What if companies rushing to deploy AI agents are overlooking the basic problem that much of their business data is still trapped inside PDFs, emails, attachments, spreadsheets, and paper documents?
In this episode of Tech Talks Daily, I speak with Sylvestre Dupont, co-founder and CEO of Parseur, about why successful AI adoption begins with making business data usable, why traditional automation can often outperform more sophisticated AI systems, and how he built a profitable global technology company with six employees across six countries without venture capital funding.
Sylvestre introduces the concept of data liquidity, the ability to move information from the documents and systems where it is trapped into the applications, workflows, and AI systems that can put it to work. Companies may have years of valuable operational data, but if that information remains buried inside what Sylvestre calls "digital concrete," even the most advanced AI models will struggle to produce useful results.
The conversation examines why structured data extraction has become increasingly important as companies invest in AI agents, copilots, and automated workflows. Sylvestre explains that better models alone cannot compensate for incomplete, inaccessible, or poorly structured information. Before businesses can expect AI to automate complex processes or support better decisions, they need reliable ways to collect, structure, and move data between systems.
We also challenge the assumption that every business problem now requires an AI solution. Sylvestre explains why AI should be treated as one tool among many and why deterministic automation remains the better option for repetitive processes where accuracy, consistency, and explainability matter. Parseur itself combines AI-powered document processing with template-based extraction and traditional workflow automation, using each approach where it performs best.
Drawing on Parseur's experience processing more than 100 million documents annually, Sylvestre describes the different stages companies move through as they mature their automation strategies. Some begin by manually uploading documents and downloading extracted data. Others automate document ingestion and connect information directly to accounting platforms, CRM systems, and other business applications. The most advanced companies add exception handling and human review processes for situations where automation cannot reliably complete the task.
Data privacy and security are another major part of the discussion. Sylvestre shares the questions technology leaders should ask before sending sensitive company information to AI-powered platforms, including where data is stored and processed, whether customer information is used to train AI models, how deletion requests are handled, and whether vendors genuinely understand the regulations and security standards they claim to follow.
For founders and bootstrapped entrepreneurs, Sylvestre also shares an alternative perspective on building technology companies. Parseur has remained profitable, globally distributed, and customer-funded rather than pursuing the venture capital model of rapid expansion. Sylvestre explains why he prefers customers to determine the company's priorities, how asynchronous communication supports a team operating across multiple time zones, and why building a sustainable business can offer founders greater control over product decisions and company culture.
This conversation offers practical lessons for technology leaders deciding where AI belongs in their operations, operations teams trying to reduce repetitive manual work, and founders questioning whether venture capital is the only route to building a successful global software company.
The message throughout the episode is simple: AI can be extremely useful, but companies still need reliable data, appropriate technology choices, strong privacy practices, and well-designed business processes. Sometimes the smartest technology strategy begins by solving the boring problems first.
Useful Links
Connect with Sylvestre Dupont or Follow on X
Learn more about Parseur
[00:00:04] - [Speaker 0]
Welcome back to the Tech Talks Daily podcast where every day we separate technology hype from the ideas that are genuinely trying to change the way that businesses operate and for the better. And if you spend five minutes on LinkedIn or walk on the show floor at almost any tech conference today, you'll be surrounded by conversations around AI agents, copilots, autonomous workflows, and the next big breakthrough. But what if many organizations are trying to build the top floor of the house before they've ever even laid the foundations? Well, my guest today believes that that is exactly what is happening right now. And he will argue that businesses are rushing to deploy AI while much of their most valuable information remains locked away in PDFs, emails, spreadsheets, and attachments.
[00:00:59] - [Speaker 0]
And before AI can deliver meaningful business value, the data needs to be accessible. And my guest today is the cofounder and CEO of a company called Parser. And we'll discuss why he believes boring automation often delivers better results than the latest AI trend, and what data liquidity could be the missing ingredient in many digital transformation projects. And, ultimately, why reliability, explainability, and privacy, how all these things matter just as much as artificial intelligence. But enough from me.
[00:01:34] - [Speaker 0]
Let me introduce you to my guest right now. So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do?
[00:01:46] - [Speaker 1]
Yes. Well, thank you, Dil, for inviting me. So my name is Sylvester DuPont. I'm the cofounder and CEO of a company called Passer. What we do is that we automate data extraction from documents like emails, like PDFs, like attachments, or or whatever you send at us.
[00:02:07] - [Speaker 1]
We are fully bootstrapped. We don't have any VC founding, and we've been around for about ten years. We're gonna celebrate our tenth anniversary later this year. So we are, like, six people. We're keeping it small.
[00:02:20] - [Speaker 1]
We are across six countries from Singapore to to Florida, and we have been operating for for quite some some time now. What what is special about us is that contrary to the wisdom that many people say that when you are a small company, you have to niche down and find your industry and try to to restrict what you do. We are keeping it very generic. So we work for any types of industry, any type of companies, any type of documents, and that makes me very happy because it means that every day I get to talk to people from very different way of lives and industries, and every day I'm learning about some new jobs or some new activities, and it's quite motivating.
[00:03:06] - [Speaker 0]
And I just love how many miles you're covering now. You must have a unique vantage point of what everybody's talking about. I mean, here in The UK, everywhere I look and beyond when I go to tech conferences around the world, show floors, keynotes at tech events, and, even our LinkedIn news feeds are all just full of companies talking about AI agents, copilots, autonomous workflows. But one of the reasons I was excited to get you on the podcast today is you've argued that most organizations are actually skipping over as much a much more fundamental problem, and that is what is data liquidity? And and why do you tell me more about that and why you think it's the missing piece that is missing from so many digital transformation initiatives out there.
[00:03:54] - [Speaker 1]
Yeah. So I guess maybe the the easiest is to use an example. So let's say you you, Neil, you want to double your listeners in twelve months. Yeah. So if you go to your ChatGPT and you say, oh, hey, Chat.
[00:04:07] - [Speaker 1]
How can I double my listeners in twelve months? Make no mistake. Well, if you only say that they say that, of course, Chat will say, oh, well, of course, very confidently, we tell you to maybe make partnerships or to put some ads somewhere or whatever, but it's it's gonna be very low added value. But if you would send some more data to chat and say, well, this is how much I have today. These are the people in my space.
[00:04:33] - [Speaker 1]
These are the the the the types of people that are my listeners, etcetera. Then, hopefully, you will get much better recommendations from an AI. Yeah. So that is the problem of data liquidity. So for me, data is like money.
[00:04:50] - [Speaker 1]
It's only valuable if it flows or a bit like maybe a spice in June. It has to flow. So you can have all the information you you require to do the task to be the best company and and have the best idea. But if you don't feed this information somewhere else, if you don't use the all the data you have, you will have very little outcomes. So before we can talk about using AI agents using Copilot, we have to talk about how can a company or person or an organization make their valuable data that they need on the day to day liquid, which means how can they make it flow for where it's required, so that it can be valuable.
[00:05:35] - [Speaker 1]
And this is what we try to do at our level with documents. So, of course, documents have a a lot of messy and structured information that every single company requires for for the day to day. But it's not easy to use that data because it's locked in pages and and deep down in tables in in page 122. So we try to make structure of it and have a clear structure the structured data makes it very valuable for for AI and agents.
[00:06:07] - [Speaker 0]
And when I was doing a little research on you, I was reading how you describe a huge amount of business information is being trapped in what you call digital concrete, which consists of PDFs, emails, attachments, and spreadsheets, etcetera. I love that term, digital concrete. I think it sums it up perfectly. But how how widespread is this problem, and what kind of impact does it have on organizations that are still trying to up modernize their operation?
[00:06:34] - [Speaker 1]
Well, I don't know any company that doesn't have Yeah. PDFs or emails or that's our day to day life. And maybe even before we talk about digital concrete, we could just talk about concrete. Yeah. Like, earlier this week, I was talking with a prospective customer, and he was telling me that he's a small business owner in operator in Texas, and their job is to refurbish turbo pumps.
[00:07:01] - [Speaker 1]
So I learned that turbo pumps is the pumps they use to make vacuum for creating microchips and stuff like that.
[00:07:08] - [Speaker 0]
Yeah.
[00:07:09] - [Speaker 1]
And so it's these pumps are apparently very expensive, very brittle, so they need to be refurbished and maintained every year or so. And what they do is that they get the pump in in the in the workshop. They have some form, form some paper, and with a series of checks that they have to to make to to see in which state the pump is, and they write down they have against every check, they write down the the values and then make sure that it's correct or not. And the person I was talking to was telling me, that works. I mean, the the agents are very happy.
[00:07:42] - [Speaker 1]
The the the system is working, but what he wanted to do is to be able to see if some models of pumps would have more failures than other or if there was some recurring problem that they could report back to the manufacturer to maybe prevent the maintenance from happening. And he couldn't do that because once the the form were filled and the the repair was done, the the paper would end up in the cabinet somewhere, and that's it. So he was contacting me to see if we could get his document scanned and extract the information so that it could collate it, make some statistics, and and make it work better. So already, the first step was done. He was considering going from concrete to digital concrete, which means scanning documents, and he was hoping us that we would make the data fluid and liquids by extracting the the right information from those those documents.
[00:08:36] - [Speaker 1]
And, I mean, as I was saying before, the the the problem is endless. Every single company, even the most advanced tech fin fintech NFT, whatever the acronym is of the day, that that that is the most trendy tech. I'm sure they have invoices that were received as PDF. I'm sure they issue some quotes to some customers that are PDFs. And if you want to automate that, you need the you need to get the data out of those documents.
[00:09:04] - [Speaker 0]
And I think there's also a growing tendency just to immediately reach for AI whenever a business process needs improvement. So in your experience, where are companies maybe overcomplicating problems that could be solved with a much simpler automation approaches because it it does feel that AI is that one size fits all fix for everything, but sometimes it isn't. Right?
[00:09:26] - [Speaker 1]
Yeah. Exactly. And and AI is a tool. It's a solution for me. So you should use a tool for what it's good at, so understanding context, have have answers to a a few touchy questions that require some knowledge that you can do.
[00:09:40] - [Speaker 1]
But you cannot trust AI for very reliable and repetitive operations. For that, you you could usually have much better solutions by having some workflows that are deterministic that, you know, if this con this value is x, then the roots that document to that person, otherwise, do this. And then you are certain on how it happens. And, I mean, we we use AI a lot in parser. I mean, today, a lot of our document processes is done using AI, but that was not the case when we started ten years ago.
[00:10:12] - [Speaker 1]
So when we started ten years ago, we designed what we called a template system where the user would send a sample document, and we would ask the user to highlight the pieces of data he wanted to extract from it. And then the next time we the user would send another similar layout document, we would know where to take the data from and extract it. So it was much more cumbersome than just asking the AI to extract the data because it required some setup, but it was very reliable. And when it wouldn't work, we we could tell the user, oh, it doesn't work because this took this area here was not found, or this was this error. Whereas in AI, when the we don't extract things correctly, it's a bit of a back box.
[00:10:57] - [Speaker 1]
You don't know. So even today, ten years later, we still use heavily this template system for us, for for the the people that the send us documents that have the always the same layout, and we use it in combination with AI. So when we we need reliability, when we we need determinism, we use our old system. And when the documents are a bit more complex, a bit more varied, then we use AI, and that works great for us.
[00:11:24] - [Speaker 0]
And I also think that many are guilty of getting distracted by shiny object syndrome and only looking at tackling those exciting or cool new projects. But, again, quite refreshingly, you've made the case that boring automation often often delivers far more than the latest AI trend. And and just to bring that thought process to life, is there any real world examples you could share where straightforward automation generated far better business outcomes than maybe a more sophisticated full on AI implementation?
[00:11:58] - [Speaker 1]
Yeah. Well, to continue what I was mentioning before, the today, you you you use AI for what it's it's good at, and then as soon as you can get out of AI, you should. So if you have very deterministic rules that you have to apply based on on some clear value, you should not use AI for that. Today, you can use some workflow system that can visually design the pattern of where you want it to go depending on which rules, and that's what we recommend most of our users to do. I was speaking last week with a customer that was a very interesting use case again.
[00:12:36] - [Speaker 1]
He he does data ingestions for horse races or horse shows in The US. If you want to go to a horse show, you have to make sure that your horse is vaccinated and have all the all its paperwork. So before automating it, people would have to come a few days before the show and and submit the paper on to to a desk and have it verified, but that would take a lot of time. So now what they can do with my customer's client solution is that they upload online their documents, and they use password to verify the documents. And there are a lot of things, would you know it, that you have to verify for a host to be validated and activated.
[00:13:20] - [Speaker 1]
So there were hundreds of data points that had to be extracted. At first, the my customer, he he asked AI or AI to to get all of their data points, and that would work somehow. But there were so many that sometimes it would miss some data points. But then it realized that by only cutting in half the number of data points towards the AI based on the document, he could infer the other half by just some simple rules. And that's what it started to do, and he he already got to much better results and much more accurate results by by doing so.
[00:13:55] - [Speaker 0]
Absolutely. Love it. It's such a great example there. And I also wanted to highlight that you guys process, I think, something around, like, a 100,000,000 documents every year. And when you're looking across a volume of data like that, I'm curious.
[00:14:09] - [Speaker 0]
What what patterns have you observed about how businesses struggle with document workflows? And and what is it that separates organizations that kinda get it right and those that don't?
[00:14:19] - [Speaker 1]
So first of all, the out of the 100,000,000 plus documents that we process, we don't know what most of those documents are because we made it our our purpose not to check it's for data privacy. We don't check what documents our users are submitting us because that that's their their job, and we only help when support is is required.
[00:14:42] - [Speaker 0]
Yeah.
[00:14:43] - [Speaker 1]
But then to answer your your questions, I think most of the people that join us at POSER already get it that they are on the the right path, and they are already the precursor of of data automation. But I think for me, the the most the biggest gap are the the companies and the the people that don't realize the time they waste to do some menial work, repetitive work that could be automated today. Of course, I I know documents the best, but in in there are many tasks in general that today you could automate with automation or AI. And so I think for me, a lot of the the job today is to try to educate or to share my experience of what what we see at Parcel to of the different use case that can be automated and how you could basically free a lot of somebody's time for them to make some much more valuable activities and higher value activities. And and this is where why I'm very happy to be on your on your podcast today because I hope I can I can convince some people that automation is is really valuable to to to save your time?
[00:15:57] - [Speaker 1]
And once they join Paster, we also see then some different levels of maturity from the companies. The simplest one, they just want their documents data. So they upload manually documents. They wait for it to be processed. They manually download the documents, and then they're happy, and they're on their way.
[00:16:14] - [Speaker 1]
The second level is when they say they have a recurring types of document they want to automate. So they can put they can put some rules to set up the ingestion automatically. For example, if they receive it by email, they can forward it to our to our service automatically, or if they they have it on the shared drive, they can set up an integration so that every time they add a new document, it comes to us. So then they they save a little more time now. They don't have to manually go to parser, upload the document, wait for it to to be extracted.
[00:16:43] - [Speaker 1]
They they did the ingestion part. And then the the last step, of course, is also automating the data push. So once or instead of downloading the documents manually, you could directly push the data where you where it belongs. So if you are using an accounting software, you could push the data there. If you are using a CRM, this is where it goes.
[00:17:05] - [Speaker 1]
If you are your own system, if you want it to to to be pushed to to your very niche thing, we can also do that because there are ways to this to integrate very easily and stuff. And this is where we you can reach 100% automation. And then finally, the very last stage what we see the other the most advanced company doing, the ones that have hundreds of thousands of volume every month, is that they realize that something sometimes some things go wrong, and they need to what we call the human in the loop. So they mean that they know that some things will not get processed correctly sometimes, so they have an exception process that say, oh, there are some rules. This is not fitting all my rules, so let me send an alert to somebody to review it.
[00:17:51] - [Speaker 1]
And this, I think, is the most efficient way to to use automation today.
[00:17:55] - [Speaker 0]
Your agents aren't producing accurate answers because they don't have a complete semantic understanding of your data, and Denodo is solving this and solving it through semantic consistency. Through semantic consistency, your agents can start making accurate predictions in real time. So see what else Denodo can do by visiting denodo.com to learn more. But now let me introduce you to today's guest. And another topic we must bring up when talking about this is data privacy and security concerns because this is something that's also growing as companies rush to adopt AI right now, and get making sure that's trustworthy is so important.
[00:18:40] - [Speaker 0]
So what questions should business and tech leaders that could be listening today what what should they be asking vendors before entrusting sensitive documents, information, data, etcetera, to AI powered platforms? Again, massive talking point right now.
[00:18:56] - [Speaker 1]
Yeah. Yeah. So the questions we get asked the most is, first and foremost, where is the your data hosted and processed? That's the the basic questions. So for us, we made the decisions since the beginning to to host the data in the EU because we knew that these companies in the EU are the ones that usually are the most sensitive to to having their data located close to the or in the EU.
[00:19:20] - [Speaker 1]
And I think that was a very good decision we we made at the time. The second one that is one we get one more recently, of course, is, oh, do you use any of my data to improve your AI systems?
[00:19:33] - [Speaker 0]
Yeah.
[00:19:33] - [Speaker 1]
And and that's a very legit questions as well. So for us, of course, we decided to not use any of the users' data because it's as I said before, it's not our data. It doesn't belong to us, so we have no rights to use use it. So this way, the the two most questions we guessed, but then there is a basically, if you are trying to access a vendor and you want to see if they will handle your data right, you have don't ask, are you GDPR compliant? Are you sub two compliant or whatever?
[00:20:07] - [Speaker 1]
You should try to assess whether they understand what it takes to be GDPR compliant or sub two compliance because it's very easy to edit on your home page or in new terms. So we are compliant. But if you don't implement all the controls that are actually required, then what what is it worse for? So I mean and for us, we've been to this exercise. We hired some some lawyers a few years back to train us on GDPR to and and to really understand what what it takes.
[00:20:37] - [Speaker 1]
And even if it's very most of the the the rules and the recommendations are are very down to earth, and and they make sense. It's there there are a few that you you have to make sure that they are put in place. So for example, that you the data is not yours. The in GDPR terms, it's called the data controller and the data processor. So the data controller is the one that submits the data that owns it.
[00:21:02] - [Speaker 1]
And if they want to delete it, they should be able to delete it without having any hurdle. If they want you to to do something about it, you can only do what they ask you and not anything else. And these are very common sense at the end of the day, but it's important you, the companies that implement them, understand the the what are their obligations and how to enforce them. And another one that we've seen is the SOC two. So it's about sec it's more about security and operation processes, making sure you you you won't get any bridge or at least you you make everything you can to have a secure system.
[00:21:42] - [Speaker 1]
And this is one we are currently pursuing, and, hopefully, by tomorrow, it's gonna be the end of our observation period. So in the next few weeks, I will be very happy to to have been certified.
[00:21:53] - [Speaker 0]
And anyone listening to our conversation that is interested in exploring everything we're talking about here, they will quickly find countless AI powered document processing tools out there on the market. So from a a practical perspective today, what is it that makes Parseur's approach different, and and why should organizations be thinking carefully about that balance between reliability, explainability, and AI driven automation? Bit of a balancing act. But what is it that makes you guys stand out from from the crowd here?
[00:22:23] - [Speaker 1]
So we have two big groups today in the in the landscape. You have the big names with enterprise ready solutions. These are the guys that you won't see their pricing on. You have to talk to a salesperson. If you you go after you go to the salesperson, and they they will send you a quote.
[00:22:44] - [Speaker 1]
And if you're happy with the quote, usually, you get some manual onboarding of a few days or weeks or so so that you have somebody helping you get started. But in in automation, I always found that a bit of a strange approach because we are selling an automation project. The first thing I would hope to see is that things are automated as a company. So why would you have to talk to someone? Why would you have to have an manual onboarding to to get started for?
[00:23:12] - [Speaker 1]
For me, doesn't make any sense. It's almost like the company didn't understand the industry they were in. So for for us, we we decided from the start to be self-service to have everything accessible from the user, to show that we understood automation, to show that we we were living by automation. And the the second group of companies are the smaller ones. So it's very easy today to to whip out a a product that uses AI to extract data from documents, and we got lots more competitors in the past four years since ChatDPT arrived than we had before because of that.
[00:23:50] - [Speaker 1]
But then it's not only about data extraction and AI. You have to have reliability. You have to be up all the time. And in all business, it's always very spiky. So sometimes you will have nothing, and the other half a second later, you have tens of thousands of documents that arrive at once.
[00:24:07] - [Speaker 1]
So you have your to make sure you have some crack infrastructure team to to scale your operations. And you also have we also go back to data privacy handling. You also want to make sure that the people at the head of the company know what they are doing, that they are safely treating your data, and that they are trying to to keep it secure as much as possible. So us in Parseur, we try to find them to be as feature full, as plentiful as the big names out there, but make it self serve that you can, that you can somebody can use it from hundreds of documents a month up to hundreds of thousands of documents a month without talking to anybody if they don't have to. But then we added the human in the loop as was I was mentioning before.
[00:24:56] - [Speaker 1]
So we have an AI agent for support if you have questions. But if the AI doesn't get you the answer, we have someone behind it that you can a real human that you can talk to. Same for new features. We only build the features that are being requested, so we have real humans that are the everything. Same sale for same for sales and support sometimes.
[00:25:18] - [Speaker 1]
So some high bigger customers, they have some specific requests. And if our self serve plan don't work, then you can we can discuss some enterprise and more advanced things. So we are trying to live by our industry of automating and and and being efficient.
[00:25:36] - [Speaker 0]
And if we do have any founders listening today, I also wanna shine a light on the fact that you've built a profitable global company with a distributed team while avoiding the venture capital growth model that seems to dominate much of the tech industry right now. So kudos to you there, and I've got to ask for those people listening, what lessons have you learned about building sustainable businesses in an era where so much attention is focused on growth at all costs and VC funding and AI hype?
[00:26:08] - [Speaker 1]
Well, for me, VC and venture capitalist is great for VCs, but less so for founders on average. So VCs, they they bet on the fact that out of a 100 of the companies they're gonna fund, one or two of them are gonna be super successful and cover for the losses of the other ones that are gonna die. So that's not the the the the game I want to play. I want to be in control of my company. I want to grow it, not following the VCs or or anybody's growth strategy, but I only want to listen to my customers.
[00:26:43] - [Speaker 1]
So my customers are the ones that tells us how we should operate, what we should build, how much we should price, and that's great because it means we align. If we build something that our my customers wants, they will be happy. They will more of them will come. If they don't, I will notice that my business is going down, and we'll have to to realign. So for me, it's it's how it should be.
[00:27:03] - [Speaker 1]
It's how normal business should operate, not based on growth of having targets of tripling your customer base by by in the next eighteen months. That doesn't make sense. That is not for the benefit of the the existing customers. So so I really like that. And on top of it, of course, it gives a a lot of freedom.
[00:27:21] - [Speaker 1]
So we decided we would be a remote company from the start, as I mentioned before, six people across six countries, which means we have to work a lot async, that we have to written communications is a very big part of how we operate. We try to limit the the calls to to the minimum so that we can we work and be focused most of the time. And and that has worked very good for us. And it's it's something I wouldn't like to change. But at the same time, of course, we recognize that being face to face is also great.
[00:27:53] - [Speaker 1]
So what we what we try to do is that every once a year, we meet altogether somewhere, and that happened to be last week. We were in Madrid, and we met the we met for a full week. Half the week was brainstorming about how we're gonna pre operate for the next year, and there was a half for some nice activities, team building, and and just some good times. And it it was very nice. It really is something that I would encourage any remote company to to try to do.
[00:28:22] - [Speaker 0]
Wow. Incredible. Absolutely love the journey that you've been on. You deserve the success that you're having now. And for anybody listening that would like to find out more information about anything we discussed today, where would you like me to point them if they wanna find out more information?
[00:28:36] - [Speaker 1]
Well, if you're interested in our product, I would point you to passeur.com. So it is parseur.com. So it's the French way of writing it, in case you hadn't noticed with my horrible accent. I am French. And if you want to follow more of our personal journey, you can find me on x at my handle is stye bridges and or on LinkedIn just following my my name.
[00:29:04] - [Speaker 0]
Well, as I said, there so much we covered in a short amount of time from data luke liquidity. Why that unsexy work of unstructured extraction is actually the prerequisite for any AI agent to function, and why you don't always need to reach for AI. And sometimes boring old school automation workflows are the best solution. But more than that, I just love your fantastic startup story there and the success that you have achieved. Thank you so much for sitting down with me and sharing that story.
[00:29:33] - [Speaker 0]
I'll include links to everything you mentioned. I urge people to check that out, but thanks again.
[00:29:39] - [Speaker 1]
Thanks very much, Jim, for having me. I had a good time.
[00:29:42] - [Speaker 0]
One of my favorite takeaways from our conversation today was that reminder that AI is a tool, not a destination. And sometimes the smartest solution is a sophisticated AI model. Other times, though, it's just a simple, reliable workflow that quietly gets the job done every day. And my guest made an important point about data liquidity. Businesses can't expect a AI agent to make intelligent decisions if the information it needs remains trapped inside documents, inboxes, and disconnected systems.
[00:30:19] - [Speaker 0]
So maybe before chasing the latest innovation, maybe organizations should focus on freeing the data that they already have. That's the foundation. But let tell your thoughts. Are you investing in AI before fixing your foundations? Or have you discovered that the so called boring work actually does deliver the biggest business wins?
[00:30:39] - [Speaker 0]
As always, techtalksnetwork.com. Send me a message. Love to hear from you. And more than anything, I invite you to join me again tomorrow for another conversation with a guest shaping the future of technology. Speak with you then.
[00:30:56] - [Speaker 0]
Bye for now.