Daniel Saks
Chief Executive Officer
Is your B2B go-to-market strategy ready for the agentic AI revolution? 2025 is poised to be the year of agentic AI in sales and marketing, as autonomous “digital agents” move from buzzword to business reality. In fact, over half of organizations plan to deploy AI agents in their GTM workflows in the next year, and analysts predict that by 2028, one-third of all enterprise software applications will include agentic AI(2). These intelligent agents are transforming how companies find, engage, and convert customers – and B2B sales and marketing leaders need to pay attention.
Landbase, an AI technology company pioneering agentic AI for B2B growth, has been at the forefront of this shift. After emerging from stealth in 2024 with the world’s first agentic AI platform for sales and marketing, Landbase’s momentum has validated the market appetite for AI-driven GTM. (The company grew revenue 825% year-to-date in 2025 and scaled from 10 to 150+ paying customers in under a year.) But beyond the hype, what practical innovations does “agentic GTM” entail? This article explores the top 7 agentic GTM innovations to watch in 2025 – game-changing developments already boosting pipeline and productivity for forward-thinking teams. Each innovation is explained in depth with data-driven examples, from multi-agent AI “SDR teams” to 24/7 pipeline engines. We’ll also highlight how Landbase and others are leveraging these advances to redefine go-to-market execution.
By understanding these seven trends, B2B sales and marketing leaders can get ahead of the curve – and learn how to turn AI from a buzzword into a strategic advantage. Let’s dive in.
In traditional sales tech, you might use a single AI assistant or chatbot to automate one task. Agentic GTM takes this a leap further by deploying multi-agent collaboration – essentially an AI team that mimics an entire sales development unit working in unison(2). Instead of one bot sending emails in isolation, you have specialized AI agents handling each stage of the outreach process and coordinating their efforts. It’s like an “AI SDR dream team” where each member has a role: one agent researches prospects, another crafts personalized messages, another orchestrates multi-channel outreach, and yet another analyzes responses to optimize strategy(2). They work 24/7 in parallel, handing off tasks seamlessly – something no single human (or single AI) could replicate at scale(2).
The impact of this specialization and synergy is dramatic. Companies using multi-agent AI report up to 7x higher conversion rates and 60–70% lower outbound costs versus traditional methods(2). In one case, a startup saw its AI-driven outbound campaign convert 7 times more prospects into pipeline compared to using a single generative AI tool(2). Why? Because a coordinated squad of AI agents can strategize and execute together far more effectively – the prospector agent finds high-potential leads, the copywriter agent generates tailored outreach for each, the outreach agent sends and follows up diligently, and the optimizer agent learns from every interaction to improve results(2). Each agent contributes its specialized intelligence, and together they adapt in real time to maximize ROI.
Multi-agent collaboration essentially turns your outbound sales process into a well-oiled, always-optimizing machine. Every prospect gets thorough research, every message is personalized, every follow-up is on time, and no promising lead “falls through the cracks” because an AI got too busy(2). As your target list grows, you don’t need to exponentially grow headcount – your AI team simply scales up its parallel processing, maintaining quality and cadence without missing a beat(2). And importantly, this isn’t about removing humans from the loop, but enhancing them: many organizations pair their human sales reps with an AI SDR team, letting the AI handle the heavy lifting of prospecting and repetitive outreach while human reps focus on high-value conversations and closing deals(2). The result is a powerful human-AI partnership.
Landbase’s platform exemplifies this multi-agent approach. Its proprietary AI engine, GTM-1 Omni, deploys a squad of specialized AI agents for each client – a “GTM Engineer” to plan campaign strategy, an AI Prospector to mine data, an AI Copywriter to personalize content, an AI Outreach Coordinator to execute sequences, an AI Optimizer to refine tactics, and more(2). These agents collaborate continuously, 24/7, acting as a virtual SDR team that can generate pipeline on autopilot. This architecture is a key differentiator for Landbase. Rather than just using AI to write an email here or there, Landbase uses it to orchestrate entire campaigns autonomously. B2B leaders are taking note: surveys show only ~10% of organizations use AI agents in sales today, but over 50% plan to adopt them in the next year, and 82% expect to integrate agentic AI within 3 years(2). Multi-agent systems are at the heart of this trend, because they prove that AI can do more than assist a task – it can own a process.
Transition: Multi-agent AI collaboration lays the foundation for an autonomous GTM engine. Next, we look at how a cutting-edge UX innovation is making it even easier for teams to harness this power in their day-to-day workflow.
Even with powerful AI agents under the hood, marketers and SDRs need an easy way to interact with AI-generated strategies. Enter the Campaign Feed UX, an innovative interface that makes launching campaigns as simple as scrolling a social feed. Landbase introduced the Campaign Feed in April 2025, dramatically simplifying campaign management by presenting AI-recommended campaigns in a familiar “feed” format. Instead of manually configuring every aspect of an outbound campaign (selecting targets, writing copy, setting up email sequences, etc.), users can now just review AI-suggested “campaign cards” and launch them with one click. The feed proactively suggests target audience segments, personalized messaging angles, and optimal channel mixes based on real-time data – much like how a Netflix feed recommends movies, the Campaign Feed recommends ready-to-go campaigns tailored for your business(1).
The result? What used to take weeks of planning can happen almost instantly. Landbase reports that since rolling out the Campaign Feed, the average time to launch a new campaign has dropped from ~14 days to just minutes. Launching campaigns in minutes, not months has become a reality. This is a game-changer for go-to-market speed and agility. For example, before, a marketing team might spend 2–3 months hiring SDRs, sourcing leads, writing outreach sequences, and then iterating – now, Landbase’s AI can propose a full multi-channel campaign in a single afternoon. One early Landbase client described how they launched an outbound blitz in under a week using the AI feed, whereas doing it manually would have taken a whole quarter. In fast-moving markets, compressing time-to-campaign gives teams a huge competitive edge.
The Campaign Feed’s UX is also lowering the skill barrier. Even a small startup with no sales ops expertise can leverage sophisticated campaigns by tapping the feed’s suggestions. The AI distills complex analytics into simple recommendations (“Target these 500 accounts in fintech with this email sequence and LinkedIn touchpoint”). Users can accept, tweak, or reject suggestions – but importantly, they don’t have to start from scratch. This “vibe coding for GTM” approach (as CEO Daniel Saks calls it) brings the fun and ease of prompt-based AI creation into the marketing realm. It makes GTM execution feel as easy as browsing your LinkedIn or Twitter feed, turning what used to be a tedious setup process into a quick, interactive experience. Early feedback has been extremely positive, with users saying the Campaign Feed “brings the fun and effortless experience of vibe coding to go-to-market”, making campaign ideation feel almost like using a creative app(1).
Key features of this AI-driven feed include:
By providing a unified, visually intuitive interface, the Campaign Feed empowers teams to move fast. It abstracts away the technical complexity (data, tools, integrations) and lets you focus on strategy and creative tweaks. Small wonder that after adopting the Campaign Feed, even lean teams with no dedicated ops support can spin up sophisticated campaigns overnight. In short, this innovation marries powerful agentic AI with an approachable UX – enabling B2B marketers to act on AI insights immediately.
Transition: With AI agents now proposing and launching campaigns at lightning speed, the next question is how AI can support the human side of the sales process. Once those campaigns generate leads, how do we ensure smooth handoffs and effective follow-through? That’s where the AI Account Executive assistant comes in.
Not all sales activities can or should be fully autonomous. The later stages of the funnel – engaging in consultative conversations, negotiating, closing – are still largely the domain of human account executives (AEs). However, AI is now stepping in as an assistant to those AEs, ensuring that no lead is left behind and that reps have superhuman support in closing deals. The AI Account Executive assistant is an emerging innovation that acts as a virtual co-pilot for your sales reps. It doesn’t replace the human touch of closing a deal; instead, it augments AEs by handling follow-ups, providing deal intelligence, and automating many time-consuming tasks between initial lead interest and final sale.
Landbase rolled out an “AI Account Executive” assistive agent in 2025 to bridge the gap between its autonomous top-of-funnel and the human-driven bottom-of-funnel. This AI AE assistant does things like draft personalized follow-up emails after a prospect expresses interest, summarize what messaging has resonated with that prospect so far, and even recommend next-best actions for the human rep. For example, if a prospect interacted with certain content in the outreach campaign, the AI might suggest the AE emphasize that topic on the next call. Or it might auto-generate a briefing note: “Prospect responded positively to cost-savings angle – recommend sending case study on ROI next.” The goal is to equip the AE with all the context the AI gathered during automated outreach, so the handoff is seamless and the rep can tailor their approach to what the prospect cares about. In essence, the AI assistant ensures the human seller walks into the meeting armed with rich insights and even pre-drafted materials, rather than starting cold.
This innovation addresses a big efficiency gap in B2B sales. Studies show reps spend only ~28–36% of their time actually selling; the rest is eaten up by administrative tasks, research, and writing emails(2). No wonder around 67% of sales professionals weren’t on pace to hit quota last year(2). An AI AE assistant directly attacks this problem by offloading a lot of the “heavy lifting” that AEs normally do between meetings. It can monitor the pipeline 24/7 and nudge both the prospect and the rep so that momentum isn’t lost – for instance, automatically sending a polite follow-up to a prospect who went silent, or alerting the AE if a key stakeholder opened the proposal document late at night (something a human rep might miss). By handling the routine touches and data gathering, the AI frees human AEs to focus on high-value conversations and personalized problem-solving with the client.
The early returns are promising. Companies that have deployed AI assistants for sales reps report significant productivity and revenue gains. In one example, Verizon saw nearly a 40% increase in sales after rolling out a Google AI assistant to support its customer representatives, allowing them to focus more on selling while the AI handled real-time call guidance and info retrieval(4). More broadly, marketing experiments have found that human teams paired with AI agents experience a 60% boost in productivity per worker on tasks like content creation and follow-up, thanks to higher-quality output and speed(4). The AI AE assistant follows this pattern – it’s like giving each of your account executives their own tireless junior sales analyst who preps materials, keeps track of every lead’s status, and even writes first drafts of outreach. Importantly, this leads to better buyer experiences too: prospects get faster, more informed responses. They aren’t stuck repeating information or waiting days for next steps, because the AI ensures timely, context-rich engagement at every step.
Landbase’s platform emphasizes this human-AI collaboration. The AI SDR agents work the top of funnel and pass warm leads to human sellers, but now with an AI AE agent assisting, both sides of the funnel are instrumented by AI. As CEO Daniel Saks notes, it creates a partnership where “the AI SDR finds the opportunity and the AI AE helps close it, with the human closer making the final pitch.” The outcome is higher win rates and a smoother customer journey. In fact, Landbase’s own clients have seen their sales cycle shorten and close rates improve after implementing the AI’s meeting follow-up capabilities – the human reps spend more time in live conversations and less on writing follow-up emails, while prospects feel consistently engaged. This kind of augmented selling is poised to grow: Salesforce and HubSpot have both introduced AI assistants (for content suggestions and CRM updates), and we expect most B2B sales teams will incorporate some AI assistant to support AEs in 2025. The key is to keep the AI in an assistive role – empowering the human rep with information and automation, but letting the rep steer the relationship. When done right, an AI AE assistant becomes like an invaluable team member who never sleeps, never forgets a detail, and never lets a hot lead grow cold.
Transition: Speaking of never sleeping – one of the most powerful advantages of agentic AI is that it can keep your pipeline engine running around the clock. Our next innovation explores how 24/7 AI-driven outreach is changing the game for pipeline development.
Imagine having a global sales team that works every minute of every day, never takes a break, and immediately engages any lead, no matter when they come in. That’s essentially what 24/7 pipeline productivity offers – and it’s now possible thanks to agentic AI. Traditional sales teams have natural limits: they work mostly during business hours, and leads that come in off-hours often wait hours or days for follow-up. But an AI sales agent doesn’t sleep. It can be Monday 3:00 A.M. or Sunday afternoon, and your AI is still diligently prospecting, emailing, following up, and setting meetings. This always-on capability ensures that no opportunities slip by due to timing. In a world where speed-to-response is critical – 50% of B2B buyers choose the vendor who contacts them first(5) – having a 24/7 outreach engine confers a huge competitive advantage.
Landbase’s multi-agent system was built with this principle in mind: the AI agents work around the clock, engaging prospects across time zones and responding to triggers in real time. If a target prospect opens an email at 10:30 PM and clicks a link, the AI can automatically follow up at 10:35 PM with a tailored second touch while the prospect’s interest is peaked(2). There’s no waiting until the next morning (by which time a competitor might have already swooped in or the lead’s interest waned). In fact, one famous analysis found that contacting a lead within 5 minutes makes you 100x more likely to connect and 21x more likely to qualify that lead than waiting just 30 minutes(2). In the inbound world this is well-known, but it matters for outbound too: when an AI is monitoring for any sign of engagement (like an email open or webinar sign-up) and can react instantly, you dramatically increase your odds of conversion(2).
Equally important is persistence. Human sales reps, no matter how dedicated, have bandwidth limits and often let follow-ups slip. Studies show nearly 48% of salespeople never even send a single follow-up after an initial contact(2) – they might intend to, but get busy with other leads or give up after one try. An AI agent, by contrast, will never forget to follow up. It will execute every step of a multi-touch cadence with machine precision. If a prospect hasn’t replied to the first email, the AI waits the preset interval then dutifully sends the second email, then a LinkedIn message, then perhaps schedules a phone call – whatever the sequence entails – without ever dropping the ball(2). This relentless consistency pays off: making just a few extra contact attempts can boost connection rates by up to 3x(2). An always-on AI will make those extra attempts every time, whereas a human rep might stop after one or two tries.
Scale is another angle of 24/7 productivity. A single human SDR might manage 50 calls or 30 emails in a day; an AI system can handle hundreds or thousands of personalized touches in parallel, day or night(2). It can send out 1,000 individualized emails as easily as 10, monitoring each prospect’s responses and triggering the next action immediately. This elastic capacity means you are no longer constrained by headcount during critical campaigns or end-of-quarter pushes. For example, an AI could send invites to 500 target accounts over a weekend for a Monday webinar, then by Monday morning have already set meetings with those who showed interest(2). No 9-to-5 team could achieve that kind of coverage in such a short window. As one GTM strategist put it, these AI agents function with “zero burnout and 24/7 uptime” – they just don’t get tired(2).
The outcomes of always-on outreach speak for themselves. Companies using Landbase have reported that meetings get set even overnight and on weekends without human intervention, something previously impossible without hiring a follow-the-sun global SDR team. Leads receive timely engagement at any hour, which keeps the pipeline flowing smoothly. And critically, human teams benefit too: with AI covering the graveyard shift and the grunt work, your sales reps can reclaim their workday for high-value tasks. As one BusinessWire report noted, running outreach around the clock with AI frees up human reps to focus on closing deals and building relationships, instead of grinding through cold emails. Essentially, 24/7 AI acts as a force multiplier – one AI SDR agent can handle the equivalent workload of ~5 human SDRs without ever needing a break.
For sales and marketing leaders, this means your team’s productivity is no longer capped by headcount or office hours. If you need more pipeline, you don’t necessarily hire more SDRs; you can dial up the AI to engage more prospects, knowing it will maintain quality and speed at any volume. And if you operate globally, agentic AI will engage prospects in Europe or Asia while your human team sleeps, ensuring you don’t miss opportunities in other regions. In 2025’s hyper-competitive environment, being first and being fast is often the difference between winning and losing a deal. An always-on AI engine gives you that edge by making sure every lead is engaged promptly and persistently. It’s like having a tireless assistant working behind the scenes to maximize your pipeline around the clock.
Transition: Speed and scale are vital, but they must be matched with relevance. Next, we’ll look at how agentic AI delivers the kind of advanced personalization that turns mass outreach into meaningful, high-converting engagements.
Personalization has long been a buzzword in marketing, but advanced personalization at scale is now a reality thanks to agentic AI. Traditional outbound campaigns often fell flat because they were one-size-fits-all – generic email blasts that prospects would ignore or delete. Today’s AI-driven systems completely change that equation by customizing each touchpoint to the individual recipient, using everything the AI knows about them. The result is outreach that feels handcrafted by a top salesperson, but is actually generated and optimized by AI across thousands of prospects. This level of personalization is a major reason Landbase and similar platforms have seen outsized success: when every message speaks directly to a prospect’s pain points and context, conversion rates skyrocket.
How does it work? An agentic AI platform like Landbase’s GTM-1 Omni ingests vast amounts of data on each prospect – firmographics (company size, industry), technographic data (what tools the company uses), intent signals (e.g. recent funding, job postings, news mentions), past interactions, and more. The AI then crafts outreach that weaves in this intel naturally. For instance, it might open an email with “Hi Jane, I noticed Acme Corp just expanded into Europe – congrats! Typically, companies going through international growth struggle with X, which is exactly what we solve…” A human salesperson might do this level of research for their top 5 accounts; the AI is doing it for every single prospect, automatically. It can even mirror the prospect’s tone or language preferences learned from their LinkedIn posts or prior calls. Crucially, the AI adapts these personal touches in real time: if it finds a new tidbit (say the prospect just tweeted about a certain challenge), it can incorporate that into the next outreach touch. This dynamic, data-driven personalization at scale was nearly impossible before – humans simply couldn’t write hundreds of individually tailored emails per week – but now the AI makes it feasible(2).
The numbers underline how powerful this is. Companies that invest in personalizing their outbound outreach generate 40% more revenue than those that don’t(2). And personalized emails have been shown to drive 6x more transactions than non-personalized ones(2). Those are game-changing lifts. Early Landbase deployments demonstrated up to 7x better lead-to-opportunity conversion rates because the AI’s tailored messaging dramatically boosted engagement(2). Instead of blasting out generic “Dear Customer, we offer XYZ” emails that get lost in the noise, each prospect receives a message that feels genuinely relevant to them. For example, an AI might learn that CFOs in SaaS companies respond best to cost-savings angles, whereas CTOs respond to scalability themes – it will adjust the content accordingly for each recipient. Over time, the model refines these insights through continuous learning (reinforcement learning from response rates), getting even better at pressing the right buttons for each micro-audience.
But advanced personalization isn’t just about immediate clicks or replies – it also builds trust, which is critical in B2B sales. When a prospect feels that the outreach is speaking to their unique situation, they’re more likely to view the sender as credible and worth their time. Conversely, a cookie-cutter email screams “spam” and erodes trust. Landbase’s research found that lack of personalization (and the related lack of trust) is a major reason cold campaigns fail. By personalizing messages with specific details (like mentioning a prospect’s company initiative or citing a relevant industry challenge), the AI signals that “I’ve done my homework on you.” Prospects are far more inclined to respond when the message addresses their actual business pain points rather than delivering a generic pitch(2). In effect, agentic AI teaches our old outbound strategies a new trick: how to genuinely care about each lead at scale(2).
One striking example is how agentic AI can incorporate a company’s thought leadership content into outreach. Some Landbase users leverage this by feeding the AI things like their CEO’s recent blog post or a webinar deck, which the AI then references in relevant outreach. So a prospect might get an email that not only is personalized to them, but also quotes an insight from the company’s CEO on an industry issue – instantly adding credibility and a human touch, even though AI drafted the email. This blend of human expertise and AI execution is very powerful. As another benefit, when every email is customized, spam filters are less likely to flag your domain (since the content isn’t a repetitive template), improving deliverability.
To be sure, achieving true one-to-one personalization at mass scale requires a robust AI and data infrastructure. It’s not just mail-merge with names; it’s training models on what messaging works for which personas, and giving the AI a rich knowledge base to draw from. Landbase built a proprietary knowledge graph of 40+ million B2B interactions and 10 million+ intent signals to fuel its personalization engine. The result is an AI that “knows” how different buyers typically behave and can tailor outreach accordingly. As agentic AI platforms continue to evolve, expect personalization to get even more granular – e.g. adjusting tone for each individual (some people prefer brevity, others like details) or aligning messaging with a prospect’s personality profile. We’re moving toward a world where every prospect can be treated as a “market of one” at scale(2). For sales leaders, this means drastically higher campaign ROI and better customer experiences. The spray-and-pray era is ending; the era of AI-powered personalization is here, and it’s changing what effective outreach looks like.
Having a great message won’t help if it’s sent to the wrong person or at the wrong time. That’s why predictive lead scoring has become a cornerstone of AI-driven GTM. In 2025, agentic AI platforms are leveraging machine learning to analyze vast datasets and accurately predict which leads are most likely to convert – and when. This goes far beyond the static, rule-based lead scoring of the past (e.g. +5 points for job title, +3 for downloading a whitepaper). Modern predictive scoring uses AI to find subtle patterns and signals of buyer intent that humans might miss, enabling intelligent targeting: focusing your outreach on the prospects who have the highest propensity to turn into pipeline, and doing so at the moment they’re most receptive.
Landbase’s approach illustrates how this works. The platform ingests over 10 million real-time intent signals – from tech stack changes and hiring trends to news mentions and website visits – and correlates them with conversion outcomes across millions of outreach interactions. Using this data, the AI can determine, for example, that a company just raised a Series B funding round (a strong buying signal for many B2B products) or that a target contact just started a new VP role (often a time when they are open to new solutions). It then prioritizes and times outreach around these signals. If a target account shows a surge in intent (say multiple employees from that company are reading content about a problem your product solves), the AI will elevate that account in the queue and engage immediately. Conversely, leads that score poorly (no signals of interest or poor fit) won’t waste your SDRs’ time. This dynamic scoring is continuously updated – if a lead’s behavior changes, the score changes in real time, and the campaign adapts accordingly.
The impact is a significantly more efficient sales funnel. Sales teams spend their energy on the leads that matter, and fewer good prospects slip through unnoticed. According to Forrester, companies sticking to traditional lead scoring can suffer a 25% hit to sales productivity by chasing unqualified leads, whereas those adopting AI-powered predictive scoring see on average a 25% increase in conversion rates and 30% higher sales productivity(3). That’s a huge win – essentially a quarter more deals from the same lead pool – simply by being smarter about who to focus on. In one case study, an enterprise that implemented predictive lead scoring (with AI analyzing behavioral and firmographic patterns) was able to shorten its sales cycle by 20% because reps were engaging only with truly sales-ready leads, rather than nurturing every inquiry blindly.
Predictive lead scoring also improves alignment between marketing and sales. Historically, there’s often been friction: marketing might pass a bunch of leads to sales, but sales complains they’re low quality, meanwhile good leads get overlooked. An AI-scoring system provides a data-driven, unbiased way to grade leads, which both teams can trust. If the AI says Lead A has an 82% likelihood to convert based on thousands of data points, sales will call that lead first. If Lead B scores 20%, marketing might put them into a longer-term nurture track instead of pushing for immediate follow-up. This means higher efficiency and less wasted effort across the board.
What’s under the hood of predictive scoring? Typically, machine learning models (like gradient boosting or neural nets) trained on historical CRM data – wins vs. losses – plus enrichment data about those accounts. They learn which attributes and behaviors tend to precede a sale. For example, a model might discover that SaaS companies with 100–500 employees in the fintech industry that have recently hired a new CTO and opened 3 of your emails are highly likely to become customers. It will then score new leads that meet those criteria very high. These models can consider dozens of factors in combination (far beyond what a human-created point system could) and can update as new outcomes roll in. HubSpot’s AI predictive lead scoring, for instance, claims 77% accuracy in predicting lead qualification when the underlying CRM data is clean(3). As data quality improves and more signals are incorporated (like intent data from third parties), the accuracy will only get better. Landbase, through its 2025 acquisition of Delegate (a startup specializing in predictive analytics), has been enhancing its scoring algorithms to gauge not just who is a good fit, but when they are most likely in-market(1). The goal is a kind of “sixth sense” for sales – knowing precisely where to focus and when to reach out.
For GTM leaders, implementing predictive lead scoring is one of the highest ROI moves you can make with AI. It’s actionable intelligence that directly boosts revenue. It shortens the notoriously long B2B sales cycles by zeroing in on hot prospects sooner. And it helps you scale: if you suddenly get a flood of trial sign-ups or webinar leads, the AI can triage them faster than any human, ensuring your reps call the truly promising ones first. No more guessing or purely intuition-based lead qualification. As one marketing VP quipped, “Our AI scoring model is like having a crystal ball – it’s not perfect, but it’s far better than our old brute-force approach.” In 2025, expect predictive scoring to become standard in most advanced sales orgs (Gartner predicts 70% of B2B sales orgs will use AI for some form of lead scoring or process automation by 2025(3)). The technology is mature, and the competitive pressure to optimize sales efforts is intense. Those who embrace predictive targeting will reap the rewards in pipeline efficiency and win rates.
Staying ahead in the agentic AI race requires constant innovation. That’s why Landbase created an in-house Applied AI Lab in mid-2025 – to continuously push the boundaries of AI for go-to-market. This lab, led by a former ZoomInfo data science chief and staffed with PhDs and industry veterans from places like Stanford, NASA, Meta, and YouTube, is charged with advancing Landbase’s core AI engine (GTM-1 Omni) using the latest techniques in machine learning. It’s not just a token R&D department; it’s a significant investment aimed at ensuring Landbase maintains a technological lead as the market evolves. For B2B leaders, the work coming out of such labs offers a preview of next-generation GTM capabilities that could be mainstream in a few years.
So, what’s brewing in the Applied AI Lab? One focus area is reinforcement learning (RL) – training the AI agents to get better over time by simulating sales scenarios and learning from trial and error. For example, imagine an AI SDR agent that tries different email approaches in a sandbox and learns which generate replies, effectively “leveling up” its skills autonomously. By applying RL, Landbase aims to make its multi-agent system even more adaptive and self-optimizing in complex environments (like when multiple AI agents need to coordinate on the best way to engage an account). Another area is deep data intelligence: the lab is exploring how to incorporate new data sources and signals into the AI’s brain. This could include things like real-time buyer intent data from third-party platforms, or even analyzing voice tones on sales calls to gauge sentiment. The more comprehensive the AI’s view of the prospect, the smarter its actions.
We’re also likely to see new specialized AI agents coming out of this research. Landbase’s roadmap already hints at expanding beyond the SDR function into later-funnel and post-sale roles. For instance, a Pipeline Analyst AI could monitor the health of the sales pipeline, identifying bottlenecks or deals at risk, and recommend interventions (like which opportunities the team should prioritize this week). A Customer Success AI agent might autonomously engage customers post-sale – handling onboarding messages, upsell prompts, or churn risk alerts – effectively extending agentic AI into account management and retention. By 2025 and beyond, we’ll likely see the concept of an “AI revenue team” that covers the entire customer lifecycle: from prospecting and closing (SDR + AE) to renewal and expansion (CSM). Landbase’s lab is working to bring these ideas to fruition, accelerating the development of such agents so they can be productized. This aligns with industry trends: a recent survey found 35% of Chief Revenue Officers plan to establish dedicated “GenAI” teams by 2025 to drive AI adoption in sales(4). The demand for AI across GTM functions is growing, and Landbase intends to supply the supply.
Another future-looking development is even better predictive models to power the lead scoring and content recommendations we discussed. With more data and more training, the AI’s predictive accuracy should continually improve – the lab is undoubtedly iterating on models that predict conversion likelihood, optimal send times, and even which messaging will work best for each account (a sort of meta-prediction). As mentioned earlier, HubSpot’s predictive lead scoring is ~77% accurate; Landbase will push toward higher reliability by leveraging its ever-expanding dataset and more sophisticated algorithms(3). The lab is also exploring how to incorporate generative AI advancements safely into the platform – for instance, using the latest GPT-like models but fine-tuned for GTM context, or developing new ways to ensure the AI’s outputs remain high quality and compliant as models get more powerful.
Finally, expect ongoing improvements in the “AI-native” user experience – something Landbase clearly values with innovations like the Campaign Feed. The lab can experiment with new interfaces (maybe a natural language chat interface to design campaigns by simply telling the AI your goals) or augmented reality visualizations for sales data. As agentic AI becomes more complex behind the scenes, making it simple and intuitive for end-users will be key. Landbase’s design philosophy is to have the AI do the heavy lifting while the user guides or approves, and future updates will likely double down on that approach.
In summary, the Applied AI Lab represents Landbase’s commitment to continuous improvement and thought leadership in agentic GTM. It’s a strong signal to customers and the market that Landbase isn’t resting on what it has today – it’s actively inventing the future of how companies grow. With $30 million in new funding raised in 2025 to fuel expansion and R&D(1), Landbase has the resources to execute on an ambitious vision. And as competitors emerge in the agentic AI space, this kind of R&D capability could be a decisive advantage. For B2B marketing and sales leaders, the takeaway is that agentic AI is not a static product you buy – it’s an evolving technology. Partnering with a vendor that is investing in research (and perhaps even collaborating with academia, as Landbase’s lab members do) means you’ll continue to benefit from the latest breakthroughs. The GTM innovations we’re excited about in 2025 – multi-agent systems, campaign feeds, AI assistants, etc. – were the result of past R&D. Likewise, the lab work happening now will likely produce the next wave of innovations in 2026 and 2027. It’s a fast-moving frontier, and Landbase intends to stay at the cutting edge.
Agentic AI is transforming B2B go-to-market strategy across the board. From multi-agent “virtual sales teams” that generate pipeline 24/7, to AI-curated campaign feeds that let you launch outreach in minutes, to predictive models that pinpoint your best prospects – the seven innovations above showcase how GTM is being reinvented in 2025. Forward-thinking sales and marketing leaders are already leveraging these advances to outpace competitors, close more deals, and do more with leaner teams. The common thread is clear: automation + intelligence = acceleration. By automating the grunt work and infusing every decision with data-driven intelligence, agentic AI allows your human team to focus on what they do best, amplified by machine efficiency and scale.
Landbase has emerged as a pioneer in this new era, delivering an agentic AI platform that encapsulates all these innovations – from multi-agent orchestration to hyper-personalization – in one solution(2). The company’s proprietary tech and ongoing Applied AI Lab research position it as more than a vendor, but a thought leader helping to define how modern companies grow. Landbase’s clients are seeing firsthand that agentic AI isn’t theoretical – it’s delivering real-world results like 7x pipeline conversion, 70% less time to launch campaigns, and significant cost savings(2).
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