CFO Weekly

The episode delivers several substantive ideas about AI-native finance versus bolted-on tools, governance-first approaches, and the primacy of data hygiene over technology. However, it contains notable padding (long personal backstory, repetitive framings of the same concept) and lacks sufficient concrete examples or metrics to achieve higher density. The core insight - that mindset and data matter more than tools - is repeated multiple times rather than explored with fresh angles.

“an AI native operating model is the opposite. So it means that your data, process, governance, everything you kind of built before are designed assuming AI does the work. It is not retrofitted around the tool you bought.”

“The mindset shift is understanding that this is an operating model change and it's not a software outlook.”

The distinction between AI-native and bolted-on tools is useful and relatively fresh framing, and the emphasis on quiet failure modes in AI versus visible ERP failures is genuinely insightful. However, the core thesis - prioritize data and governance, shift mindset first, avoid shiny tools - has become standard in enterprise transformation circles. The framework (DASH) is presented as novel but is essentially a repackaging of standard change management (digitize, automate, streamline, harness).

“If you remove the AI tool today, would your process still make sense? So if the answer is no, then you redesigned around it. Then you're kind of the native environment and if the answer is yes, you just go back to spreadsheets”

“a bad AI implementation is quiet. You can run on something, be so comfortable, you can think that you have the best answer and it's going to be unnoticed until a decision's already been made.”

Bojan Velikovsky holds a credible operational role (VP Finance and Treasurer) and has genuine hands-on experience across multiple industries, ERPs, and restructurings. He demonstrates practitioner knowledge (building agents, deploying solutions, managing real liquidity crises). However, he appears primarily as a consultant/book author rather than a scale operator at a major enterprise, and the interview doesn't establish the size or complexity of his current or past organizations.

“Bojan Velikovsky, Vice President of Finance and Treasurer at Vltava”

“Treasury and finance transformation leader with deep experience in MA integration, liquidity strategy, risk management and enterprise finance modernization”

The episode is notably light on concrete data, named companies, or quantified outcomes. The guest mentions surveys showing "70, 80%" of finance teams prioritize cash forecasting and references personal experience with restructurings and ERP implementations, but provides almost no specific examples, timelines, cost figures, or named client outcomes. Treasury 2.0 is mentioned repeatedly but never detailed with metrics.

“surveys show that 70, 80% maybe of finance teams name those cash management forecasting as the first couple things that they would fix”

“I've spent time in automotive, public service, renewables, renewable energy that is Gaming, sports and entertainment, food and distribution, real estate”

The host asks solid foundational questions and does follow up on key points (e.g., asking how to know when data is clean enough, or what separates successful operationalization from pilots). However, the questioning remains largely open-ended and rarely pushes back, challenge assumptions, or probe into potential contradictions. There are no moments of productive disagreement or sharp follow-ups that probe deeper when claims feel abstract.

“And treasury often sits at the center of liquidity, risk and capital allocation. How do you see AI changing the role that treasury plays in enterprise decision making over the next few years?”

“And I'm just curious, like when you look at AI, are you more excited about the future and being like a finance professional or are you scared that someday AI is going to replace the need for human judgment?”