Daniel Saks
Chief Executive Officer
The landscape of B2B sales and marketing is evolving at breakneck speed. Traditional go-to-market (GTM) strategies – often reliant on time-consuming manual processes and a patchwork of tools – are struggling to keep up. Sales reps today spend only about one-third of their time actively selling, with the rest lost to administrative tasks and prospect research(8). Inefficiencies like these slow down pipeline generation and leave revenue on the table. Businesses that fail to adapt risk being left behind. Enter agentic AI: autonomous AI agents that can plan, execute, and optimize multi-channel campaigns with minimal human intervention. Rather than juggling 8–15 different sales tools(15)and repetitive sales development tasks, companies can deploy AI agents as an extension of their team – analyzing vast datasets, identifying high-intent prospects, orchestrating personalized outreach across channels, and continuously learning from every interaction to boost engagement and conversion rates.
This new generation of AI GTM agents is changing the game for revenue teams. Agentic AI platforms operate like a virtual SDR team-on-autopilot, handling everything from prospect discovery and data enrichment to outreach, follow-ups, and hand-offs to sales – all without constant oversight. The result? Significantly faster go-to-market cycles, higher conversion rates, and substantial cost savings. Early adopters are reporting eye-popping gains, such as 4–7× higher lead-to-meeting conversion rates and 70%+ lower costs compared to traditional approaches(1). In this blog, we’ll explore leading AI agents transforming GTM execution, including Landbase and other top platforms in this emerging space. We’ll examine each solution’s key features, unique strengths, and real-world results, backed by data and user experiences.
Let’s dive into the frontrunners of autonomous go-to-market AI – and see how they’re revolutionizing sales and marketing in 2025.
Landbase stands out as the first truly agentic AI platform with complete end-to-end workflow execution for sales and marketing. Powered by its proprietary GTM-1 Omni model (the world’s first AI action model purpose-built for GTM), Landbase acts as a “GTM team in a box,” autonomously running multi-channel campaigns with minimal human input(2). It orchestrates the entire process – from identifying ideal prospects to crafting personalized outreach to managing technical send-outs – all using collaborative AI agents that mimic a full sales and marketing org. Since launching in late 2024, Landbase has gained strong traction (825% revenue growth, 150+ customers in its first year) and delivered 4–7x higher conversion rates than traditional outbound campaigns(2). One customer, P2 Telecom, even added $400k in new MRR during a historically slow period using Landbase – and had to pause campaigns because their human AEs couldn’t keep up with the leads(2)!
Key Features: Landbase’s GTM-1 Omni model is trained on a massive dataset of successful B2B sales motions, giving it unparalleled knowledge of what works. Some defining capabilities include:
Pricing & ROI: Landbase offers a transparent, SMB-friendly subscription model with no hefty annual minimums or long-term commitments. The pricing is usage-based and designed to be accessible even to startups. Thanks to automation efficiencies, customers typically see an up to 80% reduction in total cost of ownership versus piecemeal tools(1). In fact, Landbase can replace 15+ separate sales tools with one unified platform – eliminating costs for data providers, email automation software, sequence tools, intent databases, etc. The economic impact is substantial: businesses not only save on software, but also avoid the headcount costs of hiring large SDR teams. By automating outreach and letting sales reps focus on closing, Landbase users do more with less.
Perhaps Landbase’s greatest strength is its truly autonomous operation. The platform requires very minimal babysitting – customers often describe it as a “set it and forget it” system that reliably generates pipeline in the background. “It’s like hiring a 24/7 SDR team that never sleeps,” one user noted, “except the AI team actually produces 5x the results of my human team.” Landbase’s documented outcomes include 4–7x conversion uplifts in campaigns(2), and even mid-market companies have added hundreds of thousands in new recurring revenue within a single quarter of use(2). For instance, Bill Patchett, CEO of P2 Telecom, said: “With Landbase, we added $400k MRR in a slow period… We had to put it on pause because our AEs couldn’t keep up.”(2). Such real-world successes underscore how Landbase’s agentic approach can unlock growth even when traditional methods falter.
For B2B SaaS companies looking to optimize their go-to-market, Landbase’s autonomous AI agents prove invaluable. The platform essentially delivers pipeline-as-a-service. It eliminates the content quality issues that plague some AI tools – thanks to the enormous training dataset of successful B2B communications behind GTM-1 Omni, Landbase’s generated emails sound natural and on-brand, not like spam. Every touchpoint is hyper-personalized and contextually relevant, yet the AI scales this across thousands of prospects effortlessly. Users consistently highlight that Landbase requires far less hands-on management than other platforms. Unlike generic sales automation that needs constant tweaking, Landbase adapts itself. Teams can truly “set it and forget it,” intervening only to check the analytics or to toggle campaign parameters – the heavy lifting is handled by the agents.
In short, Landbase is leading the charge in AI-driven GTM execution. It exemplifies the next evolution of sales tech: fully autonomous agents that drive revenue growth while your human team focuses on closing deals. The platform’s proven ability to generate pipeline at scale, faster and cheaper than ever, gives companies a decisive edge. One early adopter summed it up: “Landbase delivered in one quarter what we had struggled to do in an entire year before.” For organizations seeking to supercharge their sales funnel, Landbase is a compelling first stop on the path to an AI-augmented go-to-market.
San Francisco-based 11x has emerged as another prominent player in the autonomous GTM arena. Whereas Landbase offers a comprehensive platform, 11x brands its solution as “Digital Workers” – essentially specialized AI agents that can be “hired” to perform specific sales roles. Notably, 11x offers two main AI sales agents: Alice, an AI SDR focused on outbound prospecting, and Julian, an AI phone agent for rapid inbound lead follow-up. This two-agent suite covers both sides of the sales funnel – Alice engages cold prospects across email, social, etc., booking meetings on autopilot, while Julian handles incoming inquiries, calling new sign-ups within seconds, qualifying them, and routing hot leads to human reps(6). Together, they aim to drive full-funnel revenue acceleration for businesses by operating 24/7 and at a scale no human team could match.
11x positions itself as an AI-native alternative to hiring SDRs, boasting that its digital workers “never sleep, never quit, and never get tired of cold outreach.” Under the hood, 11x’s platform leverages advanced multi-agent architecture and extensive data integrations. According to an IBM partnership announcement, 11x’s AI agents tap into 17+ intent signal providers and achieve email deliverability rates 5x higher than standard sales tools(6). In other words, they bring a deep data advantage – monitoring many data sources (from technographic and firmographic databases to engagement signals) to inform who to contact and when. The system also emphasizes enterprise-grade email/domain setup to maximize inbox placement (a critical factor for outbound success). This focus on data depth and technical execution helps 11x’s agents outperform typical outreach sequences that might suffer from bounces or spam issues.
Another strength is 11x’s execution speed and volume. Because the AI workers operate continuously, they can scale outreach dramatically. For instance, Alice can manage prospecting across hundreds of accounts simultaneously, sending personalized emails, LinkedIn messages, and even physical mailers as needed. Julian, on the other hand, can call every new demo request or website sign-up within minutes, ensuring no inbound lead falls through the cracks. This immediacy can be game-changing – research shows 35–50% of sales go to the vendor that responds first to a buyer inquiry(8), so automating that instant response confers a big advantage. Companies using 11x effectively get an “always-on” SDR team: every lead is touched quickly and persistently.
While 11x is a younger startup (founded in 2023), it has garnered notable investor backing and validation. The company is funded by top VCs including Andreessen Horowitz and Benchmark, and in 2025 IBM chose 11x as a partner to integrate into its platform(6). This strategic partnership means 11x’s AI sales agents will be available to IBM’s enterprise customers worldwide – a strong endorsement of 11x’s technology. According to 11x CEO Prabhav Jain, “Digital Workers will become as important to driving revenue as CRM did over a decade ago – but with an even bigger impact.”(6) By teaming up with IBM, 11x aims to accelerate adoption of AI-driven sales automation in large organizations.
In terms of real-world impact, 11x so far has been a bit cautious about releasing specific performance metrics (their website emphasizes capabilities over case studies). However, anecdotal reports suggest meaningful results: for example, one source notes an 11x client saw a 5× increase in outreach productivity after deploying Alice and Julian, thanks to the agents’ ability to personalize at scale and engage leads around the clock. 11x itself claims its digital workers have delivered “ruthlessly efficient” pipeline creation for customers and cites higher meeting volumes and pipeline throughput as common outcomes. As evidence of traction, the company grew rapidly through 2024 – one newsletter reported that 11x closed $700K in new ARR within 2 months of switching to a sales-led go-to-market, indicating strong demand for its offering(5). By mid-2025, 11x had reportedly reached over 100 customers and was scaling revenue (helped by the IBM channel).
Artisan is another buzzed-about startup in the AI sales agent space, known for its provocative “Stop Hiring Humans” marketing campaign. Founded in 2024 and a graduate of Y Combinator, Artisan offers an AI-powered SDR platform centered on its virtual sales rep “Ava.” Ava is an AI SDR (Sales Development Representative) agent that automates outbound lead generation – essentially handling the top-of-funnel work of researching prospects, writing personalized emails, and sending sequences at scale. The concept is to replace a human BDR’s repetitive tasks with an AI employee who can prospect tirelessly 24/7. Artisan’s vision extends beyond just one role: they refer to their AI bots as “Artisans” (AI employees) and have plans for additional agents like Aaron (an AI to handle inbound sales queries) and Aria (an AI meeting scheduler) by end of 2025(4). But today, Ava is the flagship, used by all Artisan customers for outbound outreach.
In terms of traction, Artisan has quickly attracted both investors and users. The company raised a $25 million Series A in April 2025 (led by Glade Brook Capital with participation from Y Combinator and even HubSpot Ventures)(4). By that time, CEO Jaspar Carmichael-Jack (only 23 years old) reported that Artisan had 250 paying customers and ~$5 million in annual recurring revenue from its AI SDR product(4). That’s impressive growth in about a year since launch, indicating strong market appetite for AI-driven outbound. Artisan also claims to have generated significant pipeline for clients – one early report touted $700K in ARR added in just 2 monthsafter some improvements, as the company fine-tuned its go-to-market(5). These figures demonstrate that, when it works, an AI SDR like Ava can produce tangible results fairly quickly.
However, Artisan’s journey hasn’t been without challenges. Early versions of Ava in 2024 were far from perfect – users reported that the AI wrote cringey emails and often hallucinated incorrect info about prospects. Carmichael-Jack himself admitted “we had extremely bad hallucinations when we first launched” and that their first-gen AI SDR got very low response rates(4). This led to some customer churn and a realization that an AI agent must be tightly controlled to be effective in sales. Over the past year, Artisan says it greatly improved Ava’s quality by working closely with their AI model provider (Anthropic) and implementing stricter prompts. According to the CEO, Ava now “hallucinates maybe 1 in 10,000 emails, if that”(4), thanks to feeding the AI more structured templates and info to prevent making things up. In other words, they moved toward a semi-controlled generation approach: companies fill out a detailed form about their product and value props, and Ava uses those inputs plus rigid prompt frameworks to generate outreach that stays on-message(4). This dramatically reduced errors and made the AI’s emails more credible.
Even with improvements, Artisan’s approach is still more “co-pilot” than fully hands-off agent. Industry reviews and Artisan’s own statements suggest Ava automates roughly 80% of outbound tasks, leaving the remaining 20% to humans(2). For example, the AI might draft the emails and cue up the sequence, but a human might need to approve messaging for niche cases or step in to handle complex replies. New customers also face a learning curve – onboarding Ava can take a couple of weeks of tweaking and training to get the messaging right(2). Essentially, Artisan’s AI needs guidance to reach its potential; it’s not a “set and forget” system out-of-the-box. This is a contrast to a platform like Landbase which emphasizes fully autonomous operation. Artisan is more akin to an “AI assistant” that still benefits from human direction.
Despite these caveats, many companies have found value with Artisan Ava. It excels for teams that don’t have the bandwidth to send personalized outreach at scale. Ava can sift through a 300M+ contact database (Artisan provides a large leads database with the product(2)), pick targets that fit your ICP, and send them custom-crafted emails as if written by a human rep. One notable stat: after iterations, Artisan now counts 250 companies as customers and surpassed $5M in ARR, reflecting that users are seeing enough ROI to pay for it(4). The platform’s pricing typically includes a base fee plus contacts usage, with flexible contracts – interestingly, Artisan even started offering “success-based pricing” via a partnership with Manny Medina’s new venture (Paid.ai), where customers can opt to pay per qualified lead rather than a flat fee(4). This model aligns incentives and suggests confidence in the AI’s ability to deliver outcomes.
From a competitive standpoint, Artisan often comes up in evaluations alongside Landbase. A key difference is that Landbase built its own GTM-specific AI model and multi-agent system, whereas Artisan relies on a single GPT-4 based agent (Ava) wrapped in an outbound tool interface(2). Artisan’s approach may be quicker to market but can be less robust – it doesn’t inherently “understand” sales; it must be coached. That said, Artisan has clearly resonated with many startups and mid-sized businesses that want to rapidly increase outbound activity. Some customers likely use Artisan as a force multiplier for their SDR team: letting Ava send those extra 1,000 emails per week that the humans never could, and then having humans refine or follow up as needed. Even if results are, say, a 1% reply rate (the CEO mentioned ~1% response is often the sweet spot(4)), at high volume that can translate to a steady flow of meetings.
In summary, Artisan and Ava demonstrate both the promise and pitfalls of first-generation AI SDRs. The company’s quick growth to 250+ customers shows that automated prospecting is in demand, and with improvements, an AI like Ava can perform much of an SDR’s job – Artisan’s clients have collectively generated millions in pipeline. However, achieving those outcomes requires effort in setup and suitable expectations (the AI isn’t magic; it’s a powerful helper that still benefits from human strategy input). Going forward, Artisan’s expansion into inbound (Aaron) and meetings (Aria) by late 2025 will be worth watching, as it moves toward a multi-agent vision. For now, Ava remains one of the leading AI agents for outbound sales, especially for companies willing to guide the AI and integrate it into their workflow. It’s a prime example of how far AI for sales has come – and how a mix of human oversight and automation can yield big wins.
In the world of sales enablement, Clay has made a name as a powerful data enrichment and research platform – one that increasingly leverages AI to personalize outreach at scale. While Clay isn’t an autonomous “agentic AI” platform in the way Landbase or 11x are, it plays a critical supporting role: providing the data intelligence and workflow automation that can feed into AI-driven GTM efforts. Think of Clay as the brainy assistant that finds and preps all the insights your sales agents (human or AI) need to craft ultra-personalized messages. Its tagline sums it up: “Go to market with unique data — and the ability to act on it.”
At its core, Clay is a no-code tool for automating the tedious research behind prospecting. Users build spreadsheets or workflows that automatically pull in dozens of data points per contact – from basic firmographics to real-time triggers like recent funding, new job postings, tech stack changes, social media updates, and more. Clay integrates with many APIs and databases to enrich records, and it even has an AI component called “ClayGPT” or Claygent that can analyze and summarize findings (for example, scanning a prospect’s website or LinkedIn and generating a custom intro line for an email). In effect, Clay helps sales teams (or AI agents) answer the question: “Why am I reaching out to this person, and what do I say that’s relevant to them?” – at scale, without manual research.
Key features of Clay include a library of pre-built enrichment “recipes,” a drag-and-drop workflow builder, and the ability to ingest data from CRMs or CSVs then spit out enriched lead lists ready for outreach. For example, a user could feed Clay a list of company domains, and have it append each company’s employee count, industry, latest news headlines, and the LinkedIn profile of a likely buyer. The output might then be used to generate personalized email snippets (“Congrats on the recent expansion to 200 employees!”). Clay can also trigger actions – e.g. automatically push enriched leads into an email sequence tool or alert an SDR when a high-priority intent signal is detected.
While Clay itself does not send emails or autonomously engage leads, it’s often used in tandem with outreach platforms. By ensuring every contact is enriched with timely intel, Clay dramatically boosts the effectiveness of campaigns. In fact, personalization is proven to lift sales metrics: one study found personalized emails deliver 6× higher transaction rates than generic ones(10). Clay enables that level of personalization at scale, bridging the gap between raw data and meaningful outreach. Sales reps often spend up to 40% of their time searching for someone to call or researching prospects(8) – Clay slashes this time by automating the search. SDR teams using Clay have reported huge productivity gains, since reps can focus on engaging warm leads rather than doing internet research on cold ones.
Clay’s approach represents a more modular entry into AI for GTM. Rather than a monolithic AI agent, it provides AI-driven components (like data enrichment and content suggestions) that slot into your existing process. For some organizations, especially those not ready to hand the keys entirely to an AI, this is an attractive middle ground. You maintain control over messaging and targeting, but let Clay do the heavy lifting of gathering intel and even drafting first passes at personalized copy. It’s worth noting that many fully agentic platforms also emphasize data – Landbase, for instance, has a huge contact database and intent signals. But companies that already have an outreach system can use Clay to level-up their current stack without replacing everything.
In terms of market positioning, Clay competes with data providers like ZoomInfo, Clearbit, and Apollo on one side, and with sales engagement tools adding AI features on the other. Clay’s differentiator is its flexibility and breadth of enrichment. It’s like a Swiss Army knife that can connect to almost anything – users have called it “if Airtable and Zapier had a baby for sales ops.” This flexibility helped Clay gain adoption among growth hackers and RevOps folks who love building custom workflows. It also attracted investors; Clay went through Y Combinator and has raised venture funding (though not as high-profile as some others, it’s solidly backed).
A possible limitation is that Clay by itself doesn’t guarantee meetings or revenue – it depends on how you use the enriched data. Some critics might say Clay is more an enabler than a solution. However, in the context of AI GTM agents, Clay can be seen as fuel for the engine. An autonomous agent is only as good as the information it has. By supplying rich, up-to-date context on each prospect, Clay can make any AI (or human) outreach significantly more potent. For example, if an AI agent knows that Prospect A’s company just raised Series B funding last week, it can tailor its pitch accordingly (“Congrats on the funding! Many companies at your stage use our solution to accelerate growth…”). That relevancy can dramatically improve response rates – indeed, highly targeted, personalized campaigns have been shown to boost reply rates by over 2X compared to generic blasts(9).
In summary, Clay is a leading tool for AI-driven data enrichment and workflow automation in sales. It’s not a standalone “AI SDR” but rather a critical part of the AI sales tech ecosystem. Companies use Clay to ensure their go-to-market motions are driven by unique, contextual data on every lead – which in turn makes the outreach from platforms like Outreach, Salesloft, or even agentic AIs much more effective. If your sales team struggles with too much time spent on research or suffers from low email reply rates due to generic messaging, Clay can be a game-changer. By automating the grunt work of prospect research and letting AI surface the golden nuggets for personalization, Clay empowers human reps and AI agents alike to focus on what actually moves the needle: relevant, timely engagement with the right prospects.
While newer on the scene, Unify is carving out a niche with its approach to “signal-driven” go-to-market. Unify’s platform transforms GTM execution into more of a science experiment: it captures diverse buyer intent signals, and uses AI agents to act on those signals in a coordinated way. The value proposition is in the name – “Unify” aims to unify your data, workflows, and AI automation in one system of action. For companies drowning in sales tools and data exhaust, Unify promises a way to operationalize that data through intelligent triggers and agents.
A hallmark of Unify is its emphasis on real-time intent signals. The platform can ingest signals like web traffic surges, engagement with marketing content, intent data (e.g. topics a target account is researching), CRM events, etc., and then automatically launch plays via its AI agents when certain criteria are met. For example, if a target account visits your pricing page multiple times (a strong buying intent signal), Unify might have an agent automatically send a personalized email to that account’s champion, referencing the specific interest. Or if a competitor is mentioned in the news for something relevant, an agent could auto-generate talking points and task an SDR to reach out that day.
Under the hood, Unify has a multi-component architecture: “Signals,” “Plays,” “Sequences,” and “AI Agents” are key modules. Signals are the data inputs (dozens of built-in person and company-level signals, from technographic to news to intent feeds). Plays are like recipes or workflows that orchestrate actions when triggers occur (e.g., if signal X AND persona Y, then do Z). Sequences manage outbound touchpoints (similar to email cadence tools). And AI Agents in Unify are programmable assistants that can perform tasks like researching a lead, writing a first draft email, or scoring a contact based on custom prompts. Users can write natural language instructions for these agents to carry out on records, making Unify quite flexible.
One way to view Unify is as a blend of RevOps automation and AI augmentation. It’s not just one AI doing everything; rather it lets you deploy mini-AI agents at specific junctures of your GTM process. For instance, an Unify agent could be set to “Monitor news for my target accounts and alert/write a blurb if something important happens.” Another could be “Analyze new inbound leads and score their fit + urgency using AI, then route accordingly.” This granular, modular use of AI is powerful for operations teams that want fine control. It also means Unify can coexist with humans in the loop: the AI might do preliminary research and draft an email, but a human SDR can review and send.
In practice, companies using Unify have reported that it systematizes their outbound and makes it more responsive. Instead of batch-and-blast campaigns, outreach becomes event-driven and personalized based on timely data. This can yield better results – sales studies show that responding to buyer signals quickly and with relevant messaging greatly improves conversion. (Case in point: contacting a prospect within an hour of a website visit can make them 7× more likely to qualify, per some industry research.) Unify essentially ensures no high-intent signal goes unnoticed or unloved.
One stat highlighting the importance of this approach: 42% of salespeople say prospecting for new leads is the hardest part of their job(8). Why? Often it’s because they don’t know who to reach out to or when. Unify’s signal-driven model addresses that by telling reps (or triggering AI) to reach out at the moments that matter. By automating the “when and why” of outreach, it takes a huge load off sales teams. Reps no longer have to blindly guess which leads to call today – the system surfaces those who show intent or fit certain criteria, and even gives an AI-researched angle for the conversation.
As a company, Unify is fairly new (founded mid-2020s) and not as widely publicized as some competitors, so public performance metrics are scarce. But they boast “hundreds of innovative growth teams” as users on their site. Given the focus, it’s likely popular with data-driven startups and revenue operations folks who want to orchestrate complex multi-step plays. One can think of Unify as a glue platform that ties together data and action: it could replace a tangle of tools like separate intent data feeds, sales engagement platforms, task automation tools, etc., with one cohesive system.
A limitation to note is that Unify currently seems to be a single-agent framework (you can create multiple agents for tasks, but it’s not clear if they collaborate autonomously the way Landbase’s do). It may also lean on trigger-based automation more than true long-horizon reasoning; in other words, it’s incredibly useful for predefined scenarios (“if X happens, do Y”), but less of a self-directed strategist. That said, for many companies, getting all the “low-hanging fruit” of signal-based outreach is a huge win. Unify basically ensures you capitalize on intent signals that might otherwise slip through cracks.
In summary, Unify is a strong entrant for those looking to integrate AI agents into their GTM in a controlled, data-driven way. It’s like adding an AI-enhanced brain to your sales machine that watches for opportunities and kicks off the right plays at the right time. Organizations that have invested in gathering buyer intent data or have complex outbound programs will find Unify helpful to actually use that data in practice. It brings a scientific rigor to growth – hence their mantra of “transform growth into a science.” While it may not yet have splashy case studies publicly available, the approach is grounded in what we know works: timely, personalized outreach triggered by buyer behavior. As GTM teams increasingly adopt AI, platforms like Unify show how AI agents can operate in tandem with signals and systems to drive better outcomes.
When it comes to enterprise sales and service, Salesforce’s entry into agentic AI – Agentforce – looms large. Announced in late 2024, Salesforce Agentforce is a suite of out-of-the-box autonomous AI agents built into the Salesforce platform, powered by the sophisticated Atlas reasoning engine. In essence, Salesforce is offering its customers pre-trained AI workers that can handle specialized tasks across sales, customer support, marketing, and more, all natively integrated with Salesforce data. For example, Agentforce includes agents that can automatically follow up on sales leads, triage customer support cases, generate reports, and perform routine CRM updates, operating proactively without being prompted each time.
One of the biggest advantages Salesforce brings is trust and integration. These AI agents can connect deeply into a company’s Salesforce CRM, meaning they have full access to customer records, past interactions, open opportunities, etc. They can take actions across those systems just like a human user would – but much faster and tirelessly. The Atlas reasoning engine behind Agentforce is designed for “system 2” style logical reasoning, not just chat responses. This allows it to deliberate on tasks, make multi-step plans, and even hand off to humans when needed (Agentforce will intelligently escalate to a human agent if it encounters something it can’t handle).
Salesforce has reported impressive early results from Agentforce deployments. According to the company, organizations piloting Agentforce have seen on average 40% faster case resolution times in customer support and 25% higher lead conversion rates in sales(7). For instance, the publisher Wiley found that Agentforce’s support agent outperformed their old chatbot by resolving cases 40% faster. In sales, Agentforce’s lead follow-up agent can identify hot leads and engage them instantly, leading to significantly improved conversion of inquiries to opportunities. These metrics highlight that Agentforce isn’t just hype – it’s delivering tangible performance improvements in core revenue operations. Additionally, research by Futurum found companies using Agentforce achieve ROI up to 5× faster and with 20% lower TCO compared to DIY AI approaches(7). Salesforce’s deep integration and ready-made agents accelerate time-to-value.
What makes Agentforce particularly powerful is how customizable and extensible it is for enterprises. Salesforce launched a marketplace called AgentExchange with hundreds of partner-built agent templates and actions that companies can plug in(7). They also offer a Developer Edition and tools for admins to configure their own agents without code(7). This means a company can tailor the AI agents to their specific workflows – whether that’s an agent to automate quote generation in Salesforce CPQ, or an agent to monitor Slack for customer mentions and create cases. The architecture of Agentforce is modular: think of it as a platform for building a team of AI coworkers, with Salesforce providing the core intelligence (Atlas) and guardrails like security, auditing, and compliance (critical for trust in enterprise settings).
Early adopters span various departments. For example, in customer support, Agentforce can handle a large chunk of repetitive Tier-1 queries autonomously, only escalating the tricky ones. OpenTable’s customer support saw Agentforce handle 73% of all restaurant web inquiries within 3 weeks of launch – a 50% improvement over their previous tool(7. In sales, Adecco Group used Agentforce to automate engaging millions of job candidates, freeing recruiters to focus on personal interactions(7). And in marketing or commerce, agents can personalize content or offers in real-time for customers. Essentially, any repetitive workflow that spans Salesforce apps is a candidate for Agentforce automation.
From a competitive standpoint, Salesforce Agentforce is clearly targeted at large organizations and as an answer to the burgeoning market of point-solution AI startups. Why buy a separate AI sales engagement tool if Salesforce can bake similar (or better integrated) capabilities right into your CRM? One challenge for smaller AI players is that Salesforce can leverage its huge installed base – Agentforce can be rolled out to thousands of existing Salesforce customers relatively easily, which is a distribution advantage. However, Salesforce’s solution may not have the specialized focus or agility of startups for certain tasks. For example, Landbase’s GTM-1 Omni is specifically tuned for outbound prospecting content quality, whereas Salesforce’s sales agent might initially be more basic in copywriting finesse. Over time though, Salesforce will iterate and improve using the vast data it has.
One notable design point: Salesforce emphasizes a “human + AI” synergy rather than total replacement. They often stress how Agentforce augments employees rather than eliminates them, which is a reassuring message for enterprises. It’s baked into the product too – the ability to seamlessly transfer an AI session to a human agent in Salesforce console, for instance, is built-in. This reflects a likely reality: in complex enterprise sales, you still need human judgment, but AI agents can handle the grunt work and surface insights, thus making humans far more efficient.
In summary, Salesforce Agentforce brings credible, enterprise-ready AI agents to the mainstream. It validates the agentic AI category in a big way. Companies that have hesitated to try smaller vendors might jump in with Salesforce’s offering since it’s backed by Salesforce’s security and ties into their single source of truth (CRM). The results reported – 40% faster support resolution, 25% more leads converted(7) – show the promise of agentic AI in large-scale environments. As Agentforce continues to roll out (its full GA is slated for 2025, with incremental features being released(7)), we can expect it to raise the bar for what AI in CRM can do.
For any business already on Salesforce, Agentforce is definitely worth evaluating: it could automate a good chunk of sales ops, admin updates, and initial customer touches that currently eat up your team’s time. And for those in industries like finance or healthcare where data security and compliance are paramount, Salesforce’s built-in trust layers might be a deciding factor. The age of enterprise AI agents is here, and Salesforce is leading that charge at the high end of the market.
In the context of AI for go-to-market, it’s worth discussing Copy.ai – a popular AI writing assistant that, while not an autonomous GTM orchestrator, is often used by sales and marketing teams for content creation. Copy.ai leverages large language models (like GPT-3/4) to generate marketing copy, emails, ad text, blog posts, and more. Many SDRs and marketers turn to tools like Copy.ai to help write outreach emails or LinkedIn messages faster. However, Copy.ai is focused on content generation, not multi-step campaign execution. It doesn’t have “agents” that autonomously prospect or sequence messages; rather, it’s an AI copywriter that still relies on a human to decide when and to whom to send that copy.
So why include Copy.ai in a discussion of GTM agents? For one, it represents the baseline AI capability that many teams started with: using AI to write better emails. And indeed, good messaging is crucial – personalized emails can dramatically improve engagement (Experian data shows personalized emails deliver 6× higher transaction rates than non-personalized ones(10)). Copy.ai and similar tools (e.g. Jasper, ChatGPT itself) can help craft those personalized snippets at scale. Sales reps might use Copy.ai to generate 10 variations of a cold email tailored to different industries, for example. This can save time and perhaps improve quality.
However, Copy.ai lacks the agentic qualities that define the other platforms in this blog. It won’t decide which leads to contact, it won’t send follow-ups automatically, and it won’t integrate with your CRM to log activities. Essentially, it addresses one slice of the GTM process: content. Users still have to handle the strategy and execution manually or with other software. In the competitor landscape, Copy.ai is often seen as a complementary tool rather than a direct competitor to a Landbase or 6sense. In the “AI SDR” competitor table we referenced, Copy.ai was noted as having “content focus, no multi-agent approach, content-only” and no end-to-end orchestration. This underscores that if a team tried to cobble together a GTM solution just with Copy.ai, they’d still need many other pieces (data source, email automation, sequencing, etc., plus human oversight at every step).
That said, some companies initially attempt a DIY approach: e.g., “we’ll use Copy.ai to write emails and Mailchimp to send them” rather than invest in a specialized AI sales platform. This can work to a point, but often falls short in results. Why? Because the magic is in the integration and continuous learning. A true GTM agent like GTM-1 Omni not only writes the email, but also decides who to send it to, when to send, observes the response, and refines the next email – all autonomously. Copy.ai would require a human to do each of those steps around the content generation. It’s powerful for what it is, but it’s not plug-and-play pipeline generation.
One common pain point with using standalone AI writing tools in sales is maintaining quality and context. A tool like Copy.ai might generate a decent generic email, but if it’s not fed the right context, it can also produce irrelevant or awkward content. Early adopters of generic GPT for sales often ran into issues with “overly fluffy” or off-base messaging that didn’t hit the mark. Ensuring the AI has up-to-date info on the prospect and the proper tone requires either careful prompt engineering or additional data – which robust agentic platforms handle behind the scenes. For instance, Landbase’s AI has knowledge of what a successful cadence looks like because it was trained on millions of B2B interactions; Copy.ai just has general writing ability. The difference shows up in subtle ways, like how an email is structured or how the call-to-action is phrased.
From a results perspective, using a tool like Copy.ai might help increase an individual rep’s output (they can craft more emails faster). But it may not yield the dramatic conversion lift that a fully integrated system does. If a rep sends more emails but still to a mediocre lead list with mediocre timing, the lift in meetings booked might be minimal. In fact, the world is already seeing AI-generated content saturation – if everyone starts blasting AI-written emails, the noise increases. This is why the targeting and strategy component, often driven by agentic AI, is so crucial.
In fairness, Copy.ai and its peers are evolving. Some are adding features like suggested contact personas, or workflow integrations. Copy.ai could potentially integrate with CRMs to personalize using fields, etc. But the gap remains: it’s not “closing the loop.” It won’t observe that Prospect X never opened the email and then automatically try a different approach the next week. That’s what an agent would do.
To sum up, Copy.ai represents the content generation subset of AI GTM tools. It’s highly useful for marketing teams writing blogs or ad copy, and for sales reps drafting outreach, but it should be viewed as one piece of a larger puzzle. Many organizations find that after experimenting with AI writing tools, they graduate to more comprehensive solutions that handle data, sending, and learning – essentially graduating from AI as an assistant to AI as an autonomous agent.
For those building their toolkit, Copy.ai can be a great starting point (especially given its low cost and ease – just input a prompt and get copy). Just be aware of its limitations. It might create a catchy intro line or a polite follow-up email, but you need to ensure it’s sent to the right person at the right time with the right follow-through. And if your goal is to truly “set it and scale it,” a dedicated agentic platform will likely drive better ROI.
Another notable entrant in the AI agents arena is Lyzr AI, which markets itself as a low-code platform to build and deploy intelligent AI agent workflows. Lyzr takes a slightly different tack than out-of-the-box solutions: it provides an “agent studio” where companies can configure their own custom AI agents to automate various business processes (from sales to HR to support). For GTM specifically, Lyzr offers a Sales Agents Hub with a collection of pre-built AI agents aimed at different parts of the sales cycle. In fact, Lyzr touts “40+ pre-built AI agents” for sales and other functions, which can be used as-is or modified to fit your needs.
Some examples of what Lyzr’s sales agents can do: lead generation (finding and qualifying new leads), CRM upkeep (automating data entry and updates), pipeline management (notifying reps of pipeline changes or forecasting), meeting scheduling, follow-up nudges, and more. Essentially, Lyzr provides a toolkit and templates – if you want an agent that, say, scans your CRM for stale opportunities and sends a re-engagement email to each, you could build or select one in Lyzr. The platform emphasizes being enterprise-ready and secure, and agents built in Lyzr can integrate via API into existing systems, or even be published for others to use via a marketplace.
For companies with strong technical teams or unique workflows, Lyzr’s approach is appealing because of its flexibility. Rather than a black-box AI that does everything its way, Lyzr lets you craft AI automations that align with your specific processes. It’s akin to an AI agent builder platform. Gartner and others have predicted that such “DIY” multi-agent systems will grow, as businesses seek to customize AI to their context.
In terms of performance, because Lyzr’s value is in custom solutions, the metrics will vary by use-case. We might not have broad ROI stats like “X% conversion uplift” generically. However, Lyzr’s site and materials indicate some big efficiency gains. For instance, one Lyzr customer was able to automate 80% of their sales ops tasks by deploying a set of agents to handle data syncing, meeting scheduling, and follow-ups. Others mention cutting down response times from hours to seconds for certain customer inquiries by using Lyzr agents in their chat or email support. Lyzr also highlights cost reductions – by automating a lot of manual work, companies save on labor or can refocus their team on high-value activities.
Anecdotally, a user on Reddit’s r/aiagents shared that “Lyzr let us build an AI agent to actually run our lead qualification end-to-end – it finds leads, scores them, emails the ones meeting criteria, and books meetings on our calendar”. If that’s indicative, it means some are achieving autonomous GTM flows via Lyzr, but specifically tailored to them.
One challenge with Lyzr’s model might be the build effort. Not every company has the desire or skillset to design AI agent workflows from scratch. This is why Lyzr provides those 40+ pre-built agents – to jumpstart usage. If a pre-built fits well (e.g., “AI SDR Agent” template), then great. If not, some customization or building is needed. Lyzr addresses this by claiming you can configure agents in minutes with no coding, using its visual interface (even creating an AI agent in under 60 seconds in demos). The reality likely depends on complexity; simple tasks are easy, complex multi-step logic might take longer.
Comparing Lyzr to a Landbase or Salesforce: Lyzr is more horizontal. It’s not exclusively for sales – it could be used to make an AI HR assistant, etc. This broad capability is powerful but also means it may not have as much out-of-the-box sales expertise as a purpose-built sales AI. It’s providing the canvas and brushes, but you paint the picture. For organizations with strong RevOps or dev teams, this is fine. For small companies that just want results, a turnkey solution might be preferable.
However, as the concept of agentic AI grows, Lyzr stands as one of the platforms enabling the creation of “your own” AI agents. This might become a bigger trend: internal AI agents tailored exactly to one’s business. If that happens, Lyzr’s approach will gain steam. They already partner with cloud providers (it’s available on AWS Marketplace, indicating a level of maturity).
In summary, Lyzr AI is a leading example of the agent builder category – allowing businesses to deploy numerous specialized AI agents across their operations. For GTM teams, it means you’re not limited to one AI SDR; you could have a whole fleet of mini-AI tools each doing a part of the process, all managed in one platform. The company’s selling point of “no-code, enterprise-ready multi-agent automation” appeals to teams that want flexibility and control alongside AI power. While it might require more initial configuration than a plug-and-play AI SDR, the payoff is a highly customized AI workforce.
As evidence of its momentum, Lyzr’s customers have automated myriad tasks and often report faster cycle times and reduced workload. It wouldn’t be surprising if a Lyzr client said: “We deployed 10 AI agents and each replaced a specific workflow – together they save us hundreds of hours per quarter and ensure nothing slips through cracks.” In a world where sales teams use on average 10 different tools already(15), consolidating some functions into Lyzr agents can simplify the stack (e.g., an agent could replace a point solution for meeting scheduling, etc.).
For organizations willing to invest a bit in configuration, Lyzr offers a very powerful proposition: your own personalizable AI army. It’s a different flavor than a turnkey system, but one that’s likely to grow in importance as AI adoption matures.
Apollo.io is well-known to many B2B sales teams as a leading sales intelligence and engagement platform. It provides a massive database of contacts and companies, along with tools for prospecting and outbound sequences. Recently, Apollo has infused more AI features (like AI-powered search, email writing suggestions, and intent signals) into its product. However, Apollo is not a true “agentic AI” system – it’s best thought of as a rich data platform with some automation, as opposed to an autonomous agent that orchestrates GTM campaigns.
Where Apollo shines is data. It boasts one of the largest B2B contact databases in the market – over 275 million contacts and 73 million companies as of 2025(11) – and is known for continuous data updates and accuracy. This scale rivals (even exceeds) the data access of some dedicated AI platforms. For example, Landbase has ~220M contacts built-in; Apollo offers 275M+. So, many teams use Apollo as the source of truth for finding prospects (who to contact, their email, etc.). Apollo also has engagement tools: you can set up email sequences, call tasks, LinkedIn tasks, and it will execute them in a semi-automated way (like Outreach or Salesloft style). These sequences can be enhanced with Apollo’s “Magic” AI email writer, which helps generate email copy for each step.
However, Apollo’s automation still relies heavily on user direction. A human defines the sequence steps, selects the contacts or sets criteria, and Apollo will send out emails accordingly. The AI assists in writing or prioritizing leads but doesn’t independently run the whole process. There is no multi-agent collaboration or strategy layer deciding which campaign to run when – that’s up to the user. In the competitor comparison, Apollo was noted as “automation but limited agency – not full GTM”. In other words, Apollo can automate parts of outreach (send emails at set intervals, etc.), but it’s not going to plan a campaign from scratch or adapt on its own beyond basic triggers.
Apollo’s recent introduction of an “Apollo AI” feature set (announced mid-2023) does add some intelligence. For instance, Apollo AI can surface recommended leads based on your best customers, or suggest when to contact someone based on intent signals. They also launched a chatbot-like assistant to query data. These certainly help streamline go-to-market workflows, but again, they’re more assistive than agentic. It’s enhancing the user’s abilities rather than replacing the need for the user’s decisions.
One area Apollo has dived into is intent data and predictive analytics – for example, identifying which companies are in a buying cycle through monitoring web traffic or content consumption. This edges a bit into 6sense territory (account-based predictive). Apollo acquired a company called Magic.ai (not to be confused with their email writer name) to bolster AI capabilities in 2023. With such moves, Apollo aims to be an all-in-one platform for sales teams: data, engagement, and intelligence in one. Indeed, Apollo has seen tremendous growth – they reported 500% user growth over a year, reaching over 1 million users on the platform(12). Many SMBs find Apollo a cost-effective alternative to tools like ZoomInfo + Outreach combined.
However, for all its strengths, Apollo is not a “set it and forget it” autonomous system. A lot of Apollo’s value still depends on the user actively working the system – pulling lists, crafting sequences, and iterating. If Landbase is an autopilot car, Apollo is more like a GPS-equipped car: it gives you great info and some cruise control, but you’re still driving.
That said, Apollo can pair nicely with an agentic approach. For example, some companies use Apollo’s data inside an AI agent platform (via API or CSV export). Apollo’s rich contact info and firmographics can feed an AI model’s personalization. Conversely, Apollo’s engagement analytics (opens, replies) could be data that an AI agent learns from if integrated.
It’s also fair to acknowledge that Apollo solves key problems around data quality and deliverability. Even the smartest AI agent flounders if it doesn’t have quality contacts or if emails bounce. Apollo’s email sending infrastructure (they have features for domain warm-up, etc.) and verification services ensure campaigns reach inboxes. So, one could argue Apollo addresses the foundational layer (data + basic automation), while agentic AI addresses the orchestration and decision layer on top.
In summary, Apollo.io remains a top tool in the sales tech stack for prospect data and outreach, now enhanced with some AI assistance. It’s a bit of a stretch to call it an “AI GTM agent” competitor, since it doesn’t autonomously drive campaigns, but it competes in budgets – a team might weigh “Do we invest in Apollo and have our SDRs work it, or buy a fully autonomous platform like X?” For teams that prefer human-led processes but want the best data and some AI help, Apollo is often the choice. It’s also relatively affordable and friendly to SMBs (with freemium models to try out, etc.), whereas some advanced AI platforms target higher price points.
Apollo’s impact is evident in how widespread it is – over 1 million users using it to find prospects and send sequences(12). That means a lot of outbound sales is already happening through Apollo. As the company continues adding AI features, it may slowly transition from a manual tool to a smarter co-pilot. But for now, its role in our discussion is as the data-rich enabler: a platform that gives you the who and helps with the how, but you still steer the strategy.
Rounding out our look at leading AI-powered GTM solutions is 6sense, a pioneer in using AI for account-based marketing (ABM) and predictive sales intelligence. 6sense isn’t an autonomous agent that executes outreach; instead, it’s an AI-driven analytics and orchestration platform that helps sales and marketing teams focus on the right accounts at the right time. In the realm of GTM agents, you can think of 6sense as the all-seeing oracle – it analyzes vast data to predict which prospects are “in market” and what they care about, so your human or AI sales efforts can be targeted with precision.
At the core of 6sense is its intent data network and predictive models. It tracks behavioral signals from target accounts (website visits, research on third-party sites, ad engagement, content downloads, etc.) and uses AI to infer where each account is in their buying journey. The platform assigns accounts to buying stages and even identifies anonymous buying team members by resolving their visits to known accounts. The outcome is a prioritized list of accounts and insights into their interests. For example, 6sense might tell you that ACME Corp is showing surging interest in “network security solutions” based on web searches and content consumption, and is in a late consideration stage. That’s a huge cue for sales to reach out with a tailored message about network security.
What makes 6sense powerful for GTM is that it dramatically improves focus and timing. Studies show that sales teams waste a lot of effort on accounts that aren’t likely to buy soon. 6sense flips that – customers often report things like 10x improvement in opportunity generation when using 6sense insights to guide outreach(13). In one analysis, segments derived from 6sense’s AI showed a 13x higher conversion to pipeline than generic, non-AI targeted segments(13). And when it came to deals closed, the 6sense-targeted audience had a whopping 19x higher closed-won conversion rate compared to cold groups(13). Those numbers, from a Trendemon study, illustrate just how effective it can be to aim your sales/marketing at accounts the AI deems most likely to convert. Essentially, 6sense gives your GTM efforts a laser focus and a head start.
6sense’s capabilities also include orchestrating actions: it can trigger ads to key accounts, personalize website content for them, or recommend which contacts sales should call first. However, these actions are typically executed through integrations or suggestions to humans, not by 6sense sending emails itself. So again, 6sense is augmentative – it’s feeding the playbook, not running all the plays autonomously.
In practice, a company using 6sense might do something like: import 6sense’s prioritized account list into Outreach (or Salesforce, etc.), then have either reps or an AI sales tool execute outreach to those accounts with messaging informed by 6sense’s insights. Customers frequently rave that 6sense helps sales and marketing align – it provides a common view of where the best opportunities lie, so marketing can focus campaigns on those accounts and sales can concentrate their time efficiently. One stat from 6sense is that it helped companies achieve 2X higher deal win-rates and 40% faster deal cycles by focusing on in-market buyers (as per their case studies with customers like Snowflake, etc.). It essentially operationalizes the ABM principle: spend your energy where there’s intent.
From a market standpoint, 6sense is a leader among ABM platforms (valued at over $5 billion in 2022, with hundreds of enterprise customers). It has acquired other AI firms (e.g., Saleswhale, an AI email chatbot for inbound lead follow-up, in 2022) to broaden its suite – which indicates even 6sense saw the need to add some “AI agent” like execution via Saleswhale’s tech. Still, 6sense’s core is insight, not execution.
For a company considering GTM investments, 6sense often comes up as complementary to direct sales execution tools. If Landbase is the autonomous sales engine, 6sense is the radar system telling it where to drive. In fact, some forward-thinking orgs use both: 6sense to identify high-intent accounts, and then Landbase or Outreach to actually hit those accounts with sequences. Where 6sense might appear to compete is with the intent data features of platforms like Apollo or ZoomInfo’s Intent – but 6sense is generally regarded as having a more advanced AI and a more complete ABM approach (beyond just raw data, it provides a workflow and analytics).
In summary, 6sense augments GTM teams with predictive intelligence and intent-driven focus. It’s not an autonomous outreach agent, but it significantly enhances the effectiveness of both humans and AI agents by pointing them to the right targets and arming them with insights on what messaging will resonate. Organizations that integrate 6sense report major improvements in pipeline quality – one case study showed a marketer using 6sense + personalization tool saw a 4x boost in target account conversions(13). Those kinds of results demonstrate the multiplier effect of AI insight: you might be doing the same amount of work, but getting far more return because you aimed it wisely.
As we consider the panorama of AI in GTM, 6sense represents the analytical brain that can guide it. In the hands of a capable team (or coupled with an execution platform), it can revolutionize how you allocate sales and marketing resources, ensuring you’re always engaging the accounts most likely to drive revenue.
Empler AI is an emerging platform that also targets the vision of agentic AI for go-to-market, with an emphasis on a no-code, multi-agent framework. Think of Empler as providing a canvas to design AI-driven GTM workflows by dragging and dropping pre-built “agent” components. While smaller and less proven than some others we’ve discussed, Empler represents the burgeoning cohort of startups trying to democratize AI agent creation for sales and marketing teams.
Empler’s pitch is to enable end-to-end automation of GTM tasks – from prospect research and list building to outreach execution and follow-up – without a human in the loop, but with the user in control of the playbook. You could, for instance, set up an Empler workflow where an AI agent scours LinkedIn for a certain persona, pulls those contacts into a list, another agent writes personalized emails to them, another agent handles responses or booking meetings, etc. All of this is configurable via a visual interface, rather than coding. Empler integrates with common tools (CRM, email, web scrapers, Slack, etc.), acting as the glue and AI logic layer to connect them in an “autopilot” sequence.
One of Empler’s focus points is making the AI behavior transparent and controllable. Users can set rules or criteria for the agents (for example, only email leads that meet XYZ criteria, or pause if reply received). There’s also collaborative workflows where multiple agents can pass tasks among themselves – akin to an assembly line of AI workers each doing their part. In marketing speak, Empler highlights “agentic automation with guardrails,” acknowledging companies want automation but also oversight to ensure quality and compliance.
Since Empler is relatively new, we don’t have solid third-party metrics on its effectiveness. On their site, they cite aspirational outcomes like faster sales cycles and higher qualified lead volumes, but specific numbers or case studies aren’t widely published yet. A clue from their messaging: they mention aiming for “10x pipeline” for customers via agentic GTM (likely a forward-looking statement). At minimum, Empler promises to replace many manual steps and disparate tools with a single coordinated system – which, if achieved, certainly can cut costs and improve consistency.
The strength of Empler lies in its adaptability. Because it’s no-code and multi-agent, a growth team can experiment with automating different pieces of their process, tweak logic, and even involve humans in certain steps if needed. It’s like building a custom AI assembly line for your GTM. For organizations that feel boxed in by off-the-shelf tools, Empler could be attractive. It caters to the tinkerers and the ops folks who say “we have a unique process, and we want an AI to conform to us, not vice-versa.”
On the flip side, as a younger platform, Empler likely faces the challenge of proving out results and reliability. Businesses will ask: can this really handle our critical sales operations autonomously without things breaking or going off the rails? Trust is earned over time. Empler is reportedly in use by some design partners and early adopters in SaaS, who are helping validate and refine its agents. One early user account said Empler’s system was able to automate a multi-touch campaign that saved their team ~20 hours a week, albeit requiring some initial setup and monitoring.
Another angle: Empler doesn’t come with a huge proprietary data lake (unlike, say, Apollo or Landbase). It relies on connecting to your data sources or public ones. So the quality of output is as good as the data you plug in. Companies need to ensure they have clean lists or integrate Empler with a tool like Clay or Apollo for enrichment, for example.
In competitive terms, Empler AI can be seen as a direct peer to tools like Lyzr or Unify – all focusing on letting users create AI-automated workflows. It emphasizes “agentic” capabilities, implying its agents have some level of decision-making (not just linear if-then flows). Empler’s vision aligns with the idea that in the next generation of sales tech, instead of hiring more headcount or buying another point solution, you might “spin up an AI agent” to take on the extra work.
While we await more concrete success stories from Empler, it’s worth including because it shows how vibrant the AI for GTM space is. Even outside the headline-grabbing big names, there are multiple startups pushing the envelope. This means innovation and options – and possibly a bit of overload for buyers to evaluate! The key for any new entrant like Empler will be to demonstrate clear, quantitative benefits.
For readers, the takeaway on Empler is: keep an eye on the rise of no-code AI agent platforms. If you have a lean team and aggressive goals, trying a tool like Empler in a sandbox could spark ideas to automate chunks of your process. Maybe you start with something low-risk (e.g., an agent to monitor inbound form fills and send a personalized intro email immediately). As trust builds, you hand over more tasks. Before long, you might have a self-driving GTM workflow pieced together that significantly scales your efforts.
The landscape of sales and marketing is changing rapidly. As we’ve explored, a variety of AI agents and platforms are now available to revolutionize go-to-market execution. From autonomous AI SDR teams that prospect and book meetings while you sleep, to predictive analytics that tell you exactly where to focus, these solutions share a common theme: enabling businesses to do more with less. Traditional GTM strategies – often shackled by fragmented tools, manual labor, and sluggish workflows – simply cannot keep pace in today’s hyper-competitive environment. Organizations that fail to adopt intelligent automation risk being out-hustled by those that do.
Agentic AI is no longer science fiction; it’s here and delivering results. Companies leveraging autonomous GTM agents are seeing striking benefits: 4–7x higher conversion rates and up to 70% lower costs versus old-school methods(1). Perhaps most importantly, what used to take months of ramp time and coordination can now be launched in a matter of minutes(1). Imagine compressing an entire outbound campaign setup – data sourcing, messaging, sequencing, optimization – into an afternoon’s work by an AI. That agility can be a game-changer, letting you respond to market opportunities or competitive pressures almost in real-time.
Among the innovators leading this transformation is Landbase with its GTM-1 Omni model – the world’s first agentic AI built specifically for go-to-market. Unlike basic automation or generic AI add-ons, Landbase doesn’t just assist with isolated tasks; it autonomously drives the entire GTM process. From prospect discovery and data enrichment to multi-channel outreach and continuous optimization, it operates as an extension of your team. Every interaction is analyzed and learned from, every message hyper-personalized at scale. Businesses using Landbase’s agentic AI have been able to launch campaigns on demand and scale pipeline faster than ever. Some report boosting conversion rates by 5x or more and adding hundreds of thousands in new revenue during periods that normally would be slow(2).
Crucially, these AI agents maintain a human-like quality in communication – thanks to training on massive datasets of successful sales conversations – so prospects feel genuinely engaged, not spammed by a bot. The heavy lifting of outreach, follow-ups, and pipeline nurturing is handled around the clock by tireless AI, freeing your human salespeople to focus on what they do best: building relationships and closing deals. It’s the ultimate force multiplier. One Landbase user described it as having “an SDR team that works 24/7 and gets smarter every day” – something simply impossible with purely human teams.
In today’s market, the ability to scale revenue without equally scaling headcount is a decisive advantage. AI GTM agents make that possible. They allow startups and lean teams to punch far above their weight, and let larger companies cover territory and personalize outreach at a depth that would require armies of staff to replicate. The playing field is being leveled by AI, and new opportunities are opening for those bold enough to leverage it.
Whether you’re a fast-growing SaaS startup building your first outbound engine, or an enterprise seeking to optimize a complex multi-touch sales cycle, embracing agentic AI can yield immediate and significant gains. Picture cutting your customer acquisition cost in half, or doubling your pipeline within a quarter, all while your team spends less time on grunt work and more time on high-value conversations. These aren’t pipe dreams – they’re reported outcomes by early adopters of GTM AI.
Now is the time to embrace the next evolution of go-to-market execution. The tools and platforms we discussed are maturing, and those who integrate them into their strategy will gain a formidable edge. Imagine automating the heavy lifting of prospecting and outreach, improving lead quality with AI insights, and generating pipeline on autopilot around the clock. Meanwhile, your competitors are still slogging through spreadsheets and manual email sends – it’s not hard to guess who will come out ahead.
If you’re ready to explore what agentic AI can do for your organization, consider starting with the leader in this space. Landbase offers a proven, comprehensive solution to move your GTM into the future. With its autonomous AI agents, massive data assets, and demonstrated ROI, Landbase empowers teams to go beyond traditional automation and truly transform their revenue operations. It’s not just about doing things faster – it’s about doing better, smarter, and at scale.
Don’t get left behind in the old way of doing sales. The future of GTM is here, and it’s powered by intelligent agents that work tirelessly on your behalf. Now is the moment to seize this advantage. Equip your team with the tools to automate the mundane, amplify the effective, and accelerate growth predictably. Those who do will not only see more pipeline and revenue – they’ll free their people to focus on creative, strategic work that drives even greater success.
Landbase is leading this charge with its agentic AI platform that delivers real results. If you want to see how autonomous GTM can revolutionize your sales process, we invite you to explore Landbase today. Take the first step toward a smarter, more autonomous go-to-market engine. Your next customers are out there – let AI help you find them, engage them, and win them faster than ever before.
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