5 practical AI experiments your GTM team can run before 2026

Connected data is the new cookie. Start with systems that talk to each other—and build from there.

Are you using AI, or are you actively designing new systems with it?

Most of us are in the first camp where we’re testing tools, trying prompts, and maybe automating a few tasks here and there.

That’s fine for now… But if we want AI to actually move the needle in our go-to-market systems, we need to get serious about structure.

Before we can hand work over to bots or co-pilots, we have to know what that work is.

We need clean data. We need data that talks to other data. And we need systems that don’t just coexist, but cooperate.

This is a noisy time for marketers. There’s a new AI tool every hour. Every other post on LinkedIn sounds like a sales pitch or a panic attack. It’s easy to feel stuck. So where do we begin?

If we zoom in on five areas where AI can help right now, we can make progress.

PS - These aren’t silver bullets but they’re some controlled experiments you can run with a small team and tight scope.

Let’s break them down.

1. Use AI to enrich outbound prospecting

If you're still relying on broad filters and job titles to build outbound lists, you're probably wasting solid leads.

If the goal is to double your lead qualification rate without doubling effort, try this:

Use Clay to enrich your prospect lists with more context—buying signals, trigger events, or niche firmographic data.

This helps you:

• Build more accurate targeting lists

• Book better meetings without doubling your SDR headcount

• Cut the time it takes to qualify leads

📍 Bonus idea: If you’re already running LinkedIn Ads and want to know who saw your ad and clicked—so you can track signals and tailor your follow-ups—do this:

Take your enriched list and plug it into LinkedIn as a Lookalike Audience. Then use Fibbler to pull ad engagement data back into your CRM.

Suddenly, you’re not guessing who’s engaging. You’re building a feedback loop.

2. Let AI flag sales-ready leads sooner

We all know what happens when someone is ready to buy but no one follows up. The window closes fast and all that effort put into marketing gets wasted.

Use tools like MadKudu, Breadcrumbs, or even your own simple AI rules to track buying behaviour across your CRM, website, and product.

You’ll start seeing:

• Less lag between intent and follow-up

• Shorter sales cycles

• Fewer good leads slipping through the cracks

The goal isn’t to automate everything. It’s to make sure the right humans act at the right time. Which is one of the core principles of good marketing, anyway.

3. Trigger micro-personalised nurture journeys

Some people read every case study on your site but never sign up. Others sign up and disappear. These are two very different signals, but most nurture tracks treat them the same.

AI can help segment these behaviours in real time. You can trigger specific email sequences or in-product messages based on what people actually do, not just what list they’re on.

This helps you:

• Treat different users differently, without needing a huge team

• Build trust faster

• Move users from awareness to value without gaps

4. Train internal AI copilots on your own content

Support teams and sales reps waste a lot of time searching for answers. That delay adds up.

You can fix this by training internal tools (chatbots, helpdesk assistants, even sales support copilots) on your own product docs, help centre content, and real customer tickets.

What you get:

• Faster, more consistent answers

• Less back-and-forth for support tickets

• Better visibility into what customers are struggling with

It’s not just a time-saver. It’s a way to spot patterns and improve the product too.

5. Optimise web content for AI and LLMs, not just humans

SEO isn’t dead, but it’s shifting and AI ranking remains wholly unclear... The point is, ranking high in search doesn’t mean what it used to, especially with AI overviews and summarised results becoming more common.

You can start by making your high-performing content more machine-readable. Add clear, structured FAQs. Use Schema markup and break answers into short, scannable blocks.

Why this matters:

• You stay visible, even as click-through rates from search drop

• Your content becomes the “answer source” for AI-generated snippets

• You keep traffic flowing without chasing keyword tricks

Bonus ideas for the curious (and slightly overcaffeinated):

These are a bit more advanced, but worth exploring if you’ve got some momentum or extra capacity in your team.

6. Spot churn before it becomes a win-back problem

If you want to surface behaviour shifts that signal a customer might leave, you might want to set up alerts to intervene earlier and keep more of the customers you already worked hard to win.

Tools: ChurnZero or Mixpanel’s predictive AI.

7. Write outbound emails that sound human

No more automated "Hi John, I saw you breathe air and work in SaaS" intros. Try tools like Lavender or Regie.ai to generate opening lines that reflect real lead research without being too creepy.

8. Score leads by behaviour, not just by title

Stop chasing cold leads and focus on the ones that are more likely to convert. MadKudu and CaliberMind can help you prioritise leads based on quality and intent, so you’re not just relying on job function or company size.

9. Turn qualitative feedback into product direction

Get clearer input for roadmap decisions, straight from the voice of the customer.

Try tools like Viable or Dovetail to help you cluster open-ended feedback into Jobs-to-be-Done style insights.

How it all fits together: Experiments → systems

You don’t need to run all of these at once. Start with one or two. But keep an eye on how they connect.

When tools and data work together, you get more than isolated wins. You get a real GTM system that learns and improves over time.

For example:

• Clay + LinkedIn Ads + Fibbler gives you a full loop from targeting to visibility to CRM feedback.

• MadKudu + nurture sequences means only high-potential leads get sales attention.

• Internal copilots + Viable means your support team surfaces patterns that lead to faster product wins.

• Mixpanel + predictive scoring means you spot churn risks or expansion moments before your competitors do.

When systems talk to each other, your team doesn’t just work harder. It works smarter.

You don’t have to wait for the perfect setup. Start small. Prove it works. Adjust. Then scale.

What experiments are you running right now?👇