In my recent CMSWire article, I wrote about how AI search is becoming the new gatekeeper in B2B buying — how buyers are using tools like ChatGPT and Perplexity to research vendors before ever visiting a website, and how the buying decision is increasingly happening before you know a prospect exists. If you haven’t read it, I’d encourage you to start there. Because what I want to explore here goes a step further.
I’ve been thinking more deeply about the full AI search picture — and there’s an important layer that many aren’t addressing yet. There are two distinct dynamics at play in the AI search landscape, and understanding both changes what you need to do about it.
How AI Forms a View of Your Company
The first dynamic is passive. AI search tools synthesise everything that has ever been said about your company — G2 and Capterra reviews, Reddit and LinkedIn discussions, analyst commentary, community forums, support interactions that generated public feedback. It distils all of that into a characterisation that gets surfaced to buyers during their research. You didn’t do anything to create it. It’s just there, forming a view of you whether you’re paying attention or not.
The second dynamic is active — and this is where things get more complicated. Savvy companies are already figuring out how to engineer their AI visibility. They’re optimising content specifically for LLM indexing, seeding positive characterisations across the sources AI draws from, essentially training the data in their favour. This is GEO as a deliberate strategy, and it’s becoming more sophisticated by the month.
Generative Engine Optimization (GEO) — the practice of ensuring your brand surfaces favorably in AI-generated answers — has been framed almost entirely as a marketing discipline. Get your content structured correctly. Build authority in the right publications. Manage your reviews. Earn citations.
But framing GEO purely as a marketing discipline misses the most dangerous part of the equation.
Both dynamics matter. But the intersection of the two is where the real risk lives.
The GEO Trap
Here’s what I keep circling back to. A company invests in GEO strategy. They get good at it. AI starts recommending them in a compelling way that resonates with those searching — responsive, innovative, strong on implementation, well-regarded for customer support.
A VP of Customer Success is evaluating vendors. She asks ChatGPT for a shortlist. One company stands out based on exactly those descriptors. She clicks through with high expectations.
Support takes 48 hours to respond. Integration documentation is incomplete. G2 reviews confirm what she’s already experiencing. She closes the tab and goes with a competitor.
That company didn’t just lose a deal. They engineered the conditions for a faster, higher-stakes rejection than they would have faced without a GEO strategy at all. The AI set expectations their operations couldn’t meet. And because those expectations were set high, the trust collapse was immediate and decisive.
This is the GEO trap: the better you get at optimising for AI visibility, the higher the bar you’re setting for every buyer who arrives. If your operations can’t clear that bar, you’ve accelerated your own exposure, not avoided it.
AI Is Already Running a Continuous Audit of Your Business
Whether you’re actively investing in GEO or not, AI is already forming a view of your company. McKinsey found that only 16% of brands systematically track what AI says about them. That means 84% of companies have no idea what AI is telling their prospects before they arrive.
Think about that for a moment. Every support interaction that generated a public response. Each implementation experience that got discussed in a customer community. Every moment where customer expectation met operational reality and something broke. All of it is being indexed, synthesised and fed back to buyers as a characterisation of who you are.
AI-generated reputation isn’t a marketing construct. It’s an aggregated VoC signal — the accumulated voice of everyone who has ever interacted with your company and said something about it publicly. AI is auditing your business. But, do you know what it’s finding, and whether you’re doing anything about it?
The Audit and Remediation Cycle
So how do brands get ahead of this? I think about it in three stages.
The first is knowing where you stand.
Search for your company across ChatGPT, Perplexity, Google AI Mode and Claude. Ask the questions your buyers are asking. Find out about your support responsiveness. Ask how your onboarding compares to competitors. Ask what customers say about working with you. The characterisation you find is the reputation you’re selling from, whether you know it or not. I’ve done this exercise with clients and the results are frequently surprising — both in terms of what AI gets right and what it gets wrong. On the flip side, if AI is giving you glowing reviews, can your operations back that up? If not, you have uncovered gaps that create opportunities to align your reality with AI.
The second is cross-referencing what you find with your internal VoC data.
Where AI-generated reputation diverges from your internal sentiment data, you have an operational gap that buyers are already discovering. Where it aligns with internal feedback you’ve been aware of but not acted on, you have a known problem that is now actively costing you deals. In both cases, the path forward is the same: route the insight to the operational teams who can fix what’s being surfaced. This is not a marketing problem. It’s a cross-functional operational problem that requires the same disciplines as any serious CX transformation — clear ownership, connected teams, and a mechanism for turning insight into action.
The third stage is what I’d call retraining the data.
Here’s the important thing to understand: AI characterisations aren’t static. They update as the underlying ecosystem changes. If you fix your onboarding experience and a new cohort of customers starts talking positively about implementation in public forums, that signal gets indexed. If you improve support response times and that improvement shows up in updated G2 reviews, AI picks it up. The feedback loop is real — which means genuine operational improvement, communicated and visible, is how you change what AI says about you over time. You can’t fake your way to a better AI reputation long-term. The operations have to actually change.
Getting the Sequence Right
This is where most GEO strategies get the sequence wrong. They start with visibility and assume operations can catch up. In a world where AI is setting buyer expectations before the first click, that sequence is backwards.
The right sequence is: audit what AI says about you, fix the operational gaps it surfaces, then invest in GEO optimisation to amplify the improved reality. GEO built on a foundation of genuine operational quality creates a compounding advantage — better operations generate better organic reputation signals, which produces better AI characterisations, which sets expectations that your operations can meet.
GEO without that foundation is a liability. You’re engineering a faster path to your own rejection.
Where to Start
If you’re a CRO or COO reading this and wondering where to begin, the answer is simpler than the problem: start with the audit. Spend 30 minutes asking AI tools the questions your buyers are asking about you. What you find will tell you exactly where to focus.
If the gap between what AI says and what you deliver is small, you’re in good shape — focus on visibility. If the gap is significant, you have an operational alignment problem that no amount of GEO strategy will fix. That’s where the real work is. And it’s where getting ahead of this now, before AI-mediated buying becomes the dominant research mode for your category, matters most.
The companies that understand this — and move on it — will have a durable advantage that’s very hard to replicate. Because it’s not built on content optimisation. It’s built on operational reality.
Have questions about how to audit your AI-generated reputation or build the operational foundations to compete in an AI-mediated buying environment? Please contact us.
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