Andreas Welsch 0:43

All right, so there's so much talk about what you can do with AI, how you can build new applications but the most important question is what should you even build and how can you make a meaningful impact? To someone in their work. So today we'll talk about how to do that in finance, specifically for document processing of one of the founders of a company doing just that online. And really excited to get started with this. So let's jump right in. Alright, welcome back for another episode of"What's the BUZZ?", where leaders share how they have turned hype into outcome. Today we're talking about how to help finance professionals optimize their, the work with the help of document processing with AI. And it could be more excited to welcome Ariana Ana to the show. Ariana, thank you so much for joining.

Ariana Smetana 1:46

Thank you, Andreas. I'm so happy to see you again. We've done it in about five years ago when you launched it and myself as well. This is exciting.

Andreas Welsch 1:55

Yes, absolutely. So I'm so excited to have you back on the show and hear what's new. But maybe not everybody's familiar with you yet. Can you share a little bit about yourself, who you are and what you do?

Ariana Smetana 2:06

Absolutely. So as I mentioned, about five years ago I launched into AI sphere. I launched my AccelIQ company and started developing AI strategy for the company, specifically in the mid-market. And, smaller organizations. This was actually on the brink of, when AI was really unknown. It was more about digital transformation and understanding what these new possibilities of digitization can be implemented in organizations. And then, slowly I start developing, more into AI and making sure that companies understand what to do with their data and how that can be processed with the new technologies such as AI. And over the years I have been, building products for the companies and also decided about a year and a half ago, launched my own product in the sphere of finance and office of the CFO and helping those problems to be solved with technology.

Andreas Welsch 3:01

Super. That's really exciting and you already shared a little glimpse with me beforehand of what it looks like. So we'll talk a little bit more about that and what specific problems you've seen and how you've addressed them. Folks, for those of you in the audience, if you're just joining the stream, I'm always curious where you're joining us from and to see how global our audience is today. So feel free to put in the chat where you're joining us from. And always great to see, how global we are. If you're looking to learn more about how can I actually get my employees to use AI and use AI without creating slop or low quality results. I have a new book out, it's called The Human Agent, AI Edge. It's already a bestseller on Amazon, so consider picking that up in hook shape, your organization to become more AI ready in that sense. But enough with that Ariana, should we play a little game to kick things off?

Ariana Smetana 3:49

Sure.

Andreas Welsch 3:50

Okay, so let's see. In good fashion, what's the buzz? I'll show you a little clip. And I would like for you to answer with the first thing that comes to mind and why, in your own words, to make a little more interesting. You have 60 seconds for your answer. Are you ready for what's the buzz?

Ariana Smetana 4:07

Sure. Yes.

Andreas Welsch 4:08

Okay, so here we go. Let's see if AI were a band. A music band, a rock band, something, what would it be?

Ariana Smetana 4:18

Wow. In 60

Andreas Welsch 4:19

seconds on the clock.

Ariana Smetana 4:21

My favorite band, one of my favorite band is definitely Queen. And I think the with AI, what I see, AI can hit some amazing high notes as was, Freddie Mercury with his singing and also can be a flop. I'm sure there were some bad songs created with Queen as well. So AI is really the, you know what you need to have a team. You need to have orchestration. To execute it well. So I think band would be quite a good kind of synergy with AI in my mind.

Andreas Welsch 4:52

Awesome. Wonderful. Thank you so much. Great answer. And I mainly got to watch the videos or the music videos of Queen and remember them feeling stadiums. So it feels like AI is doing that too, drawing a lot of attention and a lot of space. Absolutely. Awesome. But obviously that's not the main topic of our show. We actually want to talk more about how can you help finance professionals accelerate their processes with document processing, for example. And I know, there's. There, there's so many organizations that are either leaving a lot of potential on the table when it comes to AI. We're doing little things, we're doing a bit of productivity and everybody gets an AI assistant and we're done with our AI strategy, but also on the other side, they're boiling the ocean. Here are all the things that we could be doing and long lists of use cases and ideas that are collected up and down the hierarchy and prioritized in. They still die on the vine. So how do you find that sweet spot of making an impact and being relevant with the solution that you're building? What was one of the approaches that you've pursued?

Ariana Smetana 5:54

So here I was so spot on. So like I said, I was in a strategy. We then exactly that try to, document all the use cases potential. Applications, where the problems lie. And over the years there was a repetitive problem, especially in the office of the CFO even, mid-size companies. Quarter billion dollars. These are not necessarily small anymore, but their back office, there is a. Of a bottleneck in the processing and, getting this financial data in place and be actually on proactive side instead of reactive in kind of historical. So when we talked to the customers, specifically users they were still actually stuck in very old fashioned way. There are sophisticated systems, no matter which one wanna name ERP or CRM and all the other ones. The data was always pulled in Excel spreadsheets. Manually assembled and then reports were created and presented. And that was a really tedious and erroneous process in, many organizations. So that led me to start developing because knowing as much as we know about AI and developing products, we saw this as a potential area that we ingesting this kind of data can help professionals in this area. Really be far more accurate and also have ability to generate these reports on demand and make a decision actually growing faster and deciding when things are happening instead of retroactively looking backwards. That's kinda, aha moment for companies I work with, and then led me into developing many different use cases in the sphere of CFO and also now in COO kind of operational data.

Andreas Welsch 7:35

So one of the questions I have, and I've previously worked with finance organizations too, is. Numbers have to be correct. There's no margin for error. There's no, oops, we get the decimal point wrong or something like that. Or we booked it on, on, on the wrong account. How are you approaching that with finance? When we know we are, we do not have a deterministic system but rather a probabilistic system now with AI. With approximations, with confidence scores with we're somewhat certain that this is right, but it's not a hundred percent. It's never a hundred percent. How do you navigate the tension between it has to be absolutely right with, Hey, this is how the technology works and it's more probabilistic than deterministic.

Ariana Smetana 8:16

Seven. Very good question because what we build is really not a wrapper on the AI LLMs that typically most people do, or, using the public access to LLMs. So we build layered approach, so we give access. We c partly, we really want to be human in the control. So the user who is the domain expert, needs to be deciding what data needs to be ingested. And when the data is adjusted, then it's validated for accuracy. That's the first layer. The next layer is when this determined. The user will see exactly where the things or problems are happening. There are discrepancies, errors, and that can be corrected before you start analyzing. And then also we build a deterministic layer, so the calculations are deterministic. This is not going to LLMs for guessing what could be or would be, calculation for the specific problem. So the the layer afterwards. Is, built into, sending the data you want to have for probabilistic area, and that is reporting and summarizing so multitude of layers, multitude. Also, touch points for the human to be in control. And they can also, generate their own, questions, meaning, this is my calculation, this is how I want to do it. And you can lead that so you know it's in control actually, the domain expert, not in the domain of ex of machine to the kind of dictate the output. So that. Our approach because we know that, current AI, generative AI specifically has no ability to be accurate to the point of what is needed for CFOs and operations.

Andreas Welsch 9:49

So what I'm hearing is using the strength of each actor in, in, in this process from doing the calculations with more traditional statistical methods or correct mathematical models to generative AI when it's probably more about giving context or more, longer form to the human making the final decisions.

Ariana Smetana 10:09

Absolutely from the beginning to the end. We not eliminated QME. Enable them to do it faster and be still in the control of the output.

Andreas Welsch 10:17

So now you said, Hey, you've you've been working on this for the past 18 months. And when I talked to founders in the technology space I hear so many great stories of, hey, we were able to prototype this super quickly. We're able to go from idea to prototype to final product within a matter of weeks, within a matter of months. AI is definitely shortening that, that timeframe to take something to market. How have you approached that in your case?

Ariana Smetana 10:42

So the, interestingly enough, yes, the ladies vibe, coding, no coding, low coding, and I don't know what even the names are these days anymore. We didn't take that approach. We really want to, started with a problem and understanding where the problems lie in the users and professionals and can interview them, discuss with them, and building the solution with them. That was, the main approach for us. So that's reason it taken so much longer. We put a lot more emphasis on keeping that human in the loop, actively testing it, and then piloting as we go along. We didn't wanna, you know what ma many companies do, just give it for free, run it with it. As fast as possible and then making LOP happening, and errors and the trust deteriorating. We believe that professionals in the office of CFO and operations need to be filling the trust, have the accuracy, and also have the ability that they know the data is safe. It's not leaking in many different directions.

Andreas Welsch 11:43

I think that's so important and while I get excited about vibe coding too and the opportunity and ability to, spin up something real quickly. You still need to do your due diligence. You still need to know, what data, first of all, do I need, how do I secure it? How do I make sure that the outputs are correct so that professionals to your point, can take action on it and can trust that the recommendations, that the information is correct and complete and accurate, and that

Ariana Smetana 12:08

yes,

Andreas Welsch 12:08

maybe right, and you need to figure it out if it is correct and incomplete, correct. So

Ariana Smetana 12:14

of course there is, many other ways to do it as well.

Andreas Welsch 12:18

Sure. But I also wouldn't be surprised if those are some of the ways that we'll read more about in, in the news when things go sideways because security was neglected or data is leaking or

Ariana Smetana 12:27

Yeah.

Andreas Welsch 12:27

Somebody got unauthorized access happens to some of the best organizations I think McKinsey was in, in the news a couple days ago about their in internal AI agent. Being hacked by white hackers and so on. So anyways, long story short, you definitely want to do your due diligence and especially when there's some legal, financial, or reputational risk involved.

Ariana Smetana 12:47

Yes, absolutely. That's our approach for sure. And we also, just to mention, even a, as a small organization, we put emphasis on getting a SOC two approval and following all of these policies because we know that data and security and policies around that is very important to have.

Andreas Welsch 13:05

That's another important point, right? The second you want to take that to an organization and they'll ask you for compliance and for reporting and adhering to these standards, it's good to, to think about that early on and from the beginning rather than

Ariana Smetana 13:17

exactly.

Andreas Welsch 13:18

Once you have these conversations and you need to fix it afterwards.

Ariana Smetana 13:41

Correct.

Andreas Welsch 13:41

Now you've. Taking the new product to market. And you said you did a lot of iteration and worked together with prospects, with customers. You got a lot of input and feedback. You really deeply understood what is the problem that organizations have and how can we solve it? How. How did you go about validating it? You mentioned a few things already by asking organizations to, to test it or pilot it. What were some of the other strategies that you, em employed?

Ariana Smetana 14:09

So predominantly, we approached it two different ways. We go directly to the organizations which bring those problems to us, and then we test with their specific use case and the data they have, whether it's compliance, whether it's, reporting on the variances, producing a 13 week cashflow projections. There are many different use cases that we worked on, and the beauty is that we, our plan for the product is to be billed based on what. Really the users need. It's not like we are going to build it and see who, who is going to use it. Going backwards, searching for the problem. No, we starting with the problem, making sure that we address them in the best way possible. We also work with the fractional CFOs and making sure that they can service their pool of customers as well through this tool and enable them to do that even better and more accurately or, at scale that they need to work at. So these are kind, different approaches. Maybe not a traditional way, but I think, we are not living in a traditional kind of a software development at the moment. I think AI is very unique type of business. And a beautiful thing is that, the users who are domain experts and non-technical, now they have a technology which can help them directly in the natural language to help them with the business and help them with the work.

Andreas Welsch 15:29

How much do they actually need to know about the technology underneath to be able to use it?

Ariana Smetana 15:34

For our tool, we purposely wanted to, work with non-technical people. We made it extremely light on the, it, there is no, integrations and heavy requirements and heavy lift from the company. It is mainly driven by the user and through natural language processing. It enables them to ask the questions they would ask as a colleague, they can see the, how this is going to process. We try to demystify the black boxes as much as possible, showing them how that works, giving them control of, deterministic calculations and then the output they wanna generate so fully. Different approach. So it's not just, press a button and then something comes out as most systems are built at the moment.

Andreas Welsch 16:15

And what do your customer, what do the professionals in finance use it for and how does it work? When you say natural language, but you already mentioned there they work with different documents. There, there's some more traditional calculations for accuracy that are happening. How. How do professionals work with the tool or with the system?

Ariana Smetana 16:35

So what we built we made it as, simplistic ui, meaning, user interface is very simple and intuitive. We didn't wanna have extensive implementation and integrations we ingest the data they need to work with, they determine what the data is for the output they need to generate. So they have a full control of, being human in the input side, and then generating this data validation as a first step following that. You can then generate, okay, you can see the preview, what was generated. And even if you're not an expert in prompting, we build a specific tool on our backend to en enable them to be more accurate and actually getting the output that they need to get. And they can still, like I said, in additional tools that we have in the platform, you can enable that corrections and making sure that it's really understood and produce what you have. The speed is still there. Absolutely. You are enabled to do work much faster, but you're still in control and you have ability to get the output exactly the way you need it. Whether it's a board reporting, whether they need to have, compliance reporting to the bank. Any of these reports can be generated. With through this tool, and we've seen that, customers are telling'em additional use cases they see valuable, and then we put them on the roadmap to develop.

Andreas Welsch 17:54

That's awesome. Sounds like if they're using it, they also get ideas for new new ways to use it or new cases if you will. Exactly where it can be helpful. Now we've obviously talked quite a bit about the approach you've taken, how you've identified that there's a need or a gap in the market. What's the product called for anyone listening or watching Who would love to learn more about it?

Ariana Smetana 18:13

We wanted to stay with the common language that most companies, no, it's called Excel Insight. And we know that, most professionals in this area still work extensively in Excel spreadsheets, whether, other spreadsheet tools they may have. And we think that, we are not going to enable them anything to do better in Excel, but we want to extrapolate this data and give them capabilities beyond Excel. So this is touching on the Excel because of their, where they live and how they utilize. But we are giving them, much more capabilities, extracting these data in the way that they can actually do it better, more accurately and, trustworthy output versus, copy and paste errors that often can happen.

Andreas Welsch 18:57

That makes a lot of sense. And I'm sure you've lived and breathed that earlier in, in your career, if I'm not mistaken. You're, you were in finance, right? For a long time,

Ariana Smetana 19:05

correct? Yes. I did. And like I said, no matter how sophisticated company is, and one of them, or fortune 10 companies they really, still have all the beautiful system and you still end up doing a lot of preparation and analysis in Excel and in the spreadsheets, and then pre preparing reports, and that can be very tedious and erroneous as well.

Andreas Welsch 19:27

Yeah. So I'm curious when you work with finance professionals, when you work with finance leaders, obviously there's a lot of talk about AI, there's a lot of talk about agents what they can do, what they will be able to do, what they might do that you don't want them to do. What are you seeing when you talk to finance professionals, when it comes to AI? How open, how concerned are they? What are some of the big things that get them excited, but other things that they need to have in view as well?

Ariana Smetana 19:56

So I think the, this year I definitely see much more interest in getting from talking about AI into let's use it and do it. And that is beautiful thing is because the only way somebody can really understand AI is by doing it, experimenting. And even if it's, on a small scale that is very valuable to get hands on and start testing it. And the beauty of, our approach was that we wanted to meet them where they are. So we didn't really necessarily build all the bells and whistle of the agent system, especially autonomous one, because that can be rather scary proposition.

Andreas Welsch 20:33

Yeah.

Ariana Smetana 20:33

So we still want them to be in control and deciding the input and output and, how the process works. Agents can be, in. Later stage, and especially if anything needs to be automated, that can be, easily done. But however, they need to understand how that works and that is accurately done every time. And then, they need to be removing themselves from the daily, task, which are tedious to move something more strategic, reviewing critical thinking about what the output is and how this data can impact the company.

Andreas Welsch 21:07

Awesome. I think that's so important to, to show this as a as a progressive roadmap of steps especially when it comes to trust and as we've been saying all along in, in this episode, you really need to understand is this accurate? Is this correct? Can I act upon this information? So the more trust you build, the easier it'll be to, increase the level of automation and level of autonomy.

Ariana Smetana 21:28

Yes, absolutely.

Andreas Welsch 21:30

Now for my last question I'm curious too, we've obviously known each other for five years or something like that.

Ariana Smetana 21:36

Yeah.

Andreas Welsch 21:36

Before bots were swarming LinkedIn and people put their own thought leadership out. But what is it that, that you've seen over the last five years that gets you excited working on big problems, complex problems, like the ones you're working on in, in your company right now? What gets you excited about this?

Ariana Smetana 21:54

What gets me excited that on one hand I believe that, the more people learn about it, it is going to be improving overall, I think approach and understanding, because that will give you confidence that we can all use it in the right way. And like anything, if you go in example, when we start, riding a bicycle, we were all falling down and didn't quite know what to do it, but the more we did it, the better we became. So I think the same principle applies here. I recommend that everybody get their hands on even, the commercial grade tools, whether it's, charge GPT or any other. And test it out. Of course. Very careful about, what kind of data are you putting in those systems? And there's reason, we purposely built our system very integrated with all the security and governance built into the product, not only around the organization, which is sometimes you'd also misunderstood, but I think the. The more you do it, the more you can, get understanding how it works, the better it'll be. And that is exciting part. I see that actually that curiosity and learning mentality is growing and that is part of it. Instead of being always let's do, tried and test it this time, we need to start thinking is let's experiment and see what we can do better.

Andreas Welsch 23:08

I think that's such a great message, and I hope many of you watching in, in, in listening to this feel inspired by Ariana's comments as well to get more hands on and try it out. It's never been easier to do it. We have all all tools available on our phones and browsers and whatnot, so it's really about getting started. Yes, Ariana, we're getting close to the end of the show and I was wondering if you can summarize the key three takeaways for our audience today.

Ariana Smetana 23:33

So my takeaways would be, like we said, get your hands on, but get under the handle products that are actually built for the purpose that are specifically, targeting your problems in the organization, or specifically targeting maybe your segments in the organization. Not every product that is built on general kind of approach of AI. We'll give you the answers you need. Really specifically, look for the products which help you with that. Get your hands on, learn, be curious, experiment and work with, trusted vendors that you know, that you can get from, knowledge from, like you said, LinkedIn. Now it's, crawling maybe with bots, but there are so many people who are actually still very authentic and there to help and provide the value. Like yourself.

Andreas Welsch 24:23

Yeah. Thank you so much and like you right back at you.

Ariana Smetana 24:26

Thank you.

Andreas Welsch 24:27

Awesome. So if you're not following Ariana yet, please do follow her. She puts a lot of good content around these topics as, as well. Alright. Ariana, we're at the end of the show. I want to say thank you for sharing your expertise with us. It was really insightful and interesting to learn about how you've approached this, how you're taking your product to market, and the the specific need that you're addressing that finance professionals have when it comes to pulling data from different sources, creating reports, and being able to interact with that data in natural language. All right. Thank you so much for joining Ariana.

Ariana Smetana 25:02

Thank you. And Andreas, it was my pleasure.

Andreas Welsch 25:04

Alright folks, see you next time for another episode of"What's the BUZZ?". Bye-bye

Ariana Smetana 25:09

Bye.