VibeGTM and AI-powered outbound

You’ve heard of vibe coding, but have you heard of VibeGTM?

The idea behind vibe coding is that instead of spending weeks writing code in JavaScript or Python, you simply write a prompt in natural language describing the software application you want built, and AI will build it for you.

VibeGTM applies the same principles, but to sales and marketing campaigns. Instead of spending hundreds of hours doing all the legwork to launch a campaign, you simply write a prompt and ask AI to do it for you.

For example, a VibeGTM prompt for a talent and staffing agency might be:

“Design and execute an ABM campaign to target talent acquisition leaders at UK-based companies and secure $1M in new pipeline for the sales team.”

In response to the prompt, a VibeGTM application might create a list of 100 target accounts, identify two to three potential buyers at each account, and send a series of personalized emails to each person to secure a meeting.

The term VibeGTM was coined by a startup named Landbase, which offers an agentic AI platform that can automate entire sales and marketing workflows based on simple prompts like the one above. VibeGTM is just one term in a whole new lexicon of phrases like GTM engineering, revenue orchestration, and data enrichment waterfalls being introduced by new AI-powered GTM vendors like Clay, UnifyGTM, and Exa.ai.

Concepts such as VibeGTM and revenue orchestration apply across a wide range of sales and marketing workflows, but early use cases are primarily focused on outbound campaigns.

Outbound is a natural starting point for AI because it involves many time-consuming, tedious tasks that most humans would prefer not to handle. No one wants to spend time tracking down email addresses and phone numbers for potential buyers. No one wants to spend hours researching accounts and writing personalized emails for each one.

Although VibeGTM applications offer agents that can run autonomously, most sales and marketing departments use them primarily as copilots to assist with their campaigns. AI doesn’t need to run in “full self-driving mode” to offer value. If 80% of the low-value, tedious work in a marketing campaign can be automated with AI, the ROI will be significant.

In this article, we’ll explore how sales and marketing teams can use VibeGTM and AI platforms to execute an outbound account-based marketing (ABM) campaign.

Outbound marketing: Early adopters of VibeGTM

Let’s revisit the example above, in which we asked a VibeGTM app to help build an ABM campaign targeting talent leaders at UK companies. The major steps in the campaign would be:

1. Target accounts: Identify a list of target accounts for the campaign based on the ideal customer profile.
2. Identify potential buyers: Find the right potential buyers at each account, as well as their email and mobile phone numbers.
3. Build messaging: Develop personalized messaging for each buyer based on their LinkedIn profile and company website.
4. Start outreach: Reach out via email, phone, and LinkedIn to secure a meeting.

Let’s walk through each step of the campaign. We’ll explore how a human would approach each of these tasks and then show how emerging AI technologies can help to boost their productivity.

Step 1: Target accounts

First, we want to find the right list of companies to include in the campaign.

The traditional approach

To start, we open a sales intelligence tool such as ZoomInfo, Cognism, or LinkedIn Sales Navigator, then use filters to find accounts that match our ideal customer profile (ICP). We’ll add key firmographic criteria for our ICP accounts, such as revenue range and industry sector, then add more specific filters for this campaign, including headquarters location (UK) and open job listings.

A skilled ABM marketer or SDR can create an account list in five to ten minutes, but often the list contains either too many or too few accounts for the campaign. Suppose that our query returned a list of 10,000 companies. That’s too many for a small sales team to prospect with a high-touch motion over a four-to-six-week campaign.

To narrow down the list, we could add more specific criteria to the filters. For example, we know that our best accounts usually meet one of the three following criteria:

• Hybrid remote/office work culture
• Seasonal workers employed during the holidays or summertime
• Top employee perks like continuing education budgets and unlimited PTO

You won’t find these types of details as reportable fields in most sales intelligence tools. To identify if a company checks any of those boxes, you will need to visit their website. Few sales and marketing teams have the capacity to do such time-consuming, detailed research, but AI can do it in a few minutes.

Agentic AI search

AI agents can scrape the websites of each target account and read the careers pages to understand the company's benefits, job locations, and workforce profile.

These types of agentic search capabilities are built into VibeGTM platforms. With a simple one-sentence prompt, you can kick off a search for accounts that will not only look at the traditional firmographic filters available in sales intelligence tools, but will also search company websites, news releases, job listings, online reviews, patent filings, earnings reports, and academic research to find the accounts that match your desired profile.

Suppose, for our example, that an agentic search agent scanned 10,000 websites and confirmed that at least 500 meet one of the additional criteria (hybrid, seasonal workers, employee perks). That’s a heck of a starting point for your ABM campaign.

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Step 2: Identify potential buyers

Once the list of target accounts is finalized, the next step is to find the right potential buyers at each of the 500 companies. For our example, we need to identify senior decision makers in the human resources department with talent acquisition, recruiting, and search titles.

The traditional approach

Modern sales intelligence tools make it quick and easy to build this list. However, finding the contact details, such as email address, phone number, and LinkedIn profile, for each of these potential buyers is usually a painful process. The global workforce totals almost 4 billion people. Even the biggest and best sales intelligence tools don’t have up-to-date contact information for every one of them.

Suppose that Virgin Airlines is one of the target accounts for the campaign example above. An SDR might query the sales intelligence tool to find 15 potential buyers with talent acquisition titles, but only five of them have an email address in the record. The SDR will need to search additional sources, such as Lusha and Hunter, to track down the emails for the other 10.

In real-world scenarios, these prospects without an email or phone number are often dropped from the campaign list because of the effort required to find their contact details. This is another area where AI can excel.

AI data enrichment waterfalls

Modern AI-powered outbound research tools use a “data enrichment waterfall” approach, searching multiple sales intelligence databases to find the complete set of contact details for each person on the list.

Suppose that for our campaign example above, we need to find contact details for 1,000 talent acquisition leaders. The data waterfall will first search “Source A” for the emails and phone numbers. It finds some of the contact details – 700 emails and 600 phone numbers.

The waterfall will then search another sales intelligence database, “Source B,” for the remaining contact details. Source B provides 100 additional email addresses and 50 phone numbers, but many buyers still have missing information. The waterfall will then move on to “Source C,” “Source D,” “Source E,” and so on until the list is fully populated.

Some AI tools use more advanced algorithms to assign a quality score to each email and phone number, reflecting the model's confidence in the data's accuracy. For example, an email address discovered in only one source might have a low-quality score (e.g., 2 out of 10), whereas one confirmed in two, three, or four different sources would have a high score (e.g., 10 out of 10).

Step 3: Personalized messaging

Decision-makers at big companies receive hundreds of pitches from vendors every week. To get a response, we need a personalized message that helps us stand out from the noise. Personalization is an effective strategy to boost response rates, but it’s often unrealistic due to the amount of work required.

The traditional approach

Research needs to be conducted on each company and each individual person to find the type of “show me that you know me” content that gets a response. Reviewing a prospect’s LinkedIn profile along with the company’s website, press releases, and social media posts can take 10 to 20 minutes per person.

Most sales and marketing teams don’t have the capacity to conduct the level of research required for each prospect. With response rates for outbound campaigns often less than 1%, the juice isn’t worth the squeeze.

As a result, most GTM teams attempt to fake it by making a vague reference to the recipient’s job title or industry sector. However, these weak personalization strategies rarely solicit a response. Fortunately, personalization is another area that AI can scale in ways that humans cannot.

AI personalization

Agents can be trained to research each prospect, identify personalization strategies, and write targeted messages for each prospect. VibeGTM platforms can operate autonomously to perform research and decide which personalization strategies to use in messaging

For example, in the talent ABM campaign referenced above, we might want to look for current job openings we can reference in our messaging. Specifically, we want AI to focus on high-demand or hard-to-fill job postings. Roles that are challenging to source are the ones most likely to be handed over to an outside recruiter.

Another personalization strategy could be to flatter the individual person with some form of recognition. We could ask AI to search for recent promotions, awards, or career milestones to include in a congratulatory note. AI could also search for quotes from recent blog posts, podcast interviews, or media interviews to reference.

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Personalization can also be applied at the company level, not just at the contact level. For example, we could reference a quote from the CEO in a recent earnings announcement, an observation about a successful product launch, or a word of appreciation for a charity program the company sponsored.

AI platforms can use research output to write personalized messages for each prospect, which can be used in cold-call scripts, email campaigns, or LinkedIn messages.

AI can also convert messages into different languages. Suppose the SDR and marketing team are centrally located at headquarters in London, but sales representatives are located throughout Europe. Personalized messages could be created in French, German, Italian, Dutch, and Spanish, then sent to prospects on behalf of the respective sales reps in each country.

Response rates for local language messages are higher than those for English text, even when the translation isn’t perfect.

Step 4: Outreach

The fourth and final step in the campaign is to reach out to the prospects to secure an introductory meeting.

The traditional approach

Much like the earlier steps, the outreach step is tedious, time-consuming, and challenging to scale. Each prospect needs to be contacted multiple times through multiple channels (phone, email, LinkedIn) with a personalized message. Only a small percentage will respond and convert.

Fortunately, outreach is an area AI can help to scale as well.

AI SDRs and email

There are a number of AI SDR agents on the market that can run cold email campaigns autonomously today. Fully automated approaches can be a good option for simpler products and campaigns. However, for more complex offerings and campaigns, achieving operating AI in a copilot mode is a more common approach.

One example of how AI can boost productivity is monitoring prospect email responses. Unlike humans, AI can work 24/7/. If a prospect sends an email at midnight on Saturday asking about costs, an AI agent can respond immediately with a link to your website's pricing page. If the prospect asks for dates and times to meet, an AI agent can immediately send out a link to the sales representatives' Chilipiper or Calendly scheduling link.

Numerous studies have shown that the speed at which a sales team responds to a prospect’s inquiry directly correlates to the conversion rate. Of course, not all incoming emails will be appropriate for an AI-led response. More complex inquiries can be routed to a human SDR for a more thoughtful and customized response.

AI can also help improve email deliverability by automating repetitive, high-volume tasks that most sales and marketing professionals don’t have time to handle.

One good example is scanning the campaign list to identify email addresses likely to bounce or negatively affect the company’s sender reputation score. AI can verify each email address to ensure that it follows the appropriate syntax for each company (e.g., firstname.lastname@company.com). It can filter out any known spam traps and identify hostile recipients who are likely to report the message as spam or block the sender.

AI and cold calling

AI can also help with phone outreach. Currently, there are no voice AI agents that can autonomously cold call and engage in interactive conversations. However, AI can help to optimize human callers. It can automate repetitive, high-volume tasks that most sales and marketing professionals don’t have time to handle.

For example, AI can scan through a list of phone numbers to filter out fax, SMS, and voicemail-only accounts. It can filter out prospects on Do Not Call registries. It can use historical answer patterns to recommend the best time to call a specific prospect and flag numbers typically answered by a gatekeeper, such as an executive assistant.

In the not-too-distant future, we will likely have AI-powered robo-callers that can engage buyers in real-time conversations. AI sales avatars are already appearing on websites to engage buyers and offer product demonstrations. Startups such as 1Mind, Docket.io, and Saleo are among the pioneers.

Getting started with VibeGTM

There are multiple ways to jumpstart your AI-powered outbound program. The easiest approach is to start small and focus on one of the areas discussed above.

For example, if your sales team spends a lot of time finding email addresses and phone numbers, you could start with a data-enrichment waterfall such as FullEnrich or Waterfall.io. If you want to leverage agentic AI to search the web using complex ICP filters to find high-fit accounts with purchase intent, you could start with Exa.ai or Landbase.

Once you’ve successfully implemented the first project, you can move on to a second with a “crawl, walk, run” approach.

If you are ready to go all in and adopt the full stack, you should consider either a revenue orchestration platform or an AI SDR.

Revenue orchestration platforms are best for companies that want to take a hands-on, human-led approach to AI. Clay, UnifyGTM, and Cargo are examples of revenue orchestration platforms that include most of the capabilities described above, including data enrichment waterfalls, agentic search, personalized messaging, and outreach (via email).

Implementation will require technical resources, such as a GTM engineer, who can set up complex integrations, write SQL queries, and write AI prompts.

If you’re looking for a more “hands-off”, autonomous approach, consider implementing an AI SDR. Startups such as Artisan, 11x, and AI SDR offer agents designed specifically for research, qualification, and outreach. The agents will automatically find target accounts and contacts, develop personalized messages, and perform email-based outreach to secure appointments.