Note sull'episodio
B2B buying signals are compressing — and it's not because buyers care less, it's because they're doing more of the work somewhere you can't see. In this episode, the host and Jessica dig into how LLMs have changed the shape of the buying cycle: prospects are showing up to calls having already asked ChatGPT, Copilot, or Claude to compare your product against a competitor's. They're building their own battle cards before your sales team ever gets a chance to hand one over. The conversation traces what that does to the signals B2B marketers have always relied on — website visits, content downloads, form fills — and why those signals are getting both scarcer and less reliable as more research moves off-property. It also touches on why GEO and AEO (optimizing for how generative engines represent your brand) are becoming as important as traditional SEO.
The second half of the episode tackles a debate coming up constantly in B2B orgs right now: build vs. buy, specifically for AI-powered tools. Jessica makes the case that the "just build it ourselves" instinct badly underestimates what off-the-shelf software was actually doing under the hood. A traditional UI wasn't just a UI — it was a deterministic guardrail system: who can click what, who has access to which data, what actions are and aren't allowed. Replace that UI with a prompt-driven AI layer and you've traded a bounded, testable system for a probabilistic one that will always produce an output, even a wrong one, and will keep trying rather than fail cleanly like traditional software does (no clean "404" for AI).
That shift changes what QA and UAT even mean. Traditional software testing covers discrete, known use cases. Prompt-based AI has to hold up against unlimited phrasings of the same intent — "improve my campaign" said a hundred different ways — which means the real cost of building your own AI tooling isn't the initial build, it's the ongoing tuning, refining, and governance required to keep the model inside the boundaries your business actually needs. Jessica's central point: whether you build or buy, you still have to define your own governance — who approves what, what "QA'd" means at your company, what data access looks like — and skipping that step is where AI implementations go sideways. She closes with a sharp analogy comparing model drift mid-conversation to cutting a kid's hair during COVID lockdown: once it drifts a little off, there's no correcting your way back — you start over.
If you're evaluating build-vs-buy for any AI-powered MarTech capability, or trying to figure out why your funnel signals feel thinner than they used to, this one's worth the full watch.