Your AI has no idea when it’s wrong. Neither do you.

PLUS: 19 of 20 B2B deals are decided before you meet the buyer.

From the aibl team

Rana Gujral’s lead article asks a simple question of any AI system: what happens on the bad day? What does it do when it’s wrong, and can you undo it? His warning is that AI is moving upstream of our decisions, shaping what we notice and what we want before we’ve consciously decided anything. He argues that the skill that will matter most is verification.

Since this AI wave hit, I’ve thought of it as editorial thinking… we’re not writing all the base copy, but we need to know what to trust, stress-test, and override.

Now hold that thought next to our follow-up to last week’s article on GEO. The buyer in our second piece is doing exactly what Rana describes, by letting an AI layer move upstream of their decision. Out of 20 B2B deals, 19 go to a vendor the buyer named before the formal evaluation even started. Increasingly, an LLM helped them build that shortlist, in a research phase you’ll never see, across an estimated 100 million B2B prompts a day.

Happily, the answer isn’t an entirely new playbook. The highest-impact GEO levers turn out to be old virtues in new clothes: strong statistics, third-party validation, clear structure, and content built around the real questions buyers ask.

They’re harder to activate than paid marketing, but have always been worth the trouble. Now doubly so as LLMs reward credibility and punish shortcuts. Which is the through-line of the whole issue: whether you’re deploying AI inside your business or trying to get discovered by the AI inside someone else’s research, the winning move is to be the source that holds up.

What happens on the bad day?

Rana Gujral is the CEO of Behavioral Signals, which uses voice AI to detect intent, emotion, and risk in real time. He has been on both sides of enough AI deals to know which questions buyers ask, and which ones they should.

“A lot of responsible AI talk lives at the altitude of principles. Fairness, transparency, accountability. They’re beautiful words. They don’t survive contact with a Tuesday morning product decision. So when I think about what it actually looks like in practice, I try to push it down to the level of design choices and habits.”

“The question I almost never hear buyers ask, and it’s the one I’d lead with, is what does the system do when it’s wrong? Not how accurate is it, not what’s the benchmark score. What happens on the bad day?”

That depends on what the system assumes going in. Every model is trained on data that reflects someone’s world, not necessarily yours. When it hits your users, your edge cases, your context, it has no way of knowing it’s out of its depth.

Ask vendors to articulate those assumptions and you get silence. That’s data. And if the answer on rollback is a pause followed by “well, in practice that hasn’t really come up,” walk away. If you can’t undo an automated action, you don’t have oversight. You have hope.

Watch the full video interview:

aibl’s Research: GEO by the numbers

Last week we wrote about how the B2B selling process is changing, as LLMs draw the buyer away from your known marketing funnel into an increasingly opaque, front-loaded journey.

Out of 20 deals, 19 go to a vendor the buyer identifies before the formal evaluation begins. This part is familiar, if a bit more extreme than it was 10 years ago, but it underscores how our attention and investment are often focused way too late in the process.

AI citations happen in the most influential phase, with an estimated 100 million B2B research prompts a day across the name brand LLMs. For the first time in several decades, the buying process is getting shorter (to be fair, only by a month from a high of 11.3 months) even as buying committees continue to grow.

Meanwhile, AI isn’t replacing the vendor relationship, just compressing the research phase. As usual in marketing, the rise of a new thing doesn’t remove the need for the old one.

This new responsibility to address discoverability in LLMs is additive, because the later stage of the process still depends on being credible to human beings, even as you must be discoverable by machines.

If you go looking for “GEO” best practices you’ll find quite a lot of opinion, but relatively scant top-tier research. The good news is that the high-impact levers are likely a good fit with existing content and SEO strategy.

Product spotlight of the week

This week the aibl team has been digging into Culture Amp, an employee experience platform built around a familiar mid-market problem: collecting engagement data is easy, acting on it isn’t. Culture Amp’s own 2025 benchmark data found only half of employees see positive change after completing a survey.

The platform combines engagement surveys, performance reviews, and development planning. Culture Amp says AI Coach is now embedded across all of it, turning survey and review data into manager-level guidance, action plans, and draft communications rather than just another dashboard.

Support tiers run under 200 employees, 200 to 999, and 1,000-plus for enterprise. It also integrates with existing HRIS platforms like Workday and BambooHR, sitting alongside them instead of replacing them.

Onboarding typically runs several weeks. Pricing isn’t published; it’s handled through a sales conversation. The harder question is whether managers change what they do once feedback has been turned into guidance, whatever the platform. That’s usually where mid-market people teams get stuck.

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