Moderated by Niharika Sharma, Head of Marketing at Hyperbots.
Niharika: Hi everyone, this is Niharika and I take care of marketing at Hyprbots. Today we have with us Anna Tiomina, who’s a CFO with Blend2Balance currently and has been operating in the finance domain for over two decades. Hi Anna, how are you doing?
Anna: Good morning, thanks for having me. Doing great.
Niharika: Lovely to have you, Anna. We are so excited to have you here. Before we begin with the topic, which is ROI on AI-led automation initiatives, it would be great if you could take us through your journey, your roles, and past experiences as a CFO, and how you’ve seen the finance domain evolve over the years.
Anna: Okay, so I’ve been in finance throughout all of my career, which is almost two decades by now, which is unbelievable. I’ve progressed from controller to senior controller to CFO. I’ve served as a CFO for more than 10 years, and I’ve worked in different companies and smaller companies in big multinational companies. So I think I have a pretty good understanding of the financial environment and the challenges that companies face in this sphere. My biggest goal is always to ensure the financial health of the organization but also to drive all the finance-related processes because finance is responsible for a lot of important things, you can’t drop any of them. In a way, a person who guards their organization from getting into trouble.
Niharika: Thank you so much for explaining that, Anna. I’m sure this conversation is going to be very valuable for all the listeners and viewers that we have. Your experience in the finance domain is going to enlighten us with what kind of decisions one should make when considering introducing AI in their finance processes. To begin with, I would love to understand from your vantage point, how you perceive the current landscape of AI-led automation in the finance industry.
Anna: Yeah, I might say this is a very exciting process right now. A lot of new things are happening in the area. It looks like AI automation would help finance professionals streamline processes, reduce errors, provide insight, and help us avoid manual tasks, which I think is the most unpleasant part of what we people in finance do. From the perspective of a finance executive, I see that this topic is on the agenda for many CFOs and organizations. It’s important to stay on top of this process for everyone who wants to progress in their careers and lead their organizations to success.
Niharika: Lovely. I’m sure this is going to be very insightful for all of us. From your understanding, what sort of investment do you anticipate when someone is thinking of implementing AI?
Anna: The first thing to mention is I’ve read research very recently, and it shows that finance is not among the top areas for AI adoption and implementation in industries. Companies usually start in areas such as customer relations, marketing, sales, software development, and R&D. Finance is somewhere at the end of this list. My understanding is that because finance is a very risk-averse area, this is not the first place where you would put innovation. Many companies are not ready for AI adoption because the data is not integrated. There is no single source of truth in many data points, and the processes are not smooth enough to implement automation. Implementing AI in finance is a substantial investment. We finance people always think about ROI, so when starting any effort, we think about what we will get out of that. When we talk about investment in implementing AI, there are three main areas: financial investment, time, and effort.
Niharika: Absolutely.
Anna: The most obvious is the financial investment: the cost of software, the cost of maintaining the software going forward. The second is time because there is not much experience in this area, and sometimes it’s hard to understand if it would take a lot of time or not to implement and adopt any technology. Let’s not forget about effort. I’ve always worked with very lean organizations where every additional process stretches the current team. I’m always very mindful of what I put on the shoulders of my team. So the three main areas of investment time, effort, and money are what it gets to if we talk about investment.
Niharika: Absolutely. When it comes to transactional data, value associated with money, and ROI, it’s good to be very mindful of where you’re investing concerning AI. Of course, AI is created by humans and it was not 100% accurate when it was introduced. After having it tested and tried in various domains, the application of AI in finance sounds like a good decision. At the same time, it’s very opportunistic in terms of what it has to offer shortly.
Anna: Yeah, I mean, it sounds exciting, right, in the beginning. But with any new technology, you need to assess some kind of ROI when making this decision. If you’re presenting this as an offer to a board or management team, you also need to put some kind of calculation behind it. When we think about returns that companies can expect out of this whole process, it turns out it’s not that easy to quantify at this point. We can think about measurable KPIs like productivity increase, cycle time reduction, fewer errors, being able to do more with less, reducing manual input, and customer and employee satisfaction. But to quantify those, you really need some kind of experience and data based on previous implementations. We don’t have that now, so we can run some kind of very high-level calculation. At this point, I think it’s wiser to focus on long-term goals rather than pure mathematical ROI calculation.
Niharika: Absolutely. Agree with you on this, Anna. Let’s say an organization has already introduced AI in its finance section. What sort of insights derived from data-driven by AI can impact strategic decision-making within the finance function, as per you? What are your thoughts about it?
Anna: Well, in my career, I was always developing my time around data preparation and then data analysis and getting the insights from the data. It would be great to get rid of this first part and get straight to data analysis. It is amazing to see sometimes what you get out of working with data. Sometimes, you have one perception of what’s going on, but then you run the analysis and you see a different picture. So, of course, having the ability to process more data, having AI do all the operational work for you, and not spending too much time organizing tables or preparing the data, is amazing. I think it’s an amazing transformation going on in the industry. A lot of valuable insights can happen out of data-driven processes.
Niharika: Absolutely. As we evolve in introducing AI to different organizations, it will surely give us some impactful outcomes sooner or later. As we are almost approaching the end of this conversation, do you have any advice for CFOs or finance leaders considering AI-led automation initiatives to enhance their ROI?
Anna: Yes, I have some advice to share. First of all, this is very unusual advice from my side, but don’t focus on ROI in the first steps of implementation. Think more strategically and focus on long-term perspective rather than short-term. Second, invest in proper training for your team. Experiment a little bit with some smaller processes. Every finance organization has some ugly data processing tasks they hate, so try to start with these smaller parts and then move to bigger processes. Establish a robust data management framework because as soon as you move to AI and start to utilize more AI-driven functions, the quality of data becomes paramount. Make sure the data is structured, consistent, and has a single source of truth.
Niharika: Absolutely. You rightly mentioned that each one of us who interacts with AI in the finance domain should be very mindful of the decisions we are making. Thank you for that. I think these were some great insights that will surely help future CFOs. It was great having you on board and having this conversation. I had a lovely experience, and it was very insightful for me as well. I’m looking forward to having more sessions with you. Thank you so much.
Anna: Thank you for having me.
