Automate Finance End-to-End

In this episode of "Full Tech Ahead," host Amanda Razani speaks with Prashanth Saradesai, Head of AI for Wiss, about how accounting and finance organizations can successfully implement artificial intelligence. 

Saradesai shares insights from evaluating over 200 AI vendors, noting that many finance teams hesitate to adopt AI because they struggle to prove a clear Return on Investment (ROI). He explains that treating AI as an isolated "point solution" (e.g., merely extracting invoice data) is ineffective; instead, organizations must automate end-to-end workflows to see real value. 

Furthermore, he emphasizes that in the strict finance sector, "good enough" is unacceptable, making human-in-the-loop processes essential to mitigate AI hallucinations. 

Finally, he advises companies to refine their internal processes, build strong data foundations, and prioritize change management to ensure a successful AI adoption.

Key Quotes: 

"In finance, 'good enough' is not an answer. You can't say, 'hey, my finances are good enough.' Even one number doesn't work; it flows through your financial reporting."
"If leadership has the vision of bringing AI to the organization, but if you are not bringing everybody in the organization... there won't be any value."
"Start investing in AI from the perspective of using it for your end-to-end workflow... being AI native, thinking from a standpoint of making all your employees AI fluent, are the North Stars."

Takeaways:

Focus on End-to-End Workflows: Using AI for a single task like invoice extraction won't drive significant ROI. AI should be implemented to handle the entire workflow—from extraction and matching to approvals, posting entries, and reconciliation.

Fix Processes and Data First: The biggest mistake companies make is starting with the technology. Organizations must first clearly define their internal processes, clear bottlenecks, and build a solid, well-organized data foundation before deploying AI.

Prioritize Change Management: An AI initiative will fail if the vision stays only at the executive level. Training employees on both the advantages and the limitations (like hallucinations) of AI is crucial for successful, company-wide adoption.

Capture Decision "Context": As the industry moves toward autonomous AI agents capable of "computer use," the organizations that will succeed are those building "context graphs"—documenting not just their raw data, but the specific reasons and context behind their past business decisions.

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Transcript

SPEAKER_01 0:19

Hello and welcome to Full Tech Ahead. I'm excited to be here today with Prashant Sarah Design. He is director of AI for WIS. How are you doing today?

SPEAKER_00 0:31

Hi, Amanda.

SPEAKER_01 0:34

I am doing well. Can you share a little bit about WIS? What services are provided there?

SPEAKER_00 0:41

Yeah, absolutely. So WIS is one of the top hundred forms. We focus on mid-market. We provide accounting tax as well as client advisory services within New York, New Jersey, and even nationally as well.

SPEAKER_01 1:00

Okay, great. Well, we're here today to talk about accounting firms and finance organizations and how they can select the right AI implementation partner. So you, I think you spent the last year doing a study of over 200 vendors. Can you talk a little bit about that?

SPEAKER_00 1:17

Basically, our approach is as technology was evolving. We see how AI is taking over all aspects of the business. So we started evaluating. So WIS as a firm uh decided to go in that direction very early. So we we took a stance that we are gonna be AI native and we are gonna transform our own business internally through AI and and the evolving technology that it is, right? So so we started um evaluating, uh, and then that's how there were a lot of startups coming up in the market, and we started evaluating them. So we have our own internal framework through which we evaluate these uh different vendors because as you know, the the venture capital field is like so so uh bustling for last few years from from the advent of ChatGPT, right? So uh basically we started reviewing a lot of these finance uh vendors or the AI platforms uh which claim to be having solutions which will solve our problems. So we started looking at uh some of these, and what we what we learned is only around you know 60% of the vendors that we looked at mainly have are they are trying to use AI in some way or the other, right? But rest of the market is is not really using AI. They are still in the initial stages of adoption, or some of them are just sitting on the on the beach thinking about it and they don't know how to go about it. I think that's where somebody like WIS comes in and helps uh those organizations. So a lot of our clients are asking us, hey, there is Chat GPT, Claude, there are so many solutions today. How can we implement it, right? That's where we are helping them implement those solutions.

SPEAKER_01 3:20

Okay. So why do you think it is that so many finance teams are not yet exploring AI options? Is it that overwhelm of the number of options? Or from your experience, why is that?

SPEAKER_00 3:34

Yeah, we like I feel and and what we are seeing at at WIS is when you look at AI adoption, you can have some of the point solutions. What I mean is like, hey, I want to process invoices. You can use AI to just extract invoices into a readable format, right? But that's not gonna drive ROI for these companies. So when we look at the whole workflow, finance workflow, uh, for example, then if we can bring in AI, that's when it's gonna make a larger impact, right? So so for the same invoice uh example, if we can have a workflow which can not just extract invoices, it can do three-way match, it can code the GLs and then help us to go through the approval process these organizations have, then you post an entry, then you perform reconciliation. So if you can, if AI can execute an end-to-end workflow, that's where it's meaningful, and that's where organizations can show the return on investments that they are making, right? So a lot of them are not able to. And some of the challenges that they have today is they don't have their processes well defined, right? We come across that. There are two major aspects I want to mention. One is their process, because they haven't defined their process really well. There are a lot of bottlenecks, there are a lot of manual processes, right? So they need to fix those aspects first before they say, like, hey, I want to transform my entire workflow. And the second thing we see is the data foundations, which is very critical for AI, right? So if you AI is as good as what data you provide to it, right? So these LLM models are very good these days, but again, they are trained on the internet data, right? So if you want to have more specific solutions for your organization, then you need to have a good data that you feed into LLMs, right? So so we see there are a lot of gaps in the data uh foundations that these organizations have. So these are two main concerns. And then the third one I just want to mention, which I have lived through my experience, is also the chain management process. So if you look at it, if leadership has the vision of bringing AI to the organization, but if you are not bringing everybody in the organization, have value, there won't be enough value that you can define without doing that. Because if if everyone in the organization understands what value AI brings, then I think you will be successful as you roll these out. Because like that's where a lot of these AI initiatives fail with with the change management aspect for the organizations.

SPEAKER_01 6:26

Yeah, imagine communication and training are critical.

SPEAKER_00 6:31

Absolutely. Absolutely. I think defining that vision, training your employees on advantages of AI, and more than advantages, I think what are the limitations of AI? I think if you can talk through the limitations, that's when everybody will know, okay, these AI models can hallucinate, right? So when they hallucinate, how do you reduce that hallucination, right? So you know, and then the other aspect of it is especially in finance, you cannot rely on an LLM model that it is generating results, right? Because what happens is in marketing and some other field, maybe if you're developing a campaign, it's okay. You have text and reviews, but in finance, good enough is not an answer, right? So you can't say, like, hey, my finances are good enough. No, even one number doesn't work, it flows through your financial reporting. And there are other challenges to it, right? From a from a financial reporting standpoint, from an audit standpoint. So so from that angle, it is very, very important for your employees to know the limitations, have the human-in-the-loop process for your finance workflows that that is critical for the success.

SPEAKER_01 7:50

Yeah, especially in the finance sector, how important or critical is it to have no errors? Obviously, I'm sure many leaders are worried about AI and LLM errors.

SPEAKER_00 8:05

It's massive. It's massive, right? Because as I mentioned, AI errors in finance uh are not cosmetic, right? You have you have you have a lot of repercussions uh if you get get any numbers wrong. Uh these general AI LLMs give you plausible, right? Like if you look at these LLM models, they are non-deterministic, right? So they are gonna give you probabilistic answers to the questions that you put in. So you need to be you need to be careful, right? Otherwise, repercussions are dangerous. And and that ties back to why CFOs, finance advisors, are a little hesitant on implementing AI at a scale that we think it's gonna work. And the good news is this field is evolving so fast. What we spoke last month, like we can be conflicting them if we speak today, because they are solving. These AI models are evolving exponentially. Whatever challenges we are seeing last month, they are getting solved, right? So if you look at the pace, the the the positive aspect of it is for FCFO, you need to constantly look at it. And and and what they need to do is be on the train, right? Like don't don't just don't sit and watch. They need to get on the train, build the processes, test it out, figure out the depth and the limitations, and then move ahead, right? If you if you don't do this, the you will be left behind. There'll be somebody who's gonna work through this and they're gonna disrupt the market for you.

SPEAKER_01 9:37

I imagine one thing they're really focused on is that return on investment. So how do they track that?

SPEAKER_00 9:45

Yeah, that that's that's a very, very good question. I think I think in the market we see that ROI has been a big, big uh question mark because these technologies are evolving. Uh, there is huge investments made by organizations, but they are not able to show the ROI for that, right? So, so I just want to give this background because like last year, right, we we we spoke so much about agents, right? And then those agents were doing one step, two step, three step at a time, right? So I think like recently, last two, three months, with some of these more powerful reasoning models, which are able to solve for a lot of it. They're not just able, they're not just solving one, two, three steps. There is agentic orchestration, multi-agent uh orchestrations that are happening, and they are able to reason very, very well and execute end-to-end workflow. So I think that's a big shift that has happened with the technology, right? Now, when you think of ROI, you are able to look at it from that lens. And how do organizations track? I mean, you need to define a very robust metrics for all of this, right? So now if you're starting, if you are starting and automating an end-to-end workflow, you need to set your metrics and be able to track them, right? So so what's happening is if you don't define metrics before you start investing, then you're just like flowing. You don't have a way to look at it from and and evaluate it for return on investment, right? So what is the biggest thing is to come up with metrics, track them regularly, right? So you know how you're progressing, figure out where there are gaps, and then re-evaluate and if you need to invest in something else or not, right? Because sometimes organizations get committed to a vendor or a vendor solution, and then they think, hey, ROI is gonna come. They were sold on a on a on some vision, but if you're not tracking it, you spend that money for six months, eight months, and then you realize, like, no, this is not the right solution for me. And you couldn't, you couldn't show anything at the end. And and we have seen a lot of cases like that, right? So that's why I think defining that metrics becomes very, very critical.

SPEAKER_01 12:12

What from your experience, what are some of the common mistakes or roadblocks that finance teams make when they're implementing these AI tools?

SPEAKER_00 12:22

I think I I want to call out three three mistakes here. So the first one is I say a lot of them start with technology, right? So I mean, if you want to solve a problem, hey, I want to use this technology to solve problems, but instead, process is where you need to look at, right? So we we we do uh advise a lot of our clients where first thing we do is looking at their process, understanding their process, what are their data, how does it look, how accessible the data is, right? So once we were able to map that, then technology conversations can come in, right? So I think that's the first mistakes a lot of them do. Um, and then the second one is uh treating AI as a point solution, right? Like as I mentioned earlier with an invoice example, like, hey, you want to solve just one. No, you you need to look at it holistically and see how you can draw out. And in some cases, we have come across where you define the workflow, you think a technology can solve the problem, but you may not be able to solve the whole workflow at once, right? So sometimes you need to have that plan to solve the whole workflow, but you have to take smaller bytes and be able to uh get there in two months, three months, right? So if you try to solve all of it, there will be challenges. So I think that's the second second mistake a lot of them do. And then the third is the chain management, right? I think the the chain management is is very crucial for organization. Starts from the leadership and then drawing the vision and then bringing the team along is is crucial. And sometimes what happens is there is a vision from the leadership. It doesn't percolate down to the uh people who's who are executing day-to-day work, right? And and there is that big gap uh which a lot of organizations don't close up front.

SPEAKER_01 14:18

So that that's another challenge or a mistake that well, this, as you mentioned, this technology is rapidly advancing. What do you see on the horizon in the future in the finance sector?

SPEAKER_00 14:34

It's it's evolving so much, right? As you mentioned. Uh so on the on the horizon, I think uh I think there are a lot of startups, a lot of uh solutions being developed in the market, right? So organizations have to look at it where they invest in those kind of solutions, you buy those solutions, or where they they need to invest in building something internally, right? So so especially just quickly, wherever there are data, your proprietary data that you don't want to give out to your vendors or or any of the LLMs, and you need to make sure that you protect your IP, right? Because so you need to look at it from that perspective. And the second aspect of what's happening in in the market and with these technologies is uh we were talking about agents doing a workflow, right? Where it is heading with things like open claw and what we see computer use aspects coming into play. So the the vision are the things that are going the direction it's going is uh like these computer use, if you give enough context, if you give all the data that it needs and you give access to these models, they are able to execute everything 24-7, right? I think I think the horizon uh is is basically you will have systems at some point which will be uh edge-based, or like what I mean is like you can have one server with cunt your data is contained within that server, and you give access to an agent, uh, something like OpenClaw, uh kind of a solution. Now, a lot of the bigger organizations are coming up with similar solutions, right? Like Claude came up with uh uh a solution dispatch, and then Nvidia has uh a solution that they announced recently. So all of this as they mature and as security aspects come into play, as you're able to put your data securely into these uh these servers uh and then give access to these agents, they're they're probably gonna do a lot of the things uh 24-7. I think I think we are heading in that direction. I think people who are are the organizations who are investing in capturing their context, right? Context is beyond data, what are some of the decisions that you make, right? So so for we run a lot of client uh that we advise and and a lot of clients within their organization, there are a lot of decisions made, right? So so are you capturing those decisions, right? Because LLMs, you can give data, but the decisions are not captured. Like organizations who can capture those decisions, who can capture, hey, why did you take a decision? What were the reasons? And if you capture that kind of an information, which is more of what we call as context, or nowadays it's also referred to as context graphs. If you are building that context graph, that's gonna give you a lot of leverage when tools like these computer use and open claw things you want to adopt. So if you have that built out, people who are doing that today will be successful, able to get to ROIs much faster than others.

SPEAKER_01 18:03

It will be interesting to watch the future unfold for sure.

SPEAKER_00 18:07

Absolutely. Absolutely. I mean it's it's exciting for for me having been in this field, the the pace and and the and and the possibilities are just unbelievable. It it it amazes me every day when I wake up and and see the news, see the things coming up. It's it's so exciting.

SPEAKER_01 18:29

Well, if there was one key takeaway you could leave our audience today with, what would that be?

SPEAKER_00 18:34

Start investing in AI from a perspective of using it for your end-to-end workflow. And when you look at it from that lens, you start defining your process, you start clearing your bottlenecks. And then once you get there, you make sure your data gets. So I think that question will lead to multiple uh other things that actions that you can take to get there. I think if if somebody is just like trying and not really investing in AI is is gonna be really dangerous uh in a way. So I think a lot of organizations are doing in doing it in in phases or in in smaller bytes in a way. But I think being AI native, thinking from a standpoint of making all your employees AI fluent are the North stars that that organizations have to set up.

SPEAKER_01 19:32

All right. Well, thank you so much for coming on the show and sharing your insights with us today.

SPEAKER_00 19:38

Thank you, Amanda. Thank thank you for uh giving me this opportunity to uh share share our thoughts and thank thanks for uh bringing me and and Viz into this conversation.

SPEAKER_01 19:48

And thank you to our audience. If you have any questions or comments, leave those below and I'll be sure to respond to it. Have a great day.