Welcome to Corazon Technologies, home of the Digital Executive Podcast. Do you work in emerging tech, working on something innovative, maybe an entrepreneur? Apply to be a guest at www.corazon.com forward slash brand. Welcome to the Digital Executive. Today's guest is Ankit Chopra. Ankit Chopra is a finance and analytics leader whose work unites strategic financial planning with intelligent system design for cloud and AI-driven enterprises. He currently serves as Director of Financial Planning and Analysis for Cloud and AI Products at Neo4j. Over the past decade, he has led initiatives that transform how organizations forecast price and optimize large-scale technology investments, combining financial discipline with advanced data science and automation. In financial planning for cloud AI and agentic systems, he has developed adaptive forecasting models and pricing frameworks that simulate consumption, predict cost behavior, and guide capital allocation across complex cloud and AI portfolios. These models have enabled companies to improve predictability, gross margin, and resource utilization through dynamic data-driven planning. Well, good afternoon, Ankit. Welcome to the show. Hi, Brian. Happy to be here. Absolutely, my friend. I appreciate it. You're hailing out of Austin, Texas. I'm in Kansas City. So we're in the same time zone today. I appreciate that. And you navigating your calendar, my calendar, everybody else's calendar to get here. So I appreciate that. And Ankit, let's jump into your first question. You specialize in financial planning for cloud, AI, and agentic systems. What makes forecasting and pricing in these emerging areas fundamentally different from traditional enterprise financial planning and analysis? That's a great question. So forecasting and pricing in the world of cloud AI and agentic systems differs fundamentally because the economic behavior of these systems is usage-driven, dynamic, and largely interconnected. If you look at the traditional FP&A systems for enterprise software, it was built for environments where demand was largely predictable and cost structures are largely stable over the annual planning cycles. What we see in the current ecosystem of cloud and AI is a lot more volatility in usage and underlying cost structures. At the same time, the pricing of these offerings have also evolved from static subscription rate cards into much more dynamic pricing models, which include either usage-based pricing, or there are interesting hybrid models, and some of the companies are even experimenting with outcome-based pricing models, which adds to the complexity of the situation. The forecasting and planning is no longer about just extrapolating revenue trends and cost trends. It's about understanding the interactions, behavior, and the causal effects among customers platforms and financials What we do for these systems is we try and understand these interactions and behaviors and translate them into a usage forecast which serves as the backbone of planning. And then further down from there, we can apply the pricing curves and cost curves to understand a whole financial forecast from this situation. In essence, the financial planning and analytics for intelligent systems is not about controlling variance. It's about designing systems for volatility. And that's what makes it fundamentally different. Thank you. I appreciate the insights, really do. I know in traditional financial planning and analysis, they were built for things that were typically predictive, as you mentioned. You know, forecasting and planning are no longer about that typical financial forecasting and planning, as you talked about, but it's more about the interaction behavior and the outcomes of these systems. And as you know, things have become more complex and there's only that data has only increased by a hundredfold in the last few years. So leveraging new processes and of course, leveraging the technology like AI makes a huge difference. So thank you. OnKit, Agendic AI for cloud spend management is one of your pioneering pioneering contributions. How do these autonomous systems work and how do they change the role of finance teams in the cloud cost optimization? So let me start with giving a little bit context on how the market these solutions connect towards. So the end customer market for cloud and AI infrastructure services is currently over 700 billion and it's growing at a double digit growth rate fueled by the AI adoption and expansion. Now, the core challenge which we face in these scenarios and managing these costs is actually not a lack of data. There is a tremendous amount of data that's available. And it's also not a lack of intelligence in human capability. It's about the speed to align and take action, right? Because the time wasted in gathering information, aligning and acting on the insights results in wasted cloud resources, which eventually translates into overspend. So this is a big problem area for a lot of companies and they, which they struggle with. Agentic systems, which by definition have the agency to act with appropriate human governance in loop, are particularly useful in the scenario. These agents can actually monitor usage, stimulate scenarios, combine information from very different sources. And take corrective actions, which drastically reduces the time of alignment and action and thereby reducing the overspend in certain categories. This is what the main value of these systems are. And this is one example where they can really, really deliver tremendous value for the organization. Thank you. Really appreciate that. And that figure 700 billion and obviously growing exponentially year after year is something that can obviously be a cost overrun or just something that can grow out of control But I like how you highlighted the agentic systems are able to do a lot of the traditional things that analysts used to be able to do like simulate scenarios and predict outcomes So, again, I appreciate your insights in this space. Ankit, you frequently advise startups on investment strategy, pricing and business model analytics. What financial mistakes do early stage AI and cloud companies make most often and how can they avoid them? Yeah, that's a great question. And I've seen a couple of different themes and in different domains of work. I'm going to pick the top three and talk a little bit more about that. The first common theme that I see is actually not about finances. It's about understanding the customer and market needs. Early stage founders are very driven people for sure, but they have a tendency to jump straight into operationalizing their idea once they get a hang of it. and this sometimes discounts the ability to do appropriate market research, which includes thinking about the problem they're trying to solve, what are the customers they're trying to serve, and what's the value of the product on the platform they're creating. So this is about clarity and flexibility a little bit more, but clarity requires good market research. And my recommendation to those folks have always been do market research on a frequent basis, And to take action on those insights that you've uncovered, one needs to be flexible. And that's like one key thing that I see recurring across various domains. The second common theme I've seen is not selecting the right price model or setting the right price point. Overpricing hurts the product adoption and underpricing hurts revenue. A good pricing model sits at the intersection of the customer value that is being delivered, the usage dynamic of the product, and the cost structures which are underlying these systems. Founders which understand this earlier on can create not only great pricing, but they can also create great tracking mechanisms which helps them in the long term. And the third common theme I have seen among founders is they are confusing scale with just revenue growth. Most of the folks think that scaling a system, once we've gotten the hang of it and once we have certain adoption, is all about just driving revenue growth. While the real scale is about driving revenue growth while maintaining cost efficiencies. because if the founders are not able to track and optimize the cost efficiencies, they're going to hurt the long-term profitability of their companies. And those are the three common themes that I have seen among cloud and AI companies that create long-term issues. Thank you. I appreciate that. And just to highlight, you talked about founders understanding the market needs. A lot of times founders tend to move into operationalize their ideas, but really clarity is the key. That market research and taking action on those insights is what I took away there And the second point not selecting the right price model and setting the right price Founders can understand that this can be kind of a make or break down the road and then scaling revenue growth right You talked about need to maintain cost efficiencies, not just thinking about that revenue growth and scaling that. I really appreciate that. And on Keith, the last question of the day, as we look ahead, what does the future of financial planning and analysis look like in an era of generative AI, intelligent decision systems, and autonomous financial agents? Which skills will define the next generation of finance leaders? Absolutely. So I think the future with generative AI and intelligent decision systems is going to obviously help folks free up a lot of their time from grant work and focus on high value work. So I think the focus of FP&A will shift fundamentally and will shift from reporting towards insights and insights to actions and from actions to intelligent orchestration of resources within the company. I think the finance function will move from describing what has happened in the past to shaping what could happen in the future. And the systems will help with basically freeing out these times. The systems will also be able to dynamically allocate resources, trigger some actions and optimize plans in real times. And generative AI and agentic apps are going to be really, really great cognitive assistants. But they're not going to be replacing human judgment and business acumen. So the real defining skill for tomorrow's finance leader is going to be on developing critical analysis skills as well as business acumen. The ability to interpret AI-driven insights, challenging the assumptions and connecting them to the business reality of that particular time is going to be what's going to drive real business growth in the future environments. The finance professional who can blend technical fluency with analytical skepticism will be able to lead this transformation in a much better way. In essence, the future of financial planning and analytics isn't about complete automation. It is about intelligent orchestration of resources powered by critical thinking and right business acumen. Amazing. Thank you. And I like how you focused on the generative AI, you know, that shift fundamentally is going to change with agents that can replace those repetitive tasks and do in some of that traditional analysis. but really a move away from the historical past to more predictive insights and true intelligent orchestration of predictive modeling and some of these other tasks that will provide more valuable insights potentially in real time. So I appreciate that. Ankit, it was such a pleasure having you on today and I look forward to speaking with you real soon. Absolutely. Thank you very much, Brian, for being a great host and happy to be here again. Bye for now. Thank you.
