An Interview With Chad Silverstein
AI helps us do that in a very practical way, with better cash forecasting, more intelligent payments, and less manual work in the background. We are not trying to reinvent finance. We are trying to make it work the way people always expect it should.
In today’s tech-driven world, artificial intelligence has become a key enabler of business success. But the question remains — how can businesses effectively harness AI to address their unique challenges while staying true to ethical principles? To explore this topic further, we are interviewing Axel Rebien, CEO of Serrala.
As a part of this series, we had the pleasure to interview Axel Rebien.
Axel Rebien is the Chief Executive Officer (CEO) of Serrala, a leading global innovator in financial software solutions that enable organizations to optimize their financial processes. Axel holds the overall responsibility for the strategic direction, operations, management, and performance of the company. Before he was appointed as CEO in July 2023, Rebien joined Serrala’s Executive Board beginning of 2022 as the Chief Financial Officer (CFO), in which he has been driving the company’s financial strategies and finance-related processes including accounting, financial planning & analysis that deliver a return in business transformation.
Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path in AI?
I started in finance, focusing on liquidity and working capital. While these are not the most visible parts of a business, they are some of the most critical. What stayed with me was how unclear things often were. You would ask about cash and get answers that were technically correct but not useful for decisions. That is really what led me here. At Serrala, we focus on making that picture clearer. AI helps us do that in a very practical way, with better cash forecasting, more intelligent payments, and less manual work in the background. We are not trying to reinvent finance. We are trying to make it work the way people always expect it should.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
There was one case where a team was pretty confident that they had their liquidity under control. When we ran their data through AI, it flagged some timing patterns no one had really paid attention to. Nothing dramatic, just small gaps between when cash came in and when it went out, but it kept repeating. Once they saw it, they tweaked a few things and freed up cash almost straight away. No big transformation, just better visibility. What stuck with me was how quickly the conversation changed. It went from explaining what had already happened to actually managing what was coming next. That shift is bigger than it sounds.
You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
Curiosity was important early on. I wanted to understand how AI could actually be used in finance, not just talk about it in theory. Pragmatism kept things grounded. There is a lot of excitement around AI, but in the end, it must show up in outcomes. I have always focused on use cases where the impact is measurable, like forecasting accuracy or working capital improvements. Resilience probably mattered most. Introducing AI into finance is not always straightforward. There is skepticism, and rightly so. You have to prove value step by step and build trust over time.
Let’s jump to the primary focus of our interview. Can you share a specific example of how you or your organization used AI to solve a major business challenge? What was the problem, and how did AI help address it?
A common challenge is poor cash visibility across systems. We worked with a company where data was spread across different platforms and regions. There was no single, reliable view of cash, especially not forward looking. By bringing that data together and applying predictive forecasting, the company gained a much clearer picture of both current and future cash positions. That allowed them to make better decisions around funding and liquidity, simply because they were working with a complete view.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
One misconception is that AI replaces finance teams. What we see in practice is that it supports them. It removes manual, repetitive work, and gives people more time to focus on decisions. Another is that AI is too complex or expensive. In many cases, you can start with a single use case and scale from there. It does not require a full transformation upfront.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
For me, it is about real time, data driven decision making. In finance, that often means seeing liquidity as it actually is, not as it was reported days or weeks ago. That visibility alone can unlock cash and improve efficiency. It has a very practical impact.
Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Complex Business Problems”? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges. If possible, please share examples or stories for each.
1. Predicting cash flow
If I look at where AI makes a difference in practice, it usually starts with cash flow prediction. When forecasts become more accurate, planning stops being a guessing exercise and becomes something you can rely on.
2. Automating processes
From there, a lot of value comes from automating routine processes. Payments, reconciliation, and those kinds of tasks. It is not just about efficiency; it is also about reducing errors and freeing up time.
3. Detecting risk and fraud
Another area that matters is risk and fraud detection. AI is good at spotting patterns that are easy to miss, which means issues can be addressed early instead of after the fact.
4. Optimizing working capital
You also see a clear impact on working capital. Once you understand payment behavior and timing better, there is often cash sitting in the system that can be unlocked without changing how the business operates.
5. Enhancing insights
Overall, it comes down to better insight. When data is finally connected and makes sense as a whole, decisions become a lot more grounded and a lot less reactive.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
I would start with one clear use case, something like invoicing or forecasting. There are many SaaS solutions available now that already include AI, so you do not need a large upfront investment. The key is to start small and build from there.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
I would treat AI as an evolution, not a disruption. Start with a pilot, prove the value in a controlled way, and then scale. Once people see the impact in their own environment, hesitation usually fades.
In your opinion, how will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?
I think finance is going to look very different in five to ten years, and not in a gradual way. A lot of what finance teams do today is still manual, delayed, and backward looking. That will shift toward something much more autonomous. Not fully hands off, but a lot of the heavy lifting will happen in the background without constant intervention. What I find interesting is the rise of AI copilots for CFOs. Not just dashboards, but systems that interpret what is happening, flag risks early, and suggest actions in real time. That changes the role quite a bit. Planning will also become continuous. Less about monthly or quarterly cycles, more about constantly adjusting based on what is happening in the business. So, it is not just faster finance. It is a different way of running it altogether.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
For customers, it usually means faster and more personalized interactions. For employees, it shifts the focus. Less manual work, more involvement in decisions, and analysis. Overall, it tends to make organizations more responsive, both internally and externally.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger. 🙂
I would focus on democratizing financial intelligence. A lot of companies still operate without a clear, real-time view of their cash. That creates unnecessary risks. If AI can make that level of visibility accessible to every company, regardless of size, it would have a meaningful and very practical impact.
This was great. Thank you so much for the time you spent sharing with us.
About The Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.
Axel Rebien Of Serrala On How Artificial Intelligence Can Solve Business Problems was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.