A few years ago, I tried to implement intent-based GTM at a high-growth company. The idea was right. The timing was right. But the execution failed.
And I want to be honest about why.
We had access to the right data.
We had executive buy-in.
We even had a playbook.
But we never got past the first loop.
The Vision Was Smart. The System Wasn’t Ready.
After seeing a 6Sense demo, I imagined a signal-driven GTM motion:
- Detect intent signals
- Trigger outbound automatically
- Prioritize intelligently
- Show up with timing, context, and relevance
It was supposed to feel like GTM with eyes and ears.
But in practice, it felt like friction.
Hiring the right Demand Gen leader took months.
Budget approvals dragged.
Campaign data was siloed across teams.
No one owned the system end-to-end.
When we finally launched, reps didn’t trust the leads.
They didn’t know why those accounts were surfaced.
And without trust in the why, they ignored the what.
The result? No iteration. No signal. No lift.
The Real Killer: Organizational Drag
Everyone had good intentions.
But the system couldn’t adapt quickly enough.
Sales didn’t know what marketing was doing.
Marketing didn’t know what was landing.
RevOps was stuck reconciling reporting gaps.
What we were trying to build was right.
But the coordination tax was too high.
The feedback loops were too slow.
That’s what killed it.
Not the strategy.
The lack of speed.
What I’d Do Differently Today
Now, I’d build the same idea using agents.
Here’s how I’d approach it:
- Claude or GPT detects accounts showing intent
- An agent fetches enrichment data from Clay
- Another agent scores prioritization, with feedback from a Human-in-the-Loop
- Outreach is triggered via prompt-to-tool logic
- Feedback loops update shared memory
And most importantly: the why is embedded in the interaction. No more trust gaps. No more systems drift. No more "wait for MarketingOps or RevOps."
The whole thing could be live in 48 hours.
In fact—we just did it.
LIO: What Took Months, Now Takes Days
We built LIO—LLM Inference Optimization Index—in under 72 hours.
ChatGPT diagnosed an API issue. Claude implemented the fix. The agents talked to each other. The system learned. We shipped.
The same concept that would have taken months to build inside a traditional GTM org now lives as a working product—because we removed the friction.
We didn’t need a marketing ops team.
We didn’t need weekly standups.
We didn’t need buy-in from 6 different stakeholders.
We just needed:
- A clear signal
- Agentic memory
- A tight feedback loop
- And enough velocity to make the system evolve
The GTM Shift Has Already Happened
If I tried to run that old playbook today, I’d fail faster.
Because the environment has shifted.
You can’t win with strategy alone anymore.
You need execution speed.
Not just SDRs and sequences.
But systemic feedback loops that learn and adapt.
That’s the shift to agentic GTM.
And it’s already happening.
If you want to see where your product stands across OpenAI, Claude, Gemini, and Perplexity—
👉 lio.inflect-ai.com
Because if the models don’t see you, your market won’t either.