Yesterday at Gartner Supply Chain Symposium I sat in on a session titled Magic Quadrant Insights for SCP Solutions 2026. Most of the hour walked through the two Supply Chain Planning Magic Quadrants that Pia Orup Lund‘s team produces. The closing minutes were different. She stepped back from the specific market and offered broader observations on how Magic Quadrants are built and how buyers should read them. Those remarks apply to every MQ, not just to SCP, and they deserve a wider audience.
I’m writing a longer note about Orup Lund’s talk for the Analyst Observatory at the University of Edinburgh Business School, where we run independent empirical research on the analyst industry. The Magic Quadrant remains the single most influential analyst deliverable in B2B technology buying. Yet our work with buyers keeps producing the same finding: the MQ is read more often than it is read well.
Pia made five points that travel beyond Supply Chain Planning. Each one names something AR teams and buyers should already know. Most do not act on it.
1. The Magic Quadrant is an opinion, not a dataset
Pia’s framing was direct: “the Magic Quadrant represents the Gartner opinion based on data.” Note the order. Data feeds the opinion. The opinion is the deliverable.
This matters for AR teams who treat an MQ submission as an exam to be passed by accumulating the right evidence. Evidence is necessary. It is not sufficient. The data Gartner collects — vendor submissions, demos, customer references, briefings, inquiries, external sources — informs an analyst opinion that is then peer-reviewed across the firm. A vendor who supplies excellent evidence and a weak narrative will still score against the narrative the analyst already holds.
The lesson for AR teams: an MQ engagement is a year-round campaign of opinion formation, not a submission exercise that runs for two weeks.
2. Standards rise every year
In Pia’s words, “the standards for top performance rise each and every year, so the vendors have to continuously improve and evolve to stay ahead.”
Many Leaders fall out of the Leaders quadrant not because they got worse but because they failed to get better at the rate the market demanded. We see this clearly in Observatory data. Vendors who hold Leadership for a decade are not the ones running the smoothest briefings. They are the ones who shift their product narrative each year to match where the analyst already sees the market moving.
If your AR strategy for next year looks like your AR strategy for this year, you are losing ground.
3. The vendor review checks facts, not the analysis
Pia drew a sharp line. Vendors get an external review “where vendors check for factual inaccuracy, not checking the analysis as such, we don’t ask for that.”
This is the single most misunderstood part of the MQ process. AR teams routinely use the factual review as a backdoor to dispute scoring, re-argue strengths, and lobby for repositioning. Pia’s point lands flat: that is not what the review is for. Use the factual review for facts. If you disagree with the analysis, Gartner has a separate escalation path — “they can escalate this to us, basically try to prove us wrong” — and that is the route for substantive challenge.
Conflating the two paths wastes vendor goodwill and analyst patience. It also weakens the moments when a vendor has a genuine factual correction to make.
4. The most-read document in B2B technology is the most misread
Pia: “Magic Quadrants are very popular, very well-known documents. It’s the most read document at Gartner by far, but unfortunately, it’s also oftentimes misunderstood.”
A Gartner analyst saying this in front of a room of vendors and end-users is not a small admission. The misreading happens at three levels. Buyers treat the Leaders quadrant as a shortlist. AR teams treat dot movement as the sole measure of success. Sales teams treat any MQ as the current MQ for the prospect’s market — and they are often wrong on both counts.
Pia’s correction was the one buyers most need to hear: “it’s very unlikely that all of the vendors in the previous quadrant will be relevant for your business, and unlike the other vendors that are not in the Magic Quadrant, that could be very relevant.”
Read that twice. A Gartner analyst, on a Gartner stage, telling Symposium attendees that the right vendor for them may not be on the MQ at all. The MQ is a filter built around a generic ideal-type buyer in a defined market segment. Your business is not generic. Niche Players exist by design — Pia called this “a strategic choice” — and may be the better fit for a specific industry, region, or capability requirement.
5. Functionality is the obvious axis. It is rarely the deciding one.
Pia closed by listing what buyers should weigh alongside functionality: performance, user experience, partner networks for implementation, choice of consultancy, and total cost. Each of these is captured somewhere in the MQ scoring. Each is then aggregated into a single dot position. By the time it reaches the published graphic, the trade-offs that matter most to your buying decision have been compressed away.
This is why our research keeps finding that the heaviest users of analyst research are not the buyers who rely on the dot. They are the ones who use the dot as an entry point and then ask the analyst — in inquiry — the questions the dot cannot answer: how does this vendor perform under our specific constraints, with our existing systems, at our scale, with our partner ecosystem?
What this means for AR teams and for buyers
Pia’s wider points are not a critique of the Magic Quadrant. They are an honest description of what the MQ does and does not do. The MQ is a structured, peer-reviewed, opinionated summary of a market at a moment. It is built well. It is read badly.
- AR teams that internalise her five points will run better programmes: campaigning year-round on the narrative, separating factual review from analytical challenge, accepting that standards rise faster than briefings do, and resisting the temptation to treat dot position as the only outcome that matters.
- Buyers who internalise them will make better decisions: treating the MQ as one input among several, looking past the Leaders quadrant when their use case warrants it, and using analyst inquiry to extract the texture the graphic cannot show.
Pia closed her session by offering to discuss “some of the dirty secrets about the business I wasn’t allowed to say on stage.” The remarks above were the ones she did say. They were the most valuable part of the hour.
Duncan Chapple is analyst relations lead at Elisa Industriq and volunteers as a co-director of the Analyst Observatory at the University of Edinburgh Business School.
