In the new episode of AI for Business Leaders, Matt walks through the process of how he is rebuilding the data architecture.
Centralizing everything into one reasoning engine so that when AI does its job, 1+1 actually equals 10.
His framework for the build vs buy decision is one of the clearest I've heard:
Build when it's your core differentiator.
Buy when it's a commodity.
And when you're in between, bring in forward-deployed engineers who embed with your team, build the integration, and then hand it off.
We go deep on:
→ What "agentic commerce" actually means and where it's heading
→ The centralized data architecture that makes AI actually work
→ Build vs buy: the framework for enterprise AI decisions
→ Forward deployed engineers vs traditional consulting
→ 3 things every business leader should do right now with AI
Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-gtm-playbook-matt-dornfeld-on-building-commerces/id1882473492?i=1000754577529
I think this is the biggest structural opportunity in GTM right now.
Foundation Capital (Ashu Garg Jaya Gupta) published a few follow-ups from their initial article on "context graph," with the core argument that the companies that capture decision traces(the reasoning, not just the outcomes) will build the next billion-dollar platforms.
I've been building this in GTM.
Not theoretically.
In production.
On my own go-to-market.
Most teams run agents on top of Salesforce or HubSpot. That's outcomes without reasoning.
Here's the setup. I'm orchestrating all of GTM architecture from sales, marketing, pipeline, content, partnerships, and operations.
Just agents running and operating simultaneously.
Each agent owns a different function.
Each one receives the same raw inputs: call notes, emails, and meeting context.
But each one filters for what matters to its lane.
SDR agent has visibility on advertising messaging but doesn't take action.
Marketing takes the learnings from deal mechanics and apply to iteration on demand gen.
They capture the customer journey, extract what's relevant to their specific role, and update their own outputs.
Aaron described it perfectly: "It might end up as a Markdown file in a folder an agent has access to, and that helps your workflow."
Here's what I'm seeing after running this for 100+ prospect conversations:
Pattern recognition that no single tool gives you.
After ~50 calls, the system started surfacing patterns I didn't consciously see.
Which founder archetypes close fastest?
Which verticals have real budget vs. aspirational budget?
Which referral sources produce the highest-quality pipeline?
Not gut feel, traced decisions across dozens of interactions.
One call does the work of five people.
A 30 minute discovery call now generates a qualified pipeline entry with decision rationale, marketing signals for vertical targeting, content angles for thought leadership, and behavioral profiling that sharpens every future conversation.
Automatically, and the best part....the context compounds.
Every interaction makes every agent smarter, not just for that account, but across all accounts.
The 100th call is exponentially more useful than the 10th because the context graph grew.
What I'll say plainly: I think this is the biggest structural opportunity in GTM right now.
Not because agents are new. Because the context layer is new.
The old way is making crucial decisions based on a siloed system of records; the futuristic way is making crucial decisions based on connected and enriched contexts.
The teams building in that methodology today will have own reasoning that compounds and can't be copied.
That’s the strategic moat.
If you're running GTM, and you're tired of static tools but don't actually understand your business at its core, DM me or comment "context" below.
I'll send you what I've built.
