4 Reasons Why GTM Teams Will Fail With AI This Year
Also - 3 lessons from Open AI's Head of GTM Systems
In this edition:
4 Reasons Why GTM Teams Will Fail With AI This Year
3 Lessons from Open AI's Head of GTM Systems - Podcast Episode
POLL: Why is Marketing ROI Still So Hard to Prove?
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4 Reasons Why GTM Teams Will Fail With AI This Year
My prediction: By this time next year, 80%+ of GTM executives will be scratching their heads, wondering why AI hasn’t yielded tangible business results.
Here are the four big reasons 👇
1 - No one fixed the data
Can you guess what AI needs to do ANYTHING? Complete and accurate data. Good luck letting your AI SDRs loose on the wrong data. Companies are still investing minimally in data enrichment, normalization, and accessibility - and it’s literally the most important thing.
2 - Ops is still undervalued and underinvested
The narrative that AI replaces ops is dead wrong. In reality, AI adoption increases the need for skilled MOPs and RevOps professionals. To get AI right, you need the right integrations, orchestration, governance, and reporting - who do you think will do that for you? Hire ops now.
3 - Execs are asking for the wrong things
Top-down AI mandates are disconnected from what the team actually needs. Use AI to generate more leads instead of helping sales align with marketing. Use AI to spam more people instead of niching down to the right buyer. AI transcription notes from meetings that could have been an email in the first place. Want to know where AI can actually help? The most useful applications of AI usually come from the people closest to the work.
4 - Security concerns are slowing down enterprise
Enterprise leaders force their teams to use inferior AI tools because of data privacy concerns. Look, security concerns are real, but overreactions are killing innovation. If your teams aren’t using the latest, most powerful AI tools, you’re riding a bike while everyone else is on a jet airplane. The race won’t even be close.
We’ve seen this movie before. Just like the exec who thinks the latest tech will solve all the company's problems. Just like the VP who forces a platform change because “this is what he used last time.”
Everyone forgets that tech is only as good as the strategy and ops behind it.
3 Lessons from Open AI's Head of GTM Systems - Podcast Episode
(And how you can apply them today)
I had the chance to interview Keith Jones, Head of GTM Systems at OpenAI, alongside my Humans of Martech cohost, Phil Gamache. This one was packed with sharp insights and practical takeaways.
Here are 3 lessons that stuck with me—and how you can put them into action 👇
1. Don’t start with a spreadsheet. Start with what people really want.
Keith doesn’t just collect tool requirements in a doc. He asks stakeholders to write what they need, and then explain it out loud. He uses GPT to find patterns in what people say vs. what they write. That’s where the real insights live.
✅ Actionable takeaway:
Before choosing a new tool, ask:
“What problem are we solving?”
“What are your must-haves vs. nice-to-haves?”
Then ask team members to talk through their answers. You’ll uncover the real priorities, not just what looks good on a slide.
2. AI can help you only if you have clear structure.
Everyone wants AI to save them time and automate busywork. But if your data is inconsistent, your tags are messy, or your workflows don’t make sense… AI will only scale the chaos.
✅ Actionable takeaway:
Before plugging in agents or copilots:
Standardize your data model
Use consistent tags and field logic
Fix brittle workflows
Clean structure = usable automation.
3. Want to build better systems? Get better at understanding sales.
Keith has been in the rep’s seat, and that empathy shows in how he designs tools. But this isn’t just about carrying a quota. Even if you’ve never sold, you can still build that intuition.
✅ Actionable takeaway:
If you’re in ops, sharpen your sales fluency:
Listen to call recordings
Ask reps what slows them down
Get curious about how they close deals
The more you understand their world, the better your systems will perform in the real one.
💬 One of my favorite lines from Keith:
“You don’t need more dashboards. You need clean inputs and workflows that actually make sense.”
🎧 Full episode: Humans of Martech – Ep 170 with Keith Jones
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Why is Proving Marketing ROI Still So Hard?
Top Commentary from Social
Biggest issue? The lack of SKILL to solve all of the above. Any one of these in isolation won't solve the problem. The holistic solution requires a very skilled leader. Pranav Piyush
I chose "other" but I guess it could be "incomplete data" as well. Enterprise marketers we've spoken with have shared that the "I" (investment) part of ROI is a tough nut to crack. It might not be the sexiest thing to talk about, budget management, but when the CMO/CFO starts sniffing around what ROI you've received from your marketing budget then you'll want to have answers to justify your spend. And your campaigns. See the full explanation here from the mops shark tank the other week: https://share.descript.com/view/pXObTm6KGXA. I'll let John Notman do all the showing and telling. - Thao Ngo
Not only bad or incomplete data, it's the data wrangling from various sources and departments. I am in the middle of a major attribution project and it's not easy to set up. - Adam Pereyra
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