s allowed us to make those investments with their expertise. They have around 60 companies going through some part of this transformation, and that’s been super helpful. We were fortunate to acquire a company several years ago that gave us the talent, and we’ve been able to attract really smart people who are doing really good work. I think we’re ahead of the curve when it comes to AI for finance professionals.

CFOTL: We frequently talk with finance leaders about the tradeoff between growth and efficiency. What are some of the tougher tradeoffs facing the business?

Kinzer: One example is the investment in markets like Japan and the broader Asia-Pacific region. Those are really significant investments, and as a public company they would have been difficult to make. But if the returns and the opportunity are there, that’s the kind of short-term tradeoff in profitability you make for long-term growth.

Another example is the investments we’re making to transform the business so we can better serve the mid-market. Those are also short-term profitability tradeoffs that create longer-term opportunities to grow and scale.

And then, of course, there’s AI. We’re investing heavily there. R&D continues to be an area where we’re making significant investments because we think there’s a tremendous opportunity, and finance professionals are asking for it. We believe it’s the right investment to make.