With the rapid development of AI technology, the field of sales and marketing is undergoing an unprecedented transformation. The emergence of AI agents is revolutionizing the model and efficiency of traditional sales. This article will delve into how AI agents can disrupt traditional B2B sales processes through automation and intelligence, from customer research and personalized outreach to multi-channel marketing campaign execution, achieving significant improvements in sales efficiency.
Have you ever thought that sales may have changed completely? Traditional sales methods are moving from relying solely on human calls and emails to AI agents as the core force driving the entire sales process. This is not a small adjustment, but a complete paradigm shift. Think about it, the core concepts we’ve been accustomed to—cold calling, email templates, customer classification, and even the concept of “sales reps” — are being redefined by AI agent-driven workflows.
Recently, an AI sales startup called Landbase just closed a $30 million Series A funding round co-led by Sound Ventures and Picus Capital, and there is an interesting story behind this round. When founder Daniel Saks was preparing to raise funds, he invited 50 top investors for a roadshow, and it was finally a suggestion from Ashton Kutcher that allowed Sound Ventures to win the investment opportunity. Kutcher suggested that they change their marketing tagline from “Intelligently automate your market development” to “Find your next customer.” This seemingly simple change reflects the essence of the AI sales revolution: no longer an automated tool, but an intelligent partner that can truly help companies find and convert customers.
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We are at a historic turning point in the sales industry. Landbase represents not just a new technology, but a rethinking of the entire B2B sales model. Their concept of “VibeGTM” is transforming the way businesses engage with potential customers, making complex multichannel marketing campaigns as easy as chatting.
How AI agents are revolutionizing sales efforts
In my opinion, traditional B2B sales are like giant manual workshops where sales reps spend a lot of time every day on repetitive tasks: researching leads, drafting personalized emails, following up on leads, and updating CRM systems. According to statistics, sales reps actually spend only 28% of their time on sales, and the remaining 72% is occupied by administrative work and customer development. This inefficient work model not only wastes human resources but also misses out on a lot of business opportunities.
The emergence of AI agents has completely subverted this model. Landbase defines “agentic AI” as “advanced AI that can input independent actions and solve complex problems based on context, with the key word ‘independent’.” This is not a simple chatbot or a basic automation tool, but an intelligent agent that understands goals, strategizes, executes actions, and learns from results. They can autonomously handle the entire campaign, from customer research to personalized outreach, with little to no human oversight.
Take one case I recently learned of, where P2 Telecom, a medium-sized telecom service provider, added $400,000 in monthly recurring revenue during the traditional off-season after using Landbase’s platform. Even more impressively, their account executives were too busy to keep up with the number of AI-generated qualified leads and had to pause some of their marketing campaigns. This case illustrates the power of AI agents: not only can they identify and reach potential customers at scale, but they also ensure that these leads are of sufficient quality to convert into actual revenue.
I think the deep meaning of this shift is that it frees up human salespeople to focus on the work that really requires human skills: building relationships, solving complex problems, negotiating strategically. AI agents take on all the tedious and repetitive tasks, while humans take care of high-value activities that require creativity, empathy, and strategic thinking. This is not a simple job replacement, but a new model of human-machine collaboration.
Landbase’s core innovation: the GTM-1 Omni platform
When I took a closer look at Landbase’s technical architecture, I found that their core product, GTM-1 Omni, is actually a multi-agent system, like a full virtual marketing team. The system includes multiple specialized AI agents, each responsible for different aspects of the marketing process. There are AI GTM strategists who are responsible for identifying ideal customers and proposing targeted marketing campaign ideas; AI marketing specialists are responsible for creating hyper-personalized messages and content; AI sales development representatives are responsible for automated outreach execution; There are also AI operations analysts who handle the technical back-office work, ensuring that marketing campaigns run smoothly.
What impressed me most about this platform was its learning capabilities. GTM-1 Omni is trained on data from over 40 million marketing campaigns, which means it has “seen” a wide variety of sales scenarios and customer reactions. What’s more, it uses reinforcement learning technology to continuously improve, and the results of each marketing campaign are fed back into the system, making AI smarter and smarter. This continuous learning mechanism allowed Landbase customers to see a 4 to 7x increase in conversion rates compared to traditional manual marketing campaigns.
I’m particularly focused on how they solve a core problem in marketing: personalization at scale. Traditionally, businesses faced a dilemma: either bulk emails at scale but lacking in personalization, or customized outreach at a limited scale. Landbase’s AI agent is able to craft tailored messages for thousands of contacts by analyzing each prospect’s industry, persona, digital footprint, and behavior patterns. This hyper-personalization not only increases response rates but also makes recipients feel like the messages are written specifically for them.
From a technical implementation perspective, GTM-1 Omni integrates a vast B2B database with information from more than 175 million contacts and 22 million businesses. It also monitors intent signals in real-time – such as the topics the prospect is researching, the pages they visit, the content they engage with, etc. These signals are like the “eyes and ears” of the AI agent, telling it when and where potential opportunities exist. When the system detects that multiple team members of a target company are downloading a white paper related to your product, the AI immediately prioritizes the account and adjusts the message content to respond to the specific interest they have already demonstrated.
Quality control is also a key component of this system. Even the most advanced generative AI can occasionally produce inaccurate or inappropriate content, so Landbase has a built-in multi-layered quality assurance mechanism. Before any AI-generated email is sent, it undergoes a quality assessment: Is the content accurate? Does it comply with regulatory requirements and brand guidelines? Is the tone appropriate for the recipient? These safeguards ensure that autonomous systems do not derail from track, allowing businesses to trust AI to communicate on behalf of the company.
Let me analyze the concept of “VibeGTM” in more depth, a term coined by Landbase CEO Daniel Saks in early 2025. It represents the “atmosphere experience” of marketing, similar to the concept of “atmosphere programming” – achieving complex results guided by AI through simple, intuitive inputs. In VibeGTM, marketers or sales reps can describe their target audience and goals, and the platform’s AI agents will autonomously plan, generate, and execute multi-channel marketing campaigns to achieve those goals. The philosophy behind this is “you don’t need a PhD to grow a business.” In other words, launching an effective campaign should be accessible to anyone, not just experienced strategists or large teams.
At the heart of this “atmospheric” experience lies in removing complexity. Traditional campaign launches can take weeks or even months of planning: compiling a list of goals, drafting content, setting up sequences, and coordinating teams. VibeGTM compresses this timeline to the extreme—campaigns can be conceived and launched in a day, typically in less than 20 minutes. This agility means you can respond to market trends in real-time, not weeks later. Users simply log in to Landbase, view the AI’s campaign suggestions in the new Campaign feed, make some adjustments or approvals, and click launch. Landbase’s multi-agent system handles the rest, from selecting targets to optimizing send times.
I think the most revolutionary thing about VibeGTM is that it democratizes marketing. In the past, executing complex multichannel campaigns required a team of experts or expensive agency support. VibeGTM levels the playing field by packaging world-class marketing intelligence into one accessible AI platform. Consider a small B2B startup or regional telecommunications provider. They may not have dedicated marketing operations staff, data science teams to analyze market signals, or copywriter to craft personalized messages for each prospect. In the traditional paradigm, this puts them at a disadvantage relative to larger competitors. But with VibeGTM, that smaller business can log into Landbase and immediately take advantage of proven campaign “plays” and AI-powered optimizations that are usually only developed by top marketing teams.
Campaign feeds are essentially AI-generated campaign ideas that draw on patterns from millions of past campaigns. It’s like getting a playbook for 100 successful marketing teams, distilling it into ready-made templates. This means that a solo founder can run a marketing campaign that feels as polished and data-driven as a Fortune 500 company. The democratization effect also extends to the results. Everyone can benefit from AI’s continuous learning. If Landbase’s AI finds a particularly effective approach in one industry, this insight can inform marketing campaigns in another industry where applicable. In this way, best practices are no longer monopolized by elite companies – they are disseminated through AI.
Why now is the critical moment for AI sales to explode
I believe that the explosion of AI agents in the sales field is not accidental, but the result of a combination of factors. From a technical perspective, breakthroughs in large language models provide AI agents with the ability to understand complex instructions and generate high-quality content. But even more important is the change in market demand: the behavior patterns of modern B2B buyers have fundamentally shifted.
It is predicted that by 2025, 80% of supplier-buyer interactions in B2B sales will occur through digital channels. This shift towards digitalization has not only changed buyer expectations but has also redefined the way sales teams work. Buyers now expect instant responses, personalized experiences, and multi-channel consistency. Traditional sales models—relying on manual calls and standardized mailing—no longer meet these expectations.
From a cost perspective, AI agents offer a compelling value proposition. Traditional sales development requires recruiting, training, and managing a large number of sales reps, which is not only costly but also has limited scalability. While AI agents can provide the same or even better results at 60-80% of the cost of traditional methods. What’s more, AI agents never get tired, can work 24 hours a day, and can respond to customer inquiries instantly across time zones, providing businesses with a huge competitive advantage this “always-on” capability.
I also noticed the enthusiasm of the investment market for this space. Landbase’s $30 million Series A financing is not only considerable, but also has a luxurious lineup of investors. Sound Ventures has previously invested in star companies in the AI field such as OpenAI, Anthropic, and Hugging Face, and their investment in Landbase shows their optimism about the AI sales segment. As investor Guy Oseary put it: “Landbase makes it easy to build the hardest parts of the business. They’re not just solving outreach – they’re building the foundational platform for modern company growth. ”
The data also supports this trend. McKinsey’s research shows that businesses that use AI in sales and marketing achieve an average of 3-15% revenue growth and a 10-20% increase in sales ROI. And those organizations that leverage buyer intent data achieve 4-7 times higher conversion rates than those that don’t. These figures clearly show that AI-driven sales are not just a technological upgrade but a business necessity.
The AI automation revolution for multichannel marketing
Modern B2B marketing is far more complex than it is. Buyers now use an average of 10 different channels to interact with suppliers, including email, LinkedIn, phone, webinars, SMS, and more. Coordinating all these touchpoints can be a complex and time-consuming task for human teams, and that’s where AI agents shine.
Landbase’s AI agent can manage and synchronize outreach across multiple channels simultaneously, potentially sending a personalized email to a prospect, then following up on LinkedIn connection requests and messages, or even scheduling a phone call or leaving a voice message—all under a coherent strategy. The point is, AI decides the best channel mix for each prospect. For example, if data shows that a lead is very active on LinkedIn but never opens an email, the AI agent will focus on LinkedIn contacts. Another prospect may receive a series of orchestrated mail and phone calls if it works best for them.
This dynamic, tailored approach ensures that no single channel is overused or overlooked, in contrast to traditional outreach, which often relies heavily on email outreach. Studies show that using a true multichannel strategy can increase customer engagement by 287% compared to single-channel outreach. Businesses that leverage AI-powered omnichannel marketing campaigns see significantly higher response rates and conversion rates because they meet potential customers where they are most likely to respond.
I particularly appreciate the AI agent’s ability to scale in omnichannel marketing. An AI sales development rep won’t get tired or overwhelmed by following up. It can manage personalized communication with 500 prospects across email and social media in a single day — something a human team would need more manpower to try. And it executes every contact with consistency and perfect activity logging. This scale was demonstrated in a telecom customer case at Landbase: After adopting AI for outreach, they saw such a surge in engagement that their sales team “couldn’t keep up” with the number of qualified conversations.
Another advantage is the round-the-clock operation. Your AI agent doesn’t work a 9-to-5 work—if the data shows that leads are active at 7 p.m., they can send emails at that time or react to web queries received during the night. Lead response time – a key factor in conversion – has been drastically reduced. For example, if someone fills out a demo request form, AI can send a personalized thank you email and follow-up content in minutes, then schedule a call — all before your human rep even sees the notification. Speed and multi-channel responsiveness give you a competitive edge in capturing leads of interest.
Data-driven intelligent decision-making
I found that a key factor in Landbase’s success was its deep integration of data signals and intelligent analysis. While traditional sales often rely on intuitive or static customer lists, AI agents analyze real-time data to decide who to contact, when to outreach, what messages to deliver, and which channel to use. This data-driven decision-making is why AI agents are able to make relevant outreach that is almost predictable.
These data signals cover both traditional data and novel intent signals. Company profile and technology profile data provides core information about the company and its contacts – industry, company size, job title, technology used, etc. Intent data indicates when a company or buyer is likely to be “in the market” based on their online behavior. If a target account has several team members downloading a whitepaper on an issue related to your solution, that’s a strong signal of intent. The AI prioritizes the account and may tailor messaging to respond to the specific interests they show.
Engagement and web analytics data let AI agents track how leads interact with your own marketing and website. It knows who opened or clicked on your last email (and who ignored it), who visited your pricing page or case study library, and how often. These engagement signals inform next steps – for example, an AI agent might decide to send a special offer to someone who visits the pricing page twice this week.
Trigger events are also an excellent time for outreach, which the AI agent continuously monitors. Examples include companies announcing funding rounds, mergers or acquisitions, hiring a new CEO, or potential customers getting promoted or changing jobs (which could make them viable leads for their new company). As soon as these news emerge (usually through news APIs, social media, or data services), AI can act immediately to send congratulatory messages and customized pitches that connect the event to your value proposition.
Historical CRM data lets the AI agent view your own CRM history—past opportunities, wins/losses, and customer characteristics. It learns patterns such as “We tend to win deals faster when champions are VP level in IT” or “Financial services leads have historically had longer sales cycles.” It then applies these insights as it prioritizes and sends messages to new prospects. Essentially, it mines into your institutional data for predictive signals. Over time, as the AI records more of its own activity results into the CRM, it becomes a self-reinforcing learning loop.
Industry Cases: Successful Practices from Software to Telecommunications
I have observed that the application of AI agents in sales has spanned multiple industries, each of which can derive unique value from this technology. In the software and SaaS space, companies often face a crowded market, and quick access to the right decision-makers can make or break growth. These companies often have innovative products but need to educate specific roles (such as DevOps managers or CFOs) who are bombarded with vendor pitches.
Landbase’s AI agent gives SaaS sales teams an advantage by using technical graphs and intent signals to target strongly matched leads. For example, AI can identify companies that use complementary technologies, such as cloud platforms that integrate with SaaS products, indicating a higher likelihood of interest. The AI agent then crafts hyper-personalized messages tailored to that lead’s specific context—it might reference a recent developer conference presentation or a known pain point in the prospect’s tech stack.
In the telecommunications industry, B2B sales involve long sales cycles, strict compliance rules, and a lot of regional positioning. Keeping track of changing business developments, such as which companies are expanding their offices or needing infrastructure upgrades, can be daunting. AI agents have proven their value by monitoring trigger events and ensuring compliance in outreach. For example, telco AI agents track signals such as enterprise expansion to new locations or spikes in bandwidth usage as outreach leads about upgrading connectivity solutions. It automatically ensures that all messaging complies with telecommunications industry regulations – adhering to opt-out requirements, including appropriate disclaimers – which is built into its quality.
P2 Telecom’s success story perfectly illustrates the power of this application. The national distributor revitalized their sales pipeline using Landbase’s AI agent. During the traditional off-season, AI discovers pent-up demand by finding “hidden” leads that show relevant signals and engaging with them with personalized pitches about P2 voice and data services. The results were remarkable: P2 Telecom added $400,000 in monthly recurring revenue from AI source deals, and their sales team did struggle to keep up with the influx of qualified meetings.
The financial services industry has also begun to utilize AI agents for sales and marketing. Trust and trustworthiness are paramount in these industries, and outreach often must be highly tailored and compliant with regulations. AI agents can be trained in formal tone and resonant proof points in finance (e.g., emphasis on security, ROI, regulatory compliance). It uses a more cautious, data-driven approach when messaging bank executives than it does to tech startup founders. Crucially, AI’s built-in guardrails prevent any disallowed claims or sensitive information from leaking into communications – ensuring compliance with policies like FINRA or GDPR.
From manufacturing, which uses AI to target companies with specific supply chain needs, to healthcare and pharmaceuticals, where personalized outreach to new solutions to educate medical practices while maintaining HIPAA compliance, other industries are following suit. What all of them have in common is the core value proposition: AI agents automate the drudgery of lead generation and nurturing and do so in an intelligent, personalized way that produces better results.
My thoughts on the future of AI sales
Looking ahead, I believe we are only at the beginning of this transformation. In the next few years, having an AI “co-pilot” (or even an entire AI SDR team) is likely to become standard practice for B2B sales. Gartner predicts that by 2025, 80% of B2B sales interactions between suppliers and buyers will occur in digital channels. This digital-first reality will drive companies to deploy AI to cover these channels.
I expect to see a deeper level of personalization – a “one-to-one” marketing approach where each prospect’s experience is uniquely tailored. Future marketing AI agents will integrate more data sources about each prospect – not just company profile data, but also behavioral insights, personality tendencies (perhaps inferred from public content or previous interactions), and real-time context. They may dynamically adjust communication styles: for example, more formal language for finance executives and casual tones for tech founders, which is automatic.
Future AI agents will not be static models that require occasional retraining; They will be continuous learning systems. Using technologies like online learning and federated learning, AI agents get better in near real-time. We will see AI that can experiment autonomously (within safe limits) to improve results – for example, trying entirely new message angles or novel sequences and learning from the results. This self-optimization closes the feedback loop faster than ever before.
I also see the importance of collaboration between humans and AI becoming increasingly prominent. Far from eliminating the need for humans, the future of marketing will elevate the role of human experts working alongside AI. Routine tasks and first-contact outreach will be largely AI-led, meaning human salespeople can focus on higher-value activities: consulting with customers, solving complex problems, and negotiating deals. Human contact will remain where it matters most—building trust, handling nuanced discussions, and guiding strategies.
Of course, this shift also brings new challenges. Security and permission control have become more important than ever. When an AI agent can perform various actions on behalf of a user, how can it ensure that it is not overstepped or abused? Enterprises need to establish a strict permission framework, clearly define what actions AI agents can perform, and establish an audit mechanism to track all automatically executed actions.
I believe that those companies that adopt and deeply integrate AI agents will build an insurmountable competitive advantage. Every AI-run and learning campaign makes your future campaigns smarter, creating a widening performance gap. As Daniel Saks says on the podcast, we are entering an era where “Tech works for us, not we work for Tech.” This shift will redefine the role of sales professionals, allowing them to focus on building relationships and thinking strategically rather than being bogged down by repetitive tasks.
I see an interesting contrast in Daniel Saks’ entrepreneurial experience. In the AppDirect era, they needed to wear suits and ties, print consulting-style traditional presentations, and delve into corporate culture to adapt to large clients. At Landbase, they took the exact opposite approach: they aimed primarily at the SME market, no longer printing documents, and dressing more casually. This shift reflects a fundamental change in the market environment – from resistance to new technologies when cloud computing first emerged in 2009 to now everyone knows they need to adopt AI and are good at adopting it.
I particularly agree with Daniel on the timing of entrepreneurship. He pointed out that when SaaS rose in 2009, there were probably only about 100 companies that were actually long tenant SaaS, and competition was relatively limited. Today’s AI field is similar – although many companies claim to be doing AI, the number of companies that actually do native AI agents at the application layer is actually limited. But the biggest difference is that there are too many people creating content and noise now, and entrepreneurship has changed from niche to mainstream. This makes differentiation more difficult, but it also means greater opportunities.
I also thought about the issue of delegation of authority mentioned by Daniel. He predicts that in the next year, many business users will have more than 10 AI agents working for them, and the key question will be: How many permissions do you give these AI agents? He shared a story from a personal experience — he used a LinkedIn automation tool and ended up sending 2,000 messages over the weekend, including outright gibberish content in a language he didn’t speak, and even calling a woman “brother.” This example vividly illustrates the risks of AI automation.
I think this balance of permissions will be a core challenge for future business users: if you don’t use AI, you’re completely behind; But if you use Autopilot mode without safeguards, you have a lot of risks. Every business user needs to find this balance – how much permission you’re willing to delegate to an AI agent across different tasks and applications. This is not only a matter of technology, but also of personal branding and business risk management.
Looking at investment and technology trends, I noticed that a lot of capital is currently being invested in the infrastructure layer – just like the cloud computing infrastructure war in 2009. At that time, there were many brands such as IBM’s Softlayer and Rackspace, which were actually larger than AWS, and Azure didn’t even exist yet. But in the end, there were only three real winners: AWS, Azure, and GCP. I believe a similar situation will happen in the AI field – there will be several foundation model winners, but the real value will be reflected at the application layer. That’s why application layer companies like Landbase that focus on specific verticals (go-to-market) have great potential.
Ultimately, I think AI agents will not replace salespeople, but will make good salespeople better. They will do the heavy lifting – research, outreach, follow-up – allowing humans to focus on what they do best: understanding customer needs, building trust, creating value. This new model of human-robot collaboration will define high-performing sales organizations for the next decade.
My advice to businesses that are still hesitant to adopt AI sales technology is: start experimenting now. The barrier to entry for technology has never been lower, and the advantages early adopters are gaining – more leads, higher conversion rates, lower costs – are simply too impactful to ignore. As Landbase demonstrates, AI-powered sales are no longer a concept of the future, but a reality that can be achieved today. We are witnessing the end of one era and the beginning of another, and those businesses that can adapt to this change will dominate the competition in the future.
