24 Jun 2026 · 29m · Sumedh Chaudhary · 19 quoted facts
Enterprise AI often fails on document-heavy work not because the models are weak, but because context gets lost. It disappears at page breaks, across scattered data silos, and between disconnected tools. Sumedh Chaudhary of IBM argues that a coordinated team of specialised AI agents, backed by firm governance, is the foundation that lets AI work reliably in regulated settings.
The central tension is between a small pilot that works and a production rollout that does not. When error rates miss compliance standards, companies tend to retreat to slow but accurate manual processes. Chaudhary's answer is measurable error-rate targets tracked weekly, human oversight, and fit-for-purpose models, rather than rushing to an early win.
Chaudhary says systems built for unstructured data lose the link between page one and page two. The problem grows with documents of thousands of pages, so errors multiply at scale.
He describes an OCR agent for reading text, vector agents for storing meaning, a splitter agent for page context, and a matching agent for links between documents. He compares them to musicians in an orchestra: none can perform well alone.
Documents are unstructured by nature, so they cannot be tidied into tables and columns. The harder job is orchestrating how information is extracted and given context.
A small-scope proof of concept can look great and then struggle with real users and workflows. Chaudhary recommends target numbers for performance and error rates, tracked week by week, and a pause if improvement stalls.
He likens a sedan and a pickup truck needing different engines: one model does not suit every job. A hybrid cloud setup gives access to many models and lets a company pivot when results disappoint.
Chaudhary says AI systems lose the connection between content when moving from one page to the next, which he calls the page break problem. Documents also mix images, tables and text, which makes them harder than ordinary unstructured data.
It is a set of AI components that each do one job, coordinated together. Chaudhary names an OCR agent that reads the text, vector agents that store its meaning, a splitter agent that keeps context across pages, and a matching agent that links documents.
Chaudhary says data quality still matters, but it is incomplete as an explanation. Documents are inherently unstructured, so the bigger issue is how information is extracted and organised so that agents can understand it.
Chaudhary says that if error rates fall short of compliance standards, firms may go back to legacy manual processes. Those are accurate but slow and hard to scale.
Chaudhary gives the CFO a plan with a timeline and clear success criteria. The AI should match the legacy process's benchmarks within roughly three to six months, and the larger productivity gains come after it scales.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Sumedh Chaudhary says he has over twenty years in the industry and that the principle of garbage in, garbage out has held consistently.
“I've been in the industry for over twenty years and the one consistent thing I've heard over the years is any garbage you you give into an enterprise system, you'll get garbage out.”
Chaudhary says enterprises have recently had success using generative AI models to analyze unstructured data.
“Uh, using some of the generative AI models to be able to analyze and put solutions for unstructured data.”
Chaudhary says documents combine images, tabular data and page breaks, which add complexity beyond ordinary unstructured data.
“you've got images in it, you've got tabular data in it. And they are something we call as page breaks, right?”
Chaudhary describes an OCR agent whose sole focus is recognising data and extracting a machine-usable digital footprint.
“you have an optical character recognition or an O C R agent.”
Chaudhary says vector agents take embeddings from extracted data and store them in a vector database.
“Then you have vector agents that take the embeddings out of that extracted data and be able to store in a vector database”
Chaudhary describes a splitter agent that preserves context across consecutive pages of a document.
“So you can have something called, we call it as a splitter agent, so you understand the context between page one, page two, page three.”
Chaudhary says he has recently seen many users upload documents running to thousands of pages.
“I've seen a lot of folks recently using documents that are thousands of pages.”
Chaudhary describes a matching agent that links references across multiple uploaded documents.
“is what I call as a matching agent. And this is an agent that when you're uploading multiple documents into the system, is able to give references”
Chaudhary says proofs of concept succeed when scope is small but may fail in production.
“where you have a very successful proof of concept when the problem and the scope of the solution was small.”
Chaudhary recommends tracking performance and error-rate targets week by week.
“you should have target numbers for your performance and error rates that you're tracking week by week.”
Chaudhary suggests promising a CFO that an AI solution can match legacy benchmarks within three to six months.
“we can achieve the same benchmarks as a legacy solution in a period of three months or six months.”
Chaudhary says a successful agentic workflow is no longer limited by the number of human users who can handle it.
“You do not have to, you're not constrained by the number of, uh, users who are able to handle that workflow anymore.”
Chaudhary says many automation use cases for data access can be done before adopting agentic workflows, sometimes without an AI agent at all.
“Before we jump into the agentic workflows, there's a lot of automation use cases that can be employed in terms of how you access the data”
Chaudhary uses a sedan versus pickup truck analogy to argue that different enterprise tasks need different AI models.
“A small sedan car probably does not need the same engine that you need for a heavy-duty pickup truck, right?”
Chaudhary says enterprises can access thousands of models, though not all are available in every cloud environment.
“an enterprise can have access to thousands of models, and some of those models may or may not be available to you in your cloud environment.”
Chaudhary compares orchestrating AI agents to a concert master leading an orchestra of talented musicians.
“you've got this concert master who is trying to run this beautiful orchestra.”
Chaudhary advises first mapping all data sources and reaching them through a common virtualized layer.
“you spend a lot of initial time making sure you're mapping all your data sources and you have access to that in a in a common tool, work virtualized layer”
Chaudhary argues leaders should not rush into a use case merely to claim an early win.
“And I would not expect leaders to rush into a use case just because they wanna check a box and get an early win.”
Chaudhary says organisations whose AI error rates miss compliance standards may revert to legacy manual systems.
“you might go back to your old systems, the legacy systems, where you were doing the works a little bit more manually.”
Chaudhary explains that documents combine images, tables and page breaks that cause AI systems to lose context. This is the root problem the episode addresses.
“they lose the semantic layer between the context of the data going from page one to page two.”
Data scattered across silos is compared to unlabeled spice jars, making it hard even for a good AI system to assemble a full picture.
“if none of the spice jars are labeled, if if you don't know how to recognize between the different spices”
In regulated industries, error rates that miss standards can push teams back to slow legacy processes. This is why accuracy decides whether AI survives.
“There is a risk that these tools might be abandoned, uh, prematurely”
Chaudhary accepts the garbage-in, garbage-out rule but says documents add an extra challenge of extraction and orchestration.
“it's not just the data quality, but it's the orchestration of how you extract that information”
He lays out OCR, vector, splitter and matching agents and compares their coordination to an orchestra.
“you have vector agents that take the embeddings out of that extracted data”
Governance, with weekly error-rate targets, separates a pilot that merely works from a deployment that scales.
“you should have target numbers for your performance and error rates that you're tracking week by week.”
He recommends a clear plan with a timeline and crisp success criteria benchmarked against the legacy process.
“give it a timeline to say that we can achieve the same benchmarks as a legacy solution”
Avoiding vendor lock-in and following a staged plan of data mapping, architecture, and governance with humans in the loop.
“deploying fit-for-purpose AI models in your enterprise will give you the ability to pivot”
Welcome everyone to the Emerge AI in Business podcast. Today's guest is Sumedh Chaudhary, CTO of U S Industry Market at I B M. In this episode, Sumeet explains why so many enterprise AI deployments struggle with document heavy workflows. Not because the technology falls short, but because critical context gets lost between pages, across data silos, and between disconnected tools. He outlines how a multi-agent architecture gives enterprises the reliable foundation they need to make document AI work. Today's episode is sponsored by Arango.
A quick note for our audience that the views and opinions expressed by Sumedh Chaudhary on today's program are his own and do not reflect those of I B M or its leadership. In this episode, we cover how enterprises can build multi-agent AI architecture to handle document heavy workflows and the governance frameworks that determine whether those deployments scale. To go deeper on this topic and to learn how to structure landing pages for higher conversion and how to use self-qualification systems to prioritize high intent leads, download our free PDF report B to B AI lead generation guide at emerge dot com slash A I G one.
That's E M E R J dot com slash A I G one to download your copy. Now, the conversation with Sumedh Chaudhary Welcome to the show, Sumedh Chaudhary.
Good morning, thank you so much for having me on your show.
Absolutely, it's a pleasure to have you here. I'm gonna dive right into it. Uh, there's no shortage of AI ambition in large enterprises at the moment, but the R O I conversation is definitely getting harder. Before we start getting into solutions for that, I'd love to start with the problem itself. Uh, we see in well, in large enterprises with document heavy or highly regulated workflows, where do you usually see the biggest breakdown when organizations try to apply AI?
And how does it work at scale?
Yeah, absolutely. I think this is a very common problem and I think it's getting exacerbated because of some of the changes in the technology landscape. As we all know, you know, in the world of structured data, ability to do analytics, the ability to apply AI was the first victory for the technology industry. As we evolved and moved into the unstructured data, we had a lot of success in the recent times. Uh, using some of the generative AI models to be able to analyze and put solutions for unstructured data.
But a document heavy workflow presents the third level of challenge. This is a place where not only are you dealing with unstructured data, you're looking into a document file, which is quite complex, right? You're not looking at just unstructured data, you've got images in it, you've got tabular data in it. And they are something we call as page breaks, right? When you flip from one page to the next page, the computer and the systems that are designed to understand unstructured data somehow break down because they lose the semantic layer between the context of the data going from page one to page two.
So, that's one of the complex challenges of working with the document heavy workflow.
Am I hearing you correctly that uh, a lot of these failures come from the fact that all these different documents, formats, uh, and different types of data all live in separate systems? And if they were actually stored in one connected place, AI would have the context it needs to operate in a more reliable environment?
Absolutely right. I if I can give an analogy, right? If you've got a chef who is preparing a meal. If that person is trying to look at the ingredients that they have at hand. Let's say they have spices and let's say they have tools. If none of the spice jars are labeled, if if you don't know how to recognize between the different spices, it could be very complex for even an experienced chef to prepare that absolutely beautiful meal.
But that's kind of one of the challenges is if if you've got these pockets and silos of data living in your organization and enterprise that is spread over different silos in your organization, it becomes quite complex for even a very, uh, well designed AI system to be able to put it all together.
That was a very practical analogy and very easy to understand and identify. And getting into the identification, uh, we might have leaders in enterprises that aren't as involved in the AI functions. How would they be able to identify this from the outside? What does the leader actually see or feel when this is happening in their organization that there's no context?
Yeah, I mean, I honestly work with these leaders every day. One of the things I've noticed over the past six to twelve months is there's a little bit of a race to get to the finish line. I think everybody's trying to establish early success. Folks are looking at the low-hanging fruit. There are a lot of vendors in this space who are trying to target these enterprise leaders to, you know, highlight their products and solutions. But, you know, in the world of AI, there is a very systematic approach on how you unpack this challenge.
One of the things you have to look at is currently some of the workflows that document heavy workflows are very deterministic. What I mean by that is it follows a very sequential steps of going from point A to B to C. And that's where, you know, an A I, uh, document heavy workflow can be very challenging because a deterministic workflow can work really well with some of the structured data or even unstructured data that doesn't get stored in a document.
But as soon as you have a document heavy workflow for challenges that I explained earlier, it it really breaks down and you can have a lot of errors in that process.
And I'm wondering, in in the highly regulated environments, obviously, those industries and those organizations are pressured for compliance. You mentioned context, so how would context influence the compliance pressure in those regulated environments?
Yeah, I mean, I think one of the ways a regulated industry works is they would have error rates that are measured on these, uh, document heavy workflows, right? So you are re using optical character recognition, for example, to understand the documents. You are, uh, trying to unpack these documents and trying to create structured data so that some of these A I agents can learn from it. And then analyze it to create that intelligence that your enterprise is looking for.
Now, there is an expected outcome from this analysis, and then what is the actual outcome from these A I agent analysis? And and as soon as you know in a regulated industry, you see the error rates kind of not meeting the standards of compliance. There is a risk that these tools might be abandoned, uh, prematurely, because you might go back to your old systems, the legacy systems, where you were doing the works a little bit more manually.
But when you're doing it manually, possibly humans in the loop, you know, you still get a lot of success in terms of getting it right the first time, although your productivity will be very low. But you will get a lot of success in getting the accuracy part of the document workflow right. And and that's where the challenges are for the leaders is do you do you try to scale your enterprise using A AI agents? Or because you're not able to contain the error rates, you default back to the legacy workflow process, which was highly accurate, but, uh, relatively slow and unable to scale that process.
We've been having similar conversations with leaders that I would say are somewhat in denial. So some of them would say that this is actually a data quality problem and not an AI problem. Why is that wrong? Or or at least incomplete.
Yeah, I mean, I think, you know, I've been in the industry for over twenty years and the one consistent thing I've heard over the years is any garbage you you give into an enterprise system, you'll get garbage out. And I think that has been true and consistent, and I I never wanna contradict that statement, but I think you, you're dealing with a different set of challenges in the world of A I. One of the things that we have to look differently is, um, not only folks have to look at your data problem, which is, you need quality data, but but by design, a document is unstructured data.
So you cannot have data quality in the sense how you have in structured data. You cannot have tables and columns and things nicely organized. A document, by definition, is complex, unstructured, and very hard to understand. You're using a variety of AI tools and enterprise learning tools from optical character recognition from vectorizing your data, from extracting all of that information to be able to allow these agents to understand it. And then create that semantic layer that provides context around it.
This data, by definition, is unstructured and very complex to be able to create structure around it. So I think the the fact about the data being the data quality being the underlying problem, that remains consistent as it has been over the past twenty plus years. But in the context of a document heavy workflow, it's not just the data quality, but it's the orchestration of how you extract that information and make it useful for for your workflow.
It makes sense. So, um, we've we've definitely defined the problem. I want us to move into what could possible solutions be, and what actually has to change underneath the current workflows before A I can operate in a reliable way. Uh, what does it look like when it's actually working?
Yeah, so I think in a well designed system, right? And this is where we talk about the architecture of how you should consider designing a document heavy workflow to to be able to have, uh, positive results on the other side. I think there needs to be, uh, an architecture which has a multi-agent strategy, you know, I I talked about different tools and technologies available to us. So, in an ideal scenario, you have an optical character recognition or an O C R agent.
The sole focus of that agent is to be able to recognize the data and, uh, extract that digital footprint out of it that is useful for machine. Then you have vector agents that take the embeddings out of that extracted data and be able to store in a vector database that can be easily understood by the machine learning algorithms. I talked about the page breaks earlier. So when you go from page one to page two, an AI tool can lose a lot of context.
It doesn't seem like a big problem to humans. This is where humans are brilliant in terms of how we read and understand things. But for a computer, they pretty much lose all context when you jump from page one to page two because of how the document is organized. So you can have something called, we call it as a splitter agent, so you understand the context between page one, page two, page three. And sometimes when you are uploading documents into this A I system, I've seen a lot of folks recently using documents that are thousands of pages.
And that problem just compounds at scale when you're uploading such massive documents into a system. Uh, so that splitter agent can give you that context between those pages. And the last one, if I could mention, is what I call as a matching agent. And this is an agent that when you're uploading multiple documents into the system, is able to give references between not only the pages within the document, but how these other documents are linked to each other and what is the context between them.
A good orchestration of these agents, a multi-agent solution, which, you know, if we look in the world of music and orchestra, right, where you've got this concert master who is trying to run this beautiful orchestra. Even if you've got a really good concert master, he or she cannot have that beautiful performance without the help of their, uh, musicians who are extremely talented. So those musicians is what I would call these multi-agents in in the world of document heavy workflow that come together and put together that beautiful piece that looks like like an art to everybody.
That makes sense. If you have, uh, one instrument missing, if there's a violin missing or something, a Taylor, you will miss it. Uh, and you probably won't notice that it's missing until you've had it and it's gone now. I'm also thinking that when we think that data and governance and workflow all have to work together, which of those three is most often the real blocker in your experience? Is it data, is it workflow, is it governance?
I would say the governance is one of the key cornerstones of and good deployment of an A I solution. If I can take a step back, right? How do folks get into an A I solution? The first step is usually to maybe do a small proof of concept. You know, a solution is probably put together and you show some early success. You keep the scope of the problem really small and the solution, the A I solution probably delivers really well when you keep the scope of the problem small.
When you scale that use case in your enterprise and you're looking at a more complex scenario, more heavy document workflows, you know, across multiple systems. I think that's when the governance, which could include measurement of the quality, the error rates, the security of the solution, those aspects of the governance are deployed and measured consistently. That's when you will be able to understand the real productivity gain and and you know, what are the compromises you're making in the background?
You still have to continue to tweak your models to be able to adjust to your enterprise data. But I feel like the governance piece of it gives you that visibility that allows you to evolve your A I solution over time and get to a place where the outcomes and the error rates are acceptable and reasonable for your enterprise.
Especially in our our regulated industries, I can imagine. Uh, thinking about piloting basically this with a a smaller problem or a smaller issue. Is there a moment in the bolt where you know this is going to work before you have the results? Are there any early signals that the foundation is right?
Yeah, I mean, I think I, I, I will say, uh, when you are kicking off a project, right? Everybody who's working on a project is very optimistic. You're probably trying a new technology, you're you're expecting positive results. There are folks who who are quite motivated to deliver success in a solution. And I think everybody wants to get behind it and wants to be optimistic about delivering a good solution. As I mentioned earlier, it is quite possible, and I've seen this, um, numerous times, where you have a very successful proof of concept when the problem and the scope of the solution was small.
But as soon as you start implementing in a production environment where you're touching the real users, the real workflows, you might run into challenges. And I think that's where a leader should be very observant of this process. Um, you know, just because there was a success in the proof of concept phase does not mean that that success is gonna reveal itself at at scale in a production environment. So that's where I feel like, you know, the governance process should be very tight and you should have target numbers for your performance and error rates that you're tracking week by week.
And if, for some reason, you're not seeing incremental improvement week over week, then then you know there's a problem that you have. And and that could be as early as the first few weeks, or even if you've kind of implemented that solution for a couple of months. But if you're not seeing incremental performance, I think that's a time to pause and reevaluate.
It sounds like this can also take a long time. It sounds like it could take years to to get to that perfect foundation spot. How do you convince a CFO that the foundation, the foundational investment, uh, is worth it when they want results immediately? What is that conversation?
Yeah, I mean, I think I think some of it probably boils down to the personality of the individual we're talking to. But I think depending on the enterprise and their use cases, there are various ways to, you know, pro provide that visibility to these leaders. What I always do is I give them, uh, a plan in terms of what the solution is gonna look like, what is the timeline for performance, and what are the success criteria for an A I solution to be implemented, right?
Um, the success criteria has to be very crisp. It has to be grounded based on feedback from the individuals working in that organization and folks who've done the maybe the legacy process in the organization, right? Who know the process well, who know what it takes to deliver a successful outcome, and then use the same benchmarks for an A I solution, right? And and give it a timeline to say that we can achieve the same benchmarks as a legacy solution in a period of three months or six months.
But then once that once that solution is implemented at scale, the productivity gains for the enterprise is where the real value is. You do not have to, you're not constrained by the number of, uh, users who are able to handle that workflow anymore. Once you've got a successful A I ag agentic workflow deployed, then you can scale it at will. And and that's where the real gain for the enterprise is.
So transparency from the beginning, giving them a realistic timeline, that's that's basically where the conversation should start. Am I getting that right? Before we get to our last question, uh, I want to make sure that the senior leaders listening to this can take something concrete back to their organizations. Uh, if you were advising a CIO or a COO who's generally they're ready to act, what should they focus on first? Um, uh, how do they know when the foundation is actually ready?
Yeah, I mean, I think for a senior leader, uh, we talked about this earlier, having clean data is is an important outcome. But knowing where that data lives within your enterprise is extremely relevant in the context of document workflows. There has to be clear ownership of workflow tasks and responsibilities. Uh, you should have an AI architecture framework for your enterprise built together. Um, there are a lot of vendors in this space who are pushing their own point of view, and sometimes a lot of these conversations are lost in translation.
But having a clear AI architecture framework for your enterprise and how that can be adapted to your workflow is very important. And then make sure we are not only looking at the AI engines, but also the broader automation, uh, use cases within your workflows, right? Before we jump into the agentic workflows, there's a lot of automation use cases that can be employed in terms of how you access the data, how you're connected to those data sources.
Uh, you know, you do not even need an AI agent to be able to do that in some cases. So having that framework that blends between data, AI, and automation is gonna help, uh, create a very successful outcome for the enterprises.
That's valuable. And then once we have the foundation, right? What is the most common mistake you see organizations make after they have everything in place?
Yeah, I mean, I think recently there are some challenges that I've noticed in terms of how an enterprise might create their own inherent bias for certain type of models or certain type of vendors. So, uh, a good example would be, you know, how you are deploying your solutions on the cloud, right? Uh, there might be some challenges in terms of, you know, vendor lock-ins. You know, you as an enterprise, you might have decided that you are gonna deploy your solution on a specific cloud provider, but maybe that cloud provider does not have access to all the tools, all the models that you need for your enterprise.
Uh, I always, you know, look at, you know, a very simple analogy in terms of how I would work or how we look at it in the real world, right? Like if you are driving a car, right? A small sedan car probably does not need the same engine that you need for a heavy-duty pickup truck, right? And and that that's kind of the same analogy is that is used in the world of foundation models and GPT models for for an enterprise.
One size does not fit everybody, right? So there, um, in today's age, an enterprise can have access to thousands of models, and some of those models may or may not be available to you in your cloud environment. So having that flexibility to be able to live in this hybrid cloud environment where you have access to the best of the breed solutions, best of the breed models, and that flexibility where you are not locked into a specific vendor, but deploying fit-for-purpose AI models in your enterprise will give you the ability to pivot when you're getting not getting the right level of results for your solutions.
Sumeet, I want to leave our listeners with an action plan today. So put yourself in their position. If you were on the verge of having to make this decision of getting better context for your your data, where where do we start? What is the starting point and what do we need to do to do in the next three or six months to get to that point where we actually have context in our in our data?
Yeah, I mean, I think the the world of AI agents is is going to a place where we are living in an autonomous world of orchestrating these agents. You know, you start with a clear foundation of having access to the right data sources. Now, as I said earlier, like we some of these documents and data points are probably living in different silos. So you spend a lot of initial time making sure you're mapping all your data sources and you have access to that in a in a common tool, work virtualized layer that that can access all the data silos in your organization.
Then you have a very defined AI architecture framework that brings in the right level of tools with the flexibility that you deploy fit-for-purpose models and tools in your tool bag for for your solutions. And then have a very defined governance framework, which includes humans in the loop, so that we can have a very specific measurement criteria for success in your workflow. And then be able to repeat that process on a on an ongoing basis that allows you to drive that scale.
And if, for some reason, there is a challenge in the process where you're not getting the outcome, have the discretion to possibly pivot and change your workflow and your approach to go in a direction that can you can possibly achieve better success than where you started from.
Sumeet, this has been an interesting discussion. If I can think about what I've gained from this, is the first thing is that the problem is no longer just structured versus unstructured data. It is having stru both structured and unstructured unstructured data, um, but lacking context. Uh, and one thing that you mentioned that will help with that is, well, basically, success requires our architecture to have multi-agent strategy. And you, you kind of went in and unpacked the different agent capabilities, uh, very effectively.
And then knowing that it's successful will give you clean, findable data. There's clear ownership and responsibility, and we need set A I frameworks. And that bolts into the last part that you just mentioned, saying that we have to have very defined governance frameworks to make sure that this is also implemented successfully. Is there anything else that you would like to leave our audience with today?
I think the last thing I would say is there is a lot of noise i in the industry in terms of successful ways to do it. You have to personalize this journey for your own enterprise. Um, you have to invest the time and energy to get the right level of results. And I would not expect leaders to rush into a use case just because they wanna check a box and get an early win. I think, uh, just like any enterprise transformation journey, it takes time, focus and energy.
And and you know, you don't have to be the first to get success in the industry, but when you do get it right, I think those results are very valuable for your enterprise. So that you don't have to restart your journey. I I think that to me is more important to get to do it right, to do it in a methodical way versus trying to rush to get a early win in in your AI journey.
Absolutely, I think some important concepts were shared just there. Uh, Sumeet, thank you very much for joining me and I hope to have you in our studio again sometime in the future.
Thank you so much for your time, appreciate it.
Wrapping up today's episode, I think there are three key takeaways from our conversation with Sumeet. First, document-heavy AI workflows require a coordinated multi-agent architecture, spanning extraction, vectorization, page splitting, and cross-document matching. Because no single tool can preserve the semantic context AI needs across complex, multi-page enterprise documents. Second, proof of concept success is not a reliable indicator of production readiness. Leaders must establish measurable error rate targets and track incremental improvement week over week before scaling any AI deployment into regulated workflows.
And finally, avoiding vendor lock-in and deploying fit-for-purpose models in a hybrid cloud environment gives enterprises the flexibility to pivot when performance targets are not being met. In this episode, we cover how enterprises can build multi-agent AI architecture to handle document-heavy workflows and the governance frameworks that determine whether those deployments scale. To go deeper on this topic and learn how to structure landing pages for higher conversion and how to use self-qualification systems to prioritize high intent leads, download our free PDF report B to B AI lead generation guide at emerge dot com slash A I G one.
That's E M E R J dot com slash A I G one to download your copy. On behalf of the team at Emerge, we'll see you on the next episode.