19 Aug 2025 3 min read marketing technology

Beyond the Gartner Magic Quadrant

Rethinking the Quadrant / Part 3: What buyers really trust now

The Gartner Magic Quadrant has been a familiar reference point for most of my career, and if you are reading this, your career, too. It condenses months of evaluation into a neat two-axis diagram where vendors are split into sections labeled Leaders, Visionaries, Challengers, and Niche Players. It’s comforting in its simplicity, and funnily enough, complimentary wherever you find yourself on it. But as I’ve learned from working both inside companies and as a consultant, that neatness is part of the problem.

As we have seen, excluding some cultures, real buying decisions rarely happen in a vacuum. They’re shaped by hallway conversations, Slack threads, and increasingly, whatever AI puts in front of us when we start our research. And here’s where I think the buying game is changing.

The Gartner Magic Quadrant under pressure

Gartner’s process still carries weight, I will have to admit that. The analyst interviews, vendor briefings, and reference checks all add rigor to an already established and trusted process. But in Martech, especially in areas like API-led platforms or composable architectures, change moves faster than the update cycle, and analyst firms seem to be struggling. By the time the latest quadrant lands, a vendor might already have launched three major features that never made it into the assessment.

I’ve also witnessed the complete disconnect between a vendor’s quadrant position and how it actually performs on the workfloor. Some platforms sitting in the “Leader” box are barely used for the capabilities that supposedly earned them that spot. Meanwhile, others that practitioners rely on daily for orchestration or identity resolution might scarcely register on the chart or, if I can be brutally honest, remain hidden in the shadows.

AI as the first analyst, and why that matters

Like it or not, AI is now acting as the first analyst in many buying processes. I’ve tested this myself, ask a large language model to “recommend a CDP” based on a set of requirements and existing platform information, and it will pull from public documentation, blog posts, case studies, and, increasingly, review sites. This means your shortlist might already be shaped before a single human analyst has a say.

The trouble? AI is only as good as its source material. Peer reviews can be gold, when they’re genuine, detailed, and recent. But I’ve been in this industry, including that of user experience research, long enough to know that reviews are often incentivized, coached by customer success teams, or written at the honeymoon stage of a deployment. That’s not a full picture. And definitely not the ‘picture’ you should base your investments on.

Review gravity and the risk of shallow summaries

Review gravity, the weight that volume, recency, and specificity carry, can determine how AI describes a vendor. A thin review profile produces a shallow AI summary, which in turn lowers the odds of being shortlisted. This can sideline solid products simply because they didn’t generate enough “review noise.”

And let’s be honest, incentivized reviews aren’t going away, no matter how subtle. They’re part of the marketing playbook now and will be pushed to the frontline faster than you can say “dark pattern marketing sucks”. Which is why I tell clients: treat AI summaries as an indicator, as an unveiling of strategies and solutions that could potentially work for you. Do not treat it as a verdict.

From quadrant to evidence pack

For me, the fundamental necessary change is moving from a single reference point, whether that’s a quadrant or an AI output, to an evidence pack. My advice, that I want to share with you, is that if you find yourself with a Martech use-case that needs solving:

use analyst insights for the macro view
balance those insights with peer and practitioner feedback
use partner ecosystem checks for integration confidence
demand an effective proof of concept and test performance with real data

It’s in connecting these dots that real confidence emerges.

So yes, keep the Gartner Magic Quadrant in your toolkit if you feel compelled to do so. But remember it’s just one lens. AI can help surface options quickly, but human expertise, people who’ve seen these tools succeed and fail in the wild, is what keeps you from making a costly mistake.

Choosing solutions to serve your use-cases is difficult. Today’s technologies are challenging enough to operate, let alone choose. Add on to that internal complications, such as siloed teams and no management support, and you find yourself in a predicament you find difficult to control.

And if that sounds like a pitch for why you should have someone like me in the conversation… well, maybe it is. Thanks for following a long in the series. If you would like to connect with me, you can find me on LinkedIn.

Read the other parts in this Rethinking the Quadrant series

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