Hey, GTM Engineers!

At Clay, our mantra is “Make it work, then make it great.” In the spirit of this mantra, I’m launching the first “Trends in GTM Engineering” for 2025.

As one of the first GTM Engineers, I hope to articulate observations I’m seeing talking to the most innovative GTM teams. This post was inspired by a meeting last week and observations over the previous year after talking with hundreds of marketing, operations, strategy, and sales executives.

Last week's meeting forced me to distill my thoughts for the presentation to GTM Executives at a $40B tech company. They wanted help answering the question, “What does the future of GTM look like?”

We talked a lot about finding alpha in GTM, the need for experimentation, GTM Engineering and other trends that I’ll highlight in this post.

⁉️ What is alpha in GTM?

👉️ Alpha is your competitive advantage. Your GTM differentiation.

It’s how you beat your competition to getting to the right customer at the right time with the right message.

This is critical because product differentiation is no longer enough to win, and it will not be sufficient in the future.

It’s simply too easy to create, build, and scale products, especially with AI.

We all know this. Ironically, although it’s the easiest time to build products, breaking through the noise and getting those products in customers’ hands has never been more challenging.

Yet still, many companies are ignoring the canary in the coal mine - old playbooks aren’t working.

❌ Outdated playbooks are failing for several reasons:

1. Your competitors are running the same playbooks

2. Old playbooks have gotten boring or annoying to customers

3. You’re struggling to break through the ever-increasing noise

4. Customers are struggling to find the signal in all the sound

This is the challenge.

💡 There’s no longer alpha in running your Go-To-Market, Sales, or Marketing like your peers. 

Product AND Go-To-Market need to be differentiated.

So, how do you find alpha in GTM today?

🔁 Constant experimentation.

The pursuit of GTM competitive advantage isn’t the only trend we’re seeing. Here’s a summary of the key trends we’re seeing in 2025.

2025 Trends in GTM Engineering

Trend 1: Finding Alpha Requires Experimentation

Ultimately, we discover scientific and technological breakthroughs by experimenting. You can’t find something new unless you try something new. If you stop experimenting, you stop discovering what works.

The same concept applies in finding alpha for GTM.

Experimentation is required to find/discover what your high-impact strategies & playbooks are.

GTM teams aren’t set up to run experiments quickly. However, converting an idea into an execution requires many cycles and work.

This isn’t a one-and-done situation. You may find a compelling strategy or motion, but six months from now, it might stop working.

Maintaining a competitive advantage requires maintaining ongoing cycles of experimentation.

I think of it as the following cycle:

1. Ideate a hypothesis

2. Determine the workflow needed

3. Experiment with data

4. Automate

5. Scale what works

Going from idea to execution quickly and perpetually in a scaleable way requires GTM Engineering.

Trend 2: Rise of GTM Engineering

Think of Clay as your GTM development environment for rapidly building and running experiments that combine data, AI, automation, and workflows. Similar to how software engineers connect various services, infrastructure, and open source libraries to create a SaaS product - GTM Engineering is doing the same but by connecting GTM tooling, Data pipelines, and AI.

This is ultimately what GTM Engineering is - applying software engineering principles to running experiments at scale. These teams are starting to think more like product & engineers to build these systems.

Snyk, for example, is renaming one of its operations and strategy teams “GTM Engineering.” OpenAI has GTM Engineers. Dropbox has a Head of GTM Engineering (one of the first we’ve seen), and many companies are hiring GTM Engineers.

GTM Engineering has given rise to the GTM Engineer, now the practitioner and mad scientist of this new field.

In parallel, we are seeing the rise of the Modern Seller—sales reps who have embraced new capabilities and invested in learning how to apply AI practically. They are the beneficiaries of the GTM Engineer’s work, but they take the systems & tech a step further to learn how to maximize the system.

Modern sellers are learning prompt engineering and how to create workflows that save them time. This allows them to reinvest that time with customers, have more meaningful conversations, and move quicker.

Trend 3: Rise of the GTM Engineer

GTM Engineering is the job function that builds the infrastructure & workflows, runs the experiments (test to scale), and then integrates winning strategies into the fabric of the company’s GTM tool stack & teams.

The GTM Engineer is the one who does the assembly.

These are a couple examples of job postings that describe the role.

Example GTM Engineer Job Opening at OpenAI:

Example GTM Engineer Job Opening at Semrush:

Trend 4: Rise of the Modern Seller

The Modern Seller is the largely the beneficiary of GTM Engineering. (Yes, Marketing, RevOps, are too).

Modern sellers consume the services built by GTM Engineers, but they also invest in new skills:

• Prompt engineering

• New tooling

• Understanding of AI models

• AI Agents

Trend 5: Data & GTM Intelligence becomes Centralized

List building —> final outreach typically takes a lot of steps and intelligence gathering. This is all moving from the rep to a centralized team.

Account Executives and SDRs have historically ample time:

• Building lists

• Prioritizing those lists

• Researching people

• Researching accounts

• Reviewing historical data in CRMs

• Reviewing product data from users

• Developing account POVs

• Figuring out what’s the best product to sell into that account

• Creating personalized messages

• Staying on top of news, people changes

• Adjusting to changes in all of the above

That’s A LOT of time away from the customer.

GTM teams have realized this and are now centralizing this data aggregation, analysis, and outputs within the Operations, Strategy, and GTM Engineering teams.

GTM Intelligence is then provided to the reps so they can focus on actioning the insights & recommendations.

Trend 6: Just-in-Time Enablement for Reps

JIT is a term for software engineering, but lends itself nicely to many of the workflows that are getting built for sales teams.

I stopped doing pre-call research. It’s all done for me and a perfect example of “Just-in-Time” enablement.

The morning of every call I receive a slack alert with a link to an automated pre-call research doc on the company and people that I’m meeting with. Below is an example of one of these that is built with Notion.

Other examples include:

• Post-meeting notes (not the Gong kind, these are custom and MUCH better)

• A Slack bot that can answer questions about accounts, SFDC activity, and so on

• Alerts on website visits and summarized activities of named accounts

And many more, but I’ll save those for another time.

Trend 7: Signal Driven Pipe Generation

Finally, one of my favorites.

Using signals as a trigger to time outreach based on prospect activity.

This workflow does the following and gets a 40% reply rate on LinkedIn:

1. Listen posts on LinkedIn that mention a keyword (in this case “AI Sales”)

2. Send that post and profile into Clay

3. Analyze and segment the persona

4. Analyze the post

5. Create a personalized message

6. Send to La Growth Machine

7. Auto send connect requests and follow up messages

All of this runs in the background.

Trend 8: The AI Sales Funnel

It’s practically a mandate to integrate sales into the sales function and GTM more broadly.

Many conversations start out with the customer saying “our CEO doesn’t care how, but he knows we are behind and we need to integrate AI.”

AI is getting integrated into every step of the sales funnel. From using AI Agents to automate manual research —> segmentation —> personalization and all of the internal processes that reps need to follow to do their job.

Clay of course makes this easy as AI can be embedded at any step in an automated workflow build in Clay.

What other trends are you seeing?

Drop me a line. Always looking for community ideas to include in posts.

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