AI Data Is Becoming Easier to Produce. Knowing What to Do With It Isn't.
By Malin Gaertig · September 14, 2026
AI visibility measurement can tell a brand how often it appears across ChatGPT, Gemini or Claude. It cannot decide which signal matters, why a competitor is winning or what the business should do next. That distinction between intelligence and judgement is shaping the next generation of AI measurement — and it is the model behind MARK.
- —AI visibility data can show whether a brand appears more or less often, but measurement alone cannot determine the right business response.
- —Detecting brand performance across ChatGPT, Gemini and Claude is increasingly standardised intelligence work.
- —Explaining why a competitor is preferred, which signal matters and whether investment should change requires judgement.
- —MARK combines proprietary AI measurement with advisory judgement so leaders can decide what to do next.
What happens after AI visibility is measured?
Imagine your platform tells you your brand appeared more often in ChatGPT this month than last month.
What happens next? Do you increase investment? Change content strategy? Ignore it or escalate it? The measurement itself doesn't answer any of those questions.
That's why Sequoia Capital's thesis is interesting beyond venture capital. Their argument is that the next trillion-dollar company will likely be a software company masquerading as a services firm.
The framework underneath it is a distinction between intelligence and judgement.
What is the difference between intelligence and judgement?
Intelligence is work that follows a defined process. Inputs go in. Outputs come out. Once the process becomes sufficiently structured, software tends to get progressively better, faster and cheaper at performing it.
Judgement is different.
Judgement is deciding what matters. Which signal deserves action. Which competitor represents a genuine threat. Which finding changes a business decision and which one doesn't.
How does this apply to AI model measurement?
The same split is emerging in how brands are measured across AI models.
Detecting how brands are showing up across ChatGPT, Gemini or Claude is increasingly intelligence work. Defined prompts go in. Structured results come out. The process can be standardised. And standardised processes are often where automation and commoditisation begin.
The more interesting question comes afterwards:
- Why did the model prefer that competitor?
- Is the issue content, authority, product breadth, pricing signals or something else entirely?
- Does the finding justify reallocating budget?
- Does it matter at all?
Those are judgement questions.
Why AI visibility measurement needs both
Detecting how brands appear in AI models is increasingly intelligence work.
Understanding what that means for a business is judgement.
Sequoia's argument is that the biggest companies of the next decade will combine both, which is what I've chosen for MARK.
MARK shows brands how they appear across AI models, who appears instead of them and why.
I help leaders decide what to do next.
See how this is measured in the MARK product suite, or read the latest AI Commerce briefings.