In less than two years we’ve gone from the launch of ChatGPT to companies promising to rip-and-replace your entire SDR team. AI has officially become the “must-have” tool for GTM teams. The old school playbooks that produced dependable pipeline no longer work.
This shift in the market can feel confusing - do you go all-in on AI automation or take a co-pilot approach? How do you stay ahead of the curve when everyone seems to be running the same plays?
Two words: experimentation loops.
Teams need to be constantly testing and evolving to win in a tight market. They also need the right hires owning this process and acting as the connective tissue across the entire GTM org.
Last week we hosted a roundtable discussion with Emily Kramer, co-founder of MKT1, to get the scoop on her experimentation framework and talk about how GTM team structure is changing with the growth of AI. The event was exclusively for founding 10x GTM community members, but this information was so valuable I had to share a recap with all of you today.
Driving pipe gen through experimentation
Modern GTM teams face two major challenges:
1/ The “proven pipeline playbooks” of the past few years are no longer effective.
We’ve seen this problem before. A decade ago, SEO was supposed to be the magic hack that would solve teams’ inbound problems. Just hire content writers, keyword stuff your site, and watch the leads roll in.
The problem? When everyone discovers a new pipeline source, it quickly becomes saturated and ineffective. As Emily emphasized during our roundtable, “The thing that works today might not work tomorrow.”
2/ Experimentation can feel haphazard and unscalable.
Lots of GTM teams are running some level of experimentation today, but reps aren’t incentivized or equipped to share what’s working with the whole team. The best strategies can stay siloed to just a few people. These experiments don't move the needle without a clear owner to manage the process and automate the wins.
Experimentation loops solve both of these problems.
First, they keep teams ahead by building new pipe gen strategies proactively. Instead of scrambling when an existing playbook stops working, teams have new plays in development at any given time. Second, working in iterative cycles makes experimentation less random and more of a defined process. Teams can also find a balance between automation and manual workflows, saving their time and energy for their highest-value prospects.
Emily recommends following a four-step framework for building out experimentation loops on your team:
1/ Start with a clear hypothesis and metrics for success. No more “random acts of outbound.”
2/ Design a dedicated workflow for testing new strategies. This will make it easier to scale when you find something that works.
3/ Build a ritual for reviewing and analyzing results. Be ready to pivot if an experiment isn’t performing.
4/ Create a feedback loop with the teams you’re working with. Don’t just roll out new plays without gathering feedback from the end users.
When you’re running experimentation loops, think of the process as cycling from small and manual to scaled and automated. Kick off testing with a tightly targeted audience and fully hands-on management. Once you’ve proven a strategy works you can scale to a larger audience and layer in automation. Then, when you’re ready to optimize your new workflow, start your testing with a small group again.
The new must-have GTM hire
For experimentation loops to be successful at scale, Emily recommends moving away from hiring for a single function and instead having a dedicated owner who can act like the “glue” across GTM. The role goes by different names, including producer or program manager, but no matter the title, they work to:
- Connect the dots between your sales, marketing, and product teams
- Orchestrate your tech stack to create seamless workflows
- Design and run your experiment loops
- Translate the results into actionable insights for the rest of the team
The ideal hire is equal parts tech-savvy, strategic thinker, and cross-functional collaborator. They're not afraid to get their hands dirty with new AI tools, but they also understand the human element of sales and marketing.
To get an up-close look at this critical role, we talked with Laura Gassaway, Pipeline Generation Program Manager at LaunchDarkly. Laura sits on the Enablement team, under RevOps, though her role works cross-functionally with the entire GTM team.
While her main focus is to make sure marketing and RevOps work in lockstep, she also partners with product to support LaunchDarkly’s PLG motion and with sales to enable best practices when it comes to outbound prospecting.
As the go-between for every part of GTM, Laura often serves as a ‘translator’ between different orgs. Her most valuable skill is her ability to “lean into transformative change” and to help the teams she collaborates with do the same.
One of the most obvious areas where reps need to be willing to learn and adapt is with AI tools. Laura’s partner teams are seeing success with AI ‘basics’ like ChatGPT as a writing partner, as well as sales-specific tools like Gong AI and Pocus. In Laura’s experience, the best sellers know how to blend AI efficiency with a human element, rather than trusting AI to do it all.
Speeding up experiments with AI
Running multiple experimentation loops along with enablement and program management tasks can be time-consuming. Luckily, the testing and iteration work of experimentation is also where AI can be most helpful today. (Check out our cheat sheet of all the places we do – and don’t – recommend using AI!)
Producers can speed up their “time to knowledge” with AI in three places:
- Initial research: Use AI to quickly size your TAM, identify what your ICP cares most about, or find the best channels for outreach.
- Data synthesis: AI excels at distilling information and crunching numbers, so use it to make quick work of analyzing your experiment.
- Identifying patterns: As you run more experiments, subtle patterns might start to emerge. AI can help you identify areas of opportunity you didn’t see at first.
AI still has a long way to go, though, so we don’t recommend going full auto-pilot - at least not yet. Focus on building efficiency around tedious, time-consuming tasks with AI and hiring the right people to act as your creative engine and GTM glue. If you’re curious how Pocus AI can help power your experimentation loops and outbound strategy, book a demo with our team.
I’ll be digging deeper into how the GTM org is changing due to AI, and what to look for in your new hires, in a future newsletter, so stay tuned! If you’re a GTM leader who has changed the way you hire with the rise in AI tech, I’d love to include your thoughts. Just shoot me an email and I’ll be in touch!