The discussion around AI often focuses on content creation, software development and financial management. However, one of the business processes being transformed fastest is the go-to-market (GTM) process, where marketing, sales, and product align to generate revenue.

For years, the formula to acquire and upsell customers relied on the “demand generation funnel,” built on CRM and marketing automation tools like Salesforce or HubSpot. This process tracked customer engagement, used scoring to classify marketing-qualified leads (MQLs) and sales-qualified leads (SQLs), and relied on email to nurture prospects toward a “closed won” status.

This formula has been upended by two major developments. Firstly, email and social channel deliverability has collapsed by over 50% due to changes in spam policies (Yahoo, Google, Microsoft) and, critically, an “oversaturation” of the outbound channel. Everyone is running the same playbook with the same democratized tools, making it nearly impossible to differentiate.

Secondly, conventional personalization — like sending a “Congratulations…” email based on a job promotion announcement on LinkedIn or similar — is now considered the “new generalization” in the AI era and no longer works. Marketers need to be far more creative in both their GTM stack and their approach to personalization.

Now it’s a speed-to-market game

Everyone has the same tools; it is no longer a tools game but a speed/systems game. Maybe we can coin the term “speed to market” (STM).

While the historic dropoff in deliverability and the declining effectiveness of conventional CRM systems were bad news for marketers, the good news is that a whole new slew of AI-powered tools have emerged. And they’re empowering marketers to shape and manage highly effective GTM pipelines at a scale and speed never before seen.

With the rise of AI, custom research and workflow automation have become much faster, so it’s easier to build, test and iterate. Speed-to-market tools such as Clay, n8n and Unify GTM enable marketers to automate workflows by combining AI capabilities with business process automation. Complex prospect-nurturing workflows that once would have required intensive code development and platform integrations can now be automated and managed directly by marketers without coding.

With these powerful capabilities, the most critical step in the GTM process becomes choosing the right intent signals to base action on. We call this approach GTM engineering, which depends on finding the right signal power spectrum to inform outreach:

1. First-order: Demographic, firmographic. The classic ideal customer profile (ICP) data: Company geo, size, industry, job functions and titles.
2. Second-order: Technographic or event-based. Signals based on business events, such as a particular tech stack they’re using, a recent round of funding, a newly hired management team member, or specific job postings.
3. Third-order: Highly specific to their business. Hyper-relevant, timing-based updates like a new product release, reports, blogs or other recent content pushes.

The combination of all three signal orders provides a more focused and targeted approach to identifying and engaging the best-fit buyers.

AI-powered GTM in action

Consider a Series A-B startup implementing GTM engineering. A key tool in their stack is Teamfluence, which monitors everyone who engages with the team’s content on LinkedIn (followers, likes, comments, profile views). If, over 30 days, 3,170 people engage with content across the team, this list represents a pool of “high intent” actors.

This list is then fed via a webhook into Clay, an AI-powered outbound GTM platform, providing a warm audience. Clay allows the marketer to drill down into each individual, checking their content interactions, job function, and other data points against the ICP. Crucially, Clay then uses several apps, like Apollo, to source the best contact information, such as an individual’s email address.

Furthermore, Clay logs the engagement type (like, comment, etc.) and assigns a score (a “like” is 3, a “comment” is 5). This entry is checked against HubSpot (used as both CRM and marketing automation); if the contact exists, the engagement score is updated; if not, a new contact is created to capture the total addressable market.

Once added to HubSpot, these contacts are added to email nurtures and LinkedIn outbound motions. The final, most important step is activation, which can involve running LinkedIn ads for those contacts, adding them to a retargeting program and putting them into automated nurture workflows on HubSpot. This automated, data-driven process stays “top of mind” and informative because the engagement is based on strong-fit data-driven behavior.

The same contacts can also be added to campaigns on La Growth Machine, a tool that automates personalized outreach across LinkedIn and email. This entire process is completely automated. In the example, the initial 3,200 leads yielded an estimated 320 conversations and a significant uptick in engagement and reply rates compared with conventional CRM targeting.

The modern AI paradigm has shifted the GTM strategy. Five to 10 years ago, every channel and platform was separate; now, everything is interconnected, allowing GTM marketers to think systematically. AI enables users to more accurately find and reach out to individuals who meet specific ICP parameters, and it is far better at recognizing false positives and false negatives, preventing alienation of unreceptive prospects.

This shift means the composition of GTM teams must change. Where once skilled copywriters and email marketers were prioritized, today, having skilled GTM engineers who can sift through massive datasets to pinpoint individuals meeting the ideal customer profile and manage a systemic process to optimize outreach is paramount. The ability to craft and manage this systemic process is more important than the actual message.

*Jatin Gupta presented a talk on this material at the 2025 super{summit} in San Francisco, a session that can be viewed in this video. *

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