Build the AI GTM Agent That Knows the Buyer - Dr. Sajjan Kanukolanu, Position2 (Position Squared)

AI Engineer · 26 min · 245 sentences · from YouTube's caption track

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00:00Hello.
00:02We know that the modern B2B selling has evolved significantly.
00:06The uncomfortable truth is that by the time the buyer reaches you, the decision is mostly made.
00:13They've researched, compared, and probably shortlisted vendors or partners that they want to work with.
00:20That may include you or your competitors.
00:24This talk is about the architecture that lets GTM teams know the buyer more than they today.
00:32My name is Sajjan Khan Akolanu.
00:34I have 20-plus years experience across product, technology, and marketing.
00:39At Position Squared, as the VP of global operations and strategy, I lead the services teams and our AI-native transformation from vision to positioning to deployment, where I work closely
00:52with our technology team.
00:54We've launched 75-plus AI agents specifically for our clients, 18-plus vertical knowledge bases that power these agents, and we've had 800-plus runs per month, and this is data as of month-to-date
01:09June 2026.
01:11By the time we get to the end of the year, I expect these numbers to increase significantly.
01:17The interesting thing about GTM and AI is that many GTM teams try to bolt AI onto their existing stack or existing processes and workflows.
01:30And therein lies the problem.
01:33That approach itself is hindrance to scale.
01:37The interesting thing though is there are three different problems that GTM leaders and GTM teams have to solve in order to be successful in this AI era.
01:50And these three are number one, AI.
01:54By just bolting AI onto your existing systems, AI doesn't really and cannot really find out who your buyer is.
02:03It doesn't understand their role.
02:04It doesn't understand their history.
02:06It doesn't understand their intent stand-alone.
02:10That's number one.
02:11Number two is the integration itself.
02:14The old GTM stacks may not necessarily be best positioned to capture all the intent signals and the data, the CRM context context that the AI captures on its own
02:25and bring that all together.
02:27So, the systems today as they stand may be broken.
02:30By putting AI on top of it is not going to help solve the problem.
02:35The third is the architecture.
02:38The underlying architecture has to evolve with AI at the core.
02:44And I'll talk about what I mean by that in a little bit, but the fundamental concept here is that with AI bolted on to your existing GTM systems, processes,
02:55and workflows, you will not be able to scale.
03:00And most GTM deployments do not solve for all these three problems simultaneously.
03:07You could accomplish one and two, but if three is not accomplished or you don't solve for number three, then you still have a problem.
03:14So, it is important that all these three problems are solved together.
03:18And I will show you an architecture diagram in just a bit that's going to walk you through how we do it at Position Squared.
03:24Another problem that we face is that with the buyer.
03:28The buyers are constantly evolving.
03:31And what I mean by that is by the time the buyer reaches out to you in today's world, they're not early stage.
03:40They're actually late stage.
03:42Here are some stats to prove that point.
03:4494% of buyers use GenAI as a primary research.
03:49And these platforms are almost like a black box where we don't know what they consumed, what they read about, who they've investigated, if your organization is on top of that list or not.
04:02And that is information that is from the Forrester 2026 research.
04:07The second stat is about 67% of B2B buyers prefer a rep-free experience.
04:13They don't want to talk to people as they make the decision.
04:1880% of deals very interesting statistic.
04:2280% of deals go to the buyers who are part of the pre-contact list.
04:27So, essentially, buyers have already made up their mind.
04:31And the fourth and very vital stat here is only 17% only 17% of the total buying time is spent talking with potential vendors.
04:41What all these numbers tell us is the buyers do their research thoroughly.
04:46They use various tools including the GenAI platforms.
04:50And by the time they come talk with you or come to your website, they have made up their mind.
04:55Now, imagine a situation where the buyer does that research, comes to your website, and you have a chat window for them to engage with so they can converse with your very intelligent AI system.
05:06That is now responsible for telling them about what you have to offer and how they can buy your services or product.
05:15But many GTM systems that are bolted with AI would essentially start off with a conversation that would be something like this.
05:24How can I help you?
05:27What is it that you would like to What is it that you would like to know today?
05:32But these are questions that actually move you backwards.
05:36They don't move you forward as an organization.
05:39The buyer has done their research and then some basic questions are asked to them.
05:44They're actually walking out of the system.
05:46They're not continuing their conversation.
05:48So, it is very important that as a seller you know what the buyer's needs are, you know who the buyer is, you know who the buyer is, who what their buying needs are,
05:58and you're able to cater to those as they engage on your site.
06:04So, what does this really mean when it comes to implementing an architecture?
06:08How does one accomplish an architecture or a system where the buyer information is known as soon as they come to your site?
06:19There are essentially three architecture layers behind an intelligent system and running an intelligent GTM.
06:27The first is signals.
06:30This includes all the communication and information coming from your CRM systems, which includes your information about the deals, your contacts, who the owner or the sales rep is for that,
06:41who are you conversing with.
06:43It also includes an enrichment system where you have the right information pulled out of these anonymous visitors that are hitting your site.
06:53This system is going to work as long as that information is accurate.
06:57If you're off on the enrichment, your data is off, and so is your communication.
07:04And one of the things that we find really helpful and really useful, very important, is signals coming out of social media platforms.
07:12Especially LinkedIn, what we find is the engagement, the job changes are huge signals for you to capture as an organization, which we do as ourselves, and I'll show you what what I mean by that.
07:25And these these signals really help identify how and who you should target and when.
07:32So, for example, if you have an exact sponsor at your current account, and that individual is leaving the organization and moving into another account, and that company is not part of your list.
07:45Given that you have this exact sponsor currently as your contact, you ought to make sure that the company that they're moving to next is part of your account list,
07:55and they become your new exec sponsor, your champion in that organization.
08:00That's the power of social signals, and it's very important that these are captured, and not many companies leverage this in a automated, systematic way.
08:09And I'll talk about how to how to do that in just a second.
08:12The second layer is that of buyer intelligence.
08:15This is about making sure you have the right information about the buyer.
08:19It all starts with for us, at least at Position Squared, and we believe uh this is how it should be for a lot of organizations, is through a knowledge base.
08:31A knowledge base is a repository of context that you have about your product, your buyers, the ICPs, the personas, the different titles, different playbooks, different criteria of success.
08:46Everything that makes you successful sits in a knowledge base.
08:50It also has visitor identity information, de-anonymized information sitting in your buyer intelligence database.
08:58The ICP scoring, what defines a score, uh uh what score defines a fit, and what score defines a buying stage versus someone who's a research mode.
09:09All these are things you hope to quantify within your buyer intelligence system.
09:13Then you have a context builder.
09:16All the signals that you have need to form a context graph that I'll talk about next.
09:22And then, you need to have a routing logic.
09:26This is where you would define what message goes to who, when.
09:32So, if someone were to engage with you on LinkedIn, do they get a LinkedIn outreach, or do they get an email?
09:38Or should they be shown an ad?
09:41Um these are the insights or inputs that actually go as part of your routing logic.
09:47The third layer is that of action.
09:50Once you have the signals and information and the intelligence from the first two layers, now it's time for action, which basically means now you have when user lands on your
10:00uh page or your website, the chat window that popped up earlier that I talked about shouldn't ask generic questions like what can I help you with?
10:09What are you looking for?
10:09What's your name?
10:11That's not the communication at this point.
10:12The communication's personalized and I'll show you an example what I mean by that.
10:16It needs to identify the user, address them by name, and talk about where they potentially left off last time they were on your site.
10:25Or based on the intent signals, the chat needs to offer them specific content.
10:31The action also based on these signals should be about sending the right alerts and information to the rep.
10:38Too many, you lose the the rep, the sales rep, and too few, you're not helping them hit their hit their numbers.
10:47Um and and get revenue for the organization.
10:52The third piece here is that of updating the CRM.
10:55The CRM system cannot just be an update about the contacts that the sales had.
11:00It needs to be about the context on about the account that the first two layers drive for you.
11:08And finally, the sequence trigger.
11:12The actions need to determine what type of outreach sequence need to be sent out at what time.
11:19What outreach is triggered when, to who?
11:22Without all these three layers running in tandem, the GTM model is still obsolete, is old school, and it's not set for the AI era that we are talking about.
11:39And here's an example of context graph.
11:42All the signals we just talked about roll into this connected record, which is per buyer.
11:49The persona, account, and deal, these are all signals and actions that are logged for every touch point across the account for every single person.
12:03A person's signals link to their account.
12:08The accounts have multiple people in them.
12:11So, the buying committee information surfaces for the account based on the signals coming by person and for each account.
12:21Then, you have deal level information here, which is very specific to sales and marketing activities.
12:29So, has sales and has marketing already reached out to this individual or people the buying committee at this account?
12:38And all these together with all those signals and data points and touch points become a context graph.
12:46The reason why a context graph is important is because without it, you don't know which account is a priority and which one is not.
12:54And therefore, when you have this information, you're able to prioritize the high intent accounts and contacts within those accounts for your sales team and marketing to pursue.
13:08Now, having covered the architecture and the importance of a context graph, I want to walk you through how we implement this architecture and what that flow looks like, followed by a quick demo of what our system
13:24shows us when it comes to all the details that I just talked about and how our system helps us identify key accounts and key stakeholders that we need to reach out to.
13:34This is how the three-layered architecture I just talked about looks like as connected agents.
13:41When a user lands on our site, at that moment we don't know who they are.
13:45They're anonymous.
13:46And that is when that visit kicks off the first job, which is the identification job.
13:54And as you'll notice, I'm using more than one source here running together, and that's because no single tool catches every visitor.
14:02We pull the information provided by these tools, consolidate them, and dedupe them, and send it over to the next layer, which is the enrichment phase.
14:14These tools change all the time for us, but not necessarily the architecture as such.
14:20Once these contacts are enriched, the next layer, which is the intelligence layer, kicks in.
14:32Here you'll notice that our ICP filter agent kicks in, which is referencing information from the knowledge base.
14:41What this does at this stage is it's looking back at the knowledge base and making sure that the contacts that come in are part of the criteria we've defined in our knowledge base.
14:53Anything that doesn't fit, such as wrong industry or wrong geo, wrong size, wrong persona, are weeded out.
15:03Those contacts and information are deleted.
15:08What fits our criteria and what's relevant moves into a clean table of qualified accounts.
15:17After this, the match agent kicks in.
15:21It checks for people against our CRM.
15:24Have we talked to them?
15:26Are they warm?
15:27Are they hot?
15:28That's the evaluation that happens at this stage.
15:31Finally, the action agent kicks in.
15:35This agent decides what happens next per person.
15:42And this could include one person from an account or if there's a buying committee, the actions are decided for the entire buying committee per account.
15:56Here, the action agent decides different alerts that the sales reps have to get on Slack with the right context and a draft of an email that they can possibly send.
16:10This agent also decides if an email has to be fired up and sent over to the contacts we have.
16:15It also decides if we need to use LinkedIn for an outreach.
16:21And this is how the workflow works from the three-layered architecture to how we implement GTM agents today at Position Squared.
16:34Now, let me walk you through how the system actually looks in the back end.
16:40So, we saw the three-layered architecture.
16:42We saw the agents working together.
16:46Now, what does the output look like is what I'll show next.
16:50Here's a view of our system at Position Squared, which is called Intelligence.
16:57What you see here is as soon as I land, the system is able to understand who I am and I see a personalized message coming up here as part of our chat interface.
17:09Instead of just asking me generic questions, it's actually pointing me to one of the conversations that I had with it earlier and prompting me or asking me if I want to continue there.
17:17And if I were to say yes, it pulls up a link or gives me a link here, which I can click on and go to the specific conversation that I was
17:24uh part of earlier.
17:26What you see here is a culmination of all the signals, information, and all the data that we collected as part of the user that is reflected here in the chat.
17:38So, as opposed to having a generic greeting for me, this is more personalized.
17:44Let me now take you to our internal dashboard and show you how this data actually is projected to us when we look at the accounts that we are pursuing.
18:00Here's a view of the interface that gets us into our dashboard.
18:03What you have here is two key sections that we have built.
18:07One is for GTM, the other is organic.
18:09For our conversation today, I'll be walking you through what happens when we go to our GTM dashboard.
18:16Once you enter the dashboard, what you see are multiple cards.
18:20And for the conversation today, I will be focusing on the anonymous visitors and the LinkedIn intelligence sections.
18:26What do you see here is when I click on the anonymous visitors, it takes us to a dashboard where the flow of agents and the architecture that I showed you earlier
18:36is de-anonymizing the users.
18:38It's identifying the users that come onto our website, and it's categorizing them into multiple sections.
18:42What you see here is we have our system has identified about 3,000 odd people or visitors coming in from about 280 different accounts.
18:53And the system tells us that information technology is one of the key verticals or key industries that's visiting our site.
19:01And this is in fact one of the verticals that we focus on.
19:03What you also see is the industry breakdown, as well as hot signals.
19:07And this is information of people from different companies that are being categorized as hot contacts or hot accounts at this point.
19:17This is a combination of different signals including the conversations that have happened within HubSpot.
19:22For the sake of the conversation here, I have changed the names although the titles are accurate.
19:29If I were to scroll down, I have more information about these users, about the contacts, the companies they belong to, the location, as well as industry type.
19:43All this information is uh or or can be sorted.
19:49All this information can be sorted based on industry, seniority, different types of engagements they've had with us.
19:59And we can also look at the companies that visit our site.
20:05The second part of this is if I were to go to the LinkedIn intelligence, and this is the part that many GTM teams miss.
20:13LinkedIn has a wealth of information that is very useful.
20:16What you see here is over the last few days about 100 people visited our site through LinkedIn from 73 different companies and they've engaged with eight different posts that we put out recently.
20:30It has a list of all the folks that have engaged and the type of engagement they've had with us.
20:36We can actually sort them out by C-suite, VPs, directors, managers, and individual contributors.
20:41Those are the categories that we use.
20:43And you have a list of companies where they belong to.
20:46By clicking on each of these, we can actually go into very specific details about their overall engagement with us.
20:55This level of detail really helps us bring in the right level of intelligence into our system.
21:05Now that we've looked at the architecture, let me walk you through what does tend to break when it comes to building these types of systems.
21:13And I want to focus on four key areas.
21:14The first is the ICP drift, and this is real.
21:18Your ICPs evolve over time.
21:19For example, today you focus on a $20 AR organization from a certain vertical, and if you were to close a $50 or $100 AR firm from a different vertical, then you might realize that that is a new area for you to focus on and for you to pursue.
21:34And that information is critical to enter into your knowledge base.
21:38Without that, your agents are working off of the old information, always giving you wrong wrong data that you might not want.
21:45What this means is you need to retrain your models.
21:50You retrain your agents every quarter with your closed one or closed lost opportunities.
21:58And this is critical.
21:59Without this, your agents are looking at wrong information, pointing you to the wrong accounts and the wrong people.
22:07The second point is alert fatigue.
22:13If everything is flagged as hot to a sales rep, they would stop acting because it's it just gets overwhelming for them.
22:21And at that moment, they stop trusting the system and the system's dead.
22:26So, it's important to use the knowledge base and the context graph to cut through the noise and focus on the alerts.
22:33Focus on alerts from the accounts and contacts that do actually matter for the sales people to act upon.
22:43The third item is identity ceiling.
22:47And this is a limit that exists with tools today.
22:51A lot of these platforms, when it comes to identifying the anonymous visitors, they're almost 70% or a little more than 70% accurate when it comes to company identification.
23:00They're only about 15 to 20% accurate when it comes to individual identification.
23:05And this is a structural limit and a structural issue with these systems.
23:11The only way to truly maximize this is to keep testing different systems till you hit on one that gives you a much better numbers than what you already have.
23:22But most important thing here is to keep these metrics in mind as you plan your GTM outreach.
23:30And finally, the human bottleneck.
23:34The minute the approval queue feels like a chore, it's going to become backlogged.
23:40It's going to be ignored and the system is not going to be trusted anymore.
23:44What this means is you need to make sure that you shrink the friction between let's say a draft email that's ready to be sent to that being sent.
23:57And what I mean is if your draft email is in a spot where it's almost ready to go with very minor edit, and you're able to kick it out with just one click,
24:07then that's a system that's going to sustain.
24:11But if the time taken to edit an email that the AI writes is, let's say, longer than we look at 30 seconds.
24:20If it's longer than 30 seconds, then this is a dead initiative because the sales would then rather focus on drafting their own email as opposed to trusting what the system gives them.
24:34Finally, I want to leave you with four key takeaways.
24:37One, you have to start with the identity.
24:40Not being able to identify visitors who who land on your site is as good as a dead GTM project.
24:47What you need is a system that's put in place, just like what I showed you earlier or something similar to that, that is robust enough to identify the visitors coming to your website
24:58and has a feedback mechanism where that information is able to go back into your knowledge base and vice versa.
25:05The second is the score fit and intent needs to be looked at separately.
25:09Conflating them is going to send the wrong message to the wrong person.
25:14The third is you need to have a policy engine that is auditable and adjustable.
25:19What it means is you need to have a system where if it doesn't work, you have a way to audit which agent or what part of the workflow, for example, like the one that I just showed you, is broken
25:31and you have a way to go fix it as opposed to going after a developer to help you come fix it.
25:37So, you need to build a system where as a GTM leader or a GTM team member, you are able to control the system and fix it when needed.
25:46Because things do break quite frequently when it comes to AI and managing the output from AI.
25:53Finally, you need to let the flywheel compound.
25:57What I mean by that is every send, every reply, and every closed deal should make the model smarter, should make your system smarter.
26:06What it means is any information about wins, losses, deferred sales, all that needs to go back into your knowledge base and your agents need to be able to capture that information
26:19to make the right decision for the future.
26:23That's all I have for you today.
26:24Thank you very much for your time.