WEBVTT

NOTE Sentence-level transcript of https://www.youtube.com/watch?v=ltv-L5oMPIs

NOTE One cue per sentence. Cue ids are the line anchors on /transcripts/ltv-L5oMPIs.html. A cue ends where the next begins, or 2 s after its last word.

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Hello.

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We know that the modern B2B selling has evolved significantly.

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The uncomfortable truth is that by the time the buyer reaches you, the decision is mostly made.

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They've researched, compared, and probably shortlisted vendors or partners that they want to work with.

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That may include you or your competitors.

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This talk is about the architecture that lets GTM teams know the buyer more than they today.

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My name is Sajjan Khan Akolanu.

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I have 20-plus years experience across product, technology, and marketing.

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At 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

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with our technology team.

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We'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

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June 2026.

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By the time we get to the end of the year, I expect these numbers to increase significantly.

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The interesting thing about GTM and AI is that many GTM teams try to bolt AI onto their existing stack or existing processes and workflows.

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And therein lies the problem.

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That approach itself is hindrance to scale.

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The 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.

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And these three are number one, AI.

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By just bolting AI onto your existing systems, AI doesn't really and cannot really find out who your buyer is.

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It doesn't understand their role.

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It doesn't understand their history.

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It doesn't understand their intent stand-alone.

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That's number one.

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Number two is the integration itself.

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The 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

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and bring that all together.

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So, the systems today as they stand may be broken.

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By putting AI on top of it is not going to help solve the problem.

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The third is the architecture.

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The underlying architecture has to evolve with AI at the core.

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And 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,

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and workflows, you will not be able to scale.

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And most GTM deployments do not solve for all these three problems simultaneously.

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You 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.

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So, it is important that all these three problems are solved together.

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And 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.

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Another problem that we face is that with the buyer.

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The buyers are constantly evolving.

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And what I mean by that is by the time the buyer reaches out to you in today's world, they're not early stage.

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They're actually late stage.

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Here are some stats to prove that point.

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94% of buyers use GenAI as a primary research.

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And 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.

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And that is information that is from the Forrester 2026 research.

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The second stat is about 67% of B2B buyers prefer a rep-free experience.

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They don't want to talk to people as they make the decision.

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80% of deals very interesting statistic.

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80% of deals go to the buyers who are part of the pre-contact list.

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So, essentially, buyers have already made up their mind.

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And the fourth and very vital stat here is only 17% only 17% of the total buying time is spent talking with potential vendors.

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What all these numbers tell us is the buyers do their research thoroughly.

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They use various tools including the GenAI platforms.

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And by the time they come talk with you or come to your website, they have made up their mind.

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Now, 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.

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That is now responsible for telling them about what you have to offer and how they can buy your services or product.

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But many GTM systems that are bolted with AI would essentially start off with a conversation that would be something like this.

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How can I help you?

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What is it that you would like to What is it that you would like to know today?

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But these are questions that actually move you backwards.

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They don't move you forward as an organization.

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The buyer has done their research and then some basic questions are asked to them.

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They're actually walking out of the system.

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They're not continuing their conversation.

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So, 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,

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and you're able to cater to those as they engage on your site.

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So, what does this really mean when it comes to implementing an architecture?

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How does one accomplish an architecture or a system where the buyer information is known as soon as they come to your site?

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There are essentially three architecture layers behind an intelligent system and running an intelligent GTM.

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The first is signals.

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This 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,

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who are you conversing with.

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It also includes an enrichment system where you have the right information pulled out of these anonymous visitors that are hitting your site.

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This system is going to work as long as that information is accurate.

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If you're off on the enrichment, your data is off, and so is your communication.

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And one of the things that we find really helpful and really useful, very important, is signals coming out of social media platforms.

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Especially 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.

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And these these signals really help identify how and who you should target and when.

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So, 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.

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Given 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,

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and they become your new exec sponsor, your champion in that organization.

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That'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.

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And I'll talk about how to how to do that in just a second.

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The second layer is that of buyer intelligence.

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This is about making sure you have the right information about the buyer.

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It 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.

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A 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.

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Everything that makes you successful sits in a knowledge base.

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It also has visitor identity information, de-anonymized information sitting in your buyer intelligence database.

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The 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.

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All these are things you hope to quantify within your buyer intelligence system.

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Then you have a context builder.

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All the signals that you have need to form a context graph that I'll talk about next.

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And then, you need to have a routing logic.

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This is where you would define what message goes to who, when.

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So, if someone were to engage with you on LinkedIn, do they get a LinkedIn outreach, or do they get an email?

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Or should they be shown an ad?

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Um these are the insights or inputs that actually go as part of your routing logic.

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The third layer is that of action.

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Once 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

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uh 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?

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What are you looking for?

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What's your name?

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That's not the communication at this point.

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The communication's personalized and I'll show you an example what I mean by that.

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It needs to identify the user, address them by name, and talk about where they potentially left off last time they were on your site.

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Or based on the intent signals, the chat needs to offer them specific content.

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The action also based on these signals should be about sending the right alerts and information to the rep.

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Too many, you lose the the rep, the sales rep, and too few, you're not helping them hit their hit their numbers.

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Um and and get revenue for the organization.

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The third piece here is that of updating the CRM.

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The CRM system cannot just be an update about the contacts that the sales had.

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It needs to be about the context on about the account that the first two layers drive for you.

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And finally, the sequence trigger.

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The actions need to determine what type of outreach sequence need to be sent out at what time.

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What outreach is triggered when, to who?

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Without 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.

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And here's an example of context graph.

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All the signals we just talked about roll into this connected record, which is per buyer.

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The persona, account, and deal, these are all signals and actions that are logged for every touch point across the account for every single person.

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A person's signals link to their account.

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The accounts have multiple people in them.

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So, the buying committee information surfaces for the account based on the signals coming by person and for each account.

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Then, you have deal level information here, which is very specific to sales and marketing activities.

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So, has sales and has marketing already reached out to this individual or people the buying committee at this account?

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And all these together with all those signals and data points and touch points become a context graph.

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The 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.

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And 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.

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Now, 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

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shows 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.

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This is how the three-layered architecture I just talked about looks like as connected agents.

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When a user lands on our site, at that moment we don't know who they are.

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They're anonymous.

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And that is when that visit kicks off the first job, which is the identification job.

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And as you'll notice, I'm using more than one source here running together, and that's because no single tool catches every visitor.

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We 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.

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These tools change all the time for us, but not necessarily the architecture as such.

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Once these contacts are enriched, the next layer, which is the intelligence layer, kicks in.

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Here you'll notice that our ICP filter agent kicks in, which is referencing information from the knowledge base.

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What 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.

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Anything that doesn't fit, such as wrong industry or wrong geo, wrong size, wrong persona, are weeded out.

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Those contacts and information are deleted.

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What fits our criteria and what's relevant moves into a clean table of qualified accounts.

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After this, the match agent kicks in.

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It checks for people against our CRM.

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Have we talked to them?

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Are they warm?

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Are they hot?

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That's the evaluation that happens at this stage.

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Finally, the action agent kicks in.

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This agent decides what happens next per person.

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And 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.

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Here, 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.

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This agent also decides if an email has to be fired up and sent over to the contacts we have.

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It also decides if we need to use LinkedIn for an outreach.

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And this is how the workflow works from the three-layered architecture to how we implement GTM agents today at Position Squared.

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Now, let me walk you through how the system actually looks in the back end.

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So, we saw the three-layered architecture.

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We saw the agents working together.

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Now, what does the output look like is what I'll show next.

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Here's a view of our system at Position Squared, which is called Intelligence.

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What 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.

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Instead 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.

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And 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

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uh part of earlier.

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What 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.

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So, as opposed to having a generic greeting for me, this is more personalized.

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Let 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.

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Here's a view of the interface that gets us into our dashboard.

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What you have here is two key sections that we have built.

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One is for GTM, the other is organic.

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For our conversation today, I'll be walking you through what happens when we go to our GTM dashboard.

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Once you enter the dashboard, what you see are multiple cards.

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And for the conversation today, I will be focusing on the anonymous visitors and the LinkedIn intelligence sections.

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What 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

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is de-anonymizing the users.

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It's identifying the users that come onto our website, and it's categorizing them into multiple sections.

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What you see here is we have our system has identified about 3,000 odd people or visitors coming in from about 280 different accounts.

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And the system tells us that information technology is one of the key verticals or key industries that's visiting our site.

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And this is in fact one of the verticals that we focus on.

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What you also see is the industry breakdown, as well as hot signals.

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And this is information of people from different companies that are being categorized as hot contacts or hot accounts at this point.

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This is a combination of different signals including the conversations that have happened within HubSpot.

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For the sake of the conversation here, I have changed the names although the titles are accurate.

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If 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.

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All this information is uh or or can be sorted.

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All this information can be sorted based on industry, seniority, different types of engagements they've had with us.

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And we can also look at the companies that visit our site.

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The second part of this is if I were to go to the LinkedIn intelligence, and this is the part that many GTM teams miss.

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LinkedIn has a wealth of information that is very useful.

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What 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.

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It has a list of all the folks that have engaged and the type of engagement they've had with us.

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We can actually sort them out by C-suite, VPs, directors, managers, and individual contributors.

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Those are the categories that we use.

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And you have a list of companies where they belong to.

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By clicking on each of these, we can actually go into very specific details about their overall engagement with us.

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This level of detail really helps us bring in the right level of intelligence into our system.

s197
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Now 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.

s198
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And I want to focus on four key areas.

s199
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The first is the ICP drift, and this is real.

s200
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Your ICPs evolve over time.

s201
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For 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.

s202
00:21:34.680 --> 00:21:38.360
And that information is critical to enter into your knowledge base.

s203
00:21:38.360 --> 00:21:45.680
Without that, your agents are working off of the old information, always giving you wrong wrong data that you might not want.

s204
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What this means is you need to retrain your models.

s205
00:21:50.440 --> 00:21:58.440
You retrain your agents every quarter with your closed one or closed lost opportunities.

s206
00:21:58.440 --> 00:21:59.760
And this is critical.

s207
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Without this, your agents are looking at wrong information, pointing you to the wrong accounts and the wrong people.

s208
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The second point is alert fatigue.

s209
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If everything is flagged as hot to a sales rep, they would stop acting because it's it just gets overwhelming for them.

s210
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And at that moment, they stop trusting the system and the system's dead.

s211
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So, it's important to use the knowledge base and the context graph to cut through the noise and focus on the alerts.

s212
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Focus on alerts from the accounts and contacts that do actually matter for the sales people to act upon.

s213
00:22:43.600 --> 00:22:47.560
The third item is identity ceiling.

s214
00:22:47.560 --> 00:22:51.240
And this is a limit that exists with tools today.

s215
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A 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.

s216
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They're only about 15 to 20% accurate when it comes to individual identification.

s217
00:23:05.240 --> 00:23:11.120
And this is a structural limit and a structural issue with these systems.

s218
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The 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.

s219
00:23:22.600 --> 00:23:29.240
But most important thing here is to keep these metrics in mind as you plan your GTM outreach.

s220
00:23:30.080 --> 00:23:32.960
And finally, the human bottleneck.

s221
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The minute the approval queue feels like a chore, it's going to become backlogged.

s222
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It's going to be ignored and the system is not going to be trusted anymore.

s223
00:23:44.920 --> 00:23:57.240
What 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.

s224
00:23:57.240 --> 00:24:07.880
And 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,

s225
00:24:07.880 --> 00:24:11.040
then that's a system that's going to sustain.

s226
00:24:11.320 --> 00:24:20.280
But if the time taken to edit an email that the AI writes is, let's say, longer than we look at 30 seconds.

s227
00:24:20.280 --> 00:24:34.440
If 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.

s228
00:24:34.440 --> 00:24:37.080
Finally, I want to leave you with four key takeaways.

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One, you have to start with the identity.

s230
00:24:40.400 --> 00:24:47.800
Not being able to identify visitors who who land on your site is as good as a dead GTM project.

s231
00:24:47.800 --> 00:24:58.320
What 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

s232
00:24:58.320 --> 00:25:05.120
and has a feedback mechanism where that information is able to go back into your knowledge base and vice versa.

s233
00:25:05.120 --> 00:25:09.440
The second is the score fit and intent needs to be looked at separately.

s234
00:25:09.440 --> 00:25:14.400
Conflating them is going to send the wrong message to the wrong person.

s235
00:25:14.400 --> 00:25:19.320
The third is you need to have a policy engine that is auditable and adjustable.

s236
00:25:19.320 --> 00:25:31.400
What 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

s237
00:25:31.400 --> 00:25:37.360
and you have a way to go fix it as opposed to going after a developer to help you come fix it.

s238
00:25:37.360 --> 00:25:46.640
So, 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.

s239
00:25:46.640 --> 00:25:52.840
Because things do break quite frequently when it comes to AI and managing the output from AI.

s240
00:25:53.160 --> 00:25:57.160
Finally, you need to let the flywheel compound.

s241
00:25:57.160 --> 00:26:06.920
What I mean by that is every send, every reply, and every closed deal should make the model smarter, should make your system smarter.

s242
00:26:06.920 --> 00:26:19.640
What 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

s243
00:26:19.640 --> 00:26:22.920
to make the right decision for the future.

s244
00:26:23.160 --> 00:26:24.600
That's all I have for you today.

s245
00:26:24.600 --> 00:26:27.320
Thank you very much for your time.