When the Shortlist Becomes the Market

For many large organisations, the Gartner Magic Quadrant has become one of the first reference points in a supply chain planning technology selection.

That is understandable. The market is complex, implementations are significant investments, and buyers need independent ways to reduce risk. Gartner provides a structured framework for comparing established vendors on both their ability to execute and their completeness of vision.

But there is a problem.

The Magic Quadrant is not the supply chain planning market.

It is a deliberately filtered view of that market. And as supply chain technology enters a period of unusually rapid change, driven by AI, machine learning, agentic technology, optimisation and new approaches to decision intelligence, the distinction matters more than ever.

The danger is not that the Magic Quadrant contains the wrong vendors. The danger is that buyers mistake the vendors it contains for all the vendors worth considering.

The selection process starts before the evaluation does.

There is a common misconception that smaller supply chain planning vendors are excluded from Gartner’s Magic Quadrant because they fail to meet a minimum turnover threshold. For Gartner’s current Supply Chain Planning Magic Quadrants, that is not the simple issue.

In 2026, Gartner publishes separate Magic Quadrants for Process Industries and Discrete Industries. The Process Industries report, published in March 2026, does not appear to be defined simply by a minimum revenue threshold. It does, however, have significant inclusion criteria.

A vendor must offer a stand-alone SCP solution with a broad range of capabilities, including collaborative demand planning, constraint-based multi-enterprise supply planning, AI-driven planning and decision automation, scenario management, and manufacturing or capacity planning. It must also show a meaningful geographic presence and relevant customer scale.

Those are perfectly understandable criteria for Gartner’s purpose. They help establish that a vendor has breadth, presence and evidence of adoption.

But they also mean something important for buyers: a company can have excellent technology, deep expertise in a particular industry and proven enterprise deployments, and still sit outside the Magic Quadrant.

Gartner itself makes this distinction clear in its public guidance. Vendors are selected on factors including market presence, product capabilities and customer interest, and exclusion does not mean a vendor is not viable or competitive. Gartner also says the Magic Quadrant should not be used as the sole vendor-selection tool.

Yet in practice, that distinction can disappear.

The self-reinforcing shortlist.

Imagine the typical large-enterprise selection process. A transformation programme begins. A consultancy, procurement team or project group needs to establish a longlist. Someone reaches for the Magic Quadrant.

The familiar names enter the RFP. A smaller specialist vendor does not.

Nothing necessarily happens because the specialist’s technology was rejected. It was never evaluated.

The consequences can become self-reinforcing. Enterprise visibility helps vendors enter major evaluations. Winning those evaluations builds customer numbers, market presence and references. Those factors increase analyst visibility and make future inclusion more likely. Inclusion then increases the likelihood of appearing in the next enterprise shortlist.

Conversely, a highly capable vendor outside that cycle may find it substantially harder to generate the very market presence that analytical inclusion methodologies seek to measure.

This raises an important question: when market visibility is one of the signals used to identify important vendors, can the selection mechanism inadvertently reinforce the visibility it measures?

That question becomes particularly significant during periods of technological disruption.

Innovation does not always begin with the largest installed base.

Large incumbent vendors bring considerable advantages: resources, established customer communities, broad functional coverage, global support structures and extensive implementation ecosystems.

But scale is not synonymous with innovation.

Some of the most interesting work in AI, machine learning, advanced optimisation and decision intelligence is also being undertaken by smaller and more specialised technology companies. Gartner itself describes the current SCP landscape as spanning large established technology companies through to agile, privately funded software firms.

That creates an interesting tension. The characteristics an enterprise understandably seeks when reducing implementation risk, such as scale, geographic coverage, a substantial installed base and market recognition, are not necessarily the same characteristics that identify where the next generation of technology will emerge.

A younger or specialist vendor may have fewer customers while possessing unusually deep intellectual property in one area. It may not maintain offices across multiple continents because cloud delivery no longer requires the physical footprint that enterprise software once did. It may have concentrated deliberately on several complex customers rather than building a large-volume business.

None of those attributes automatically makes it better. But neither should they automatically prevent it from being evaluated.

We have experienced the blind spot ourselves.

At Orchestr8, this is not an abstract argument. We were recently brought into a supply chain planning selection for a major oil and gas organisation at a very late stage in the process.

The fit quickly became apparent. Orchestr8 has substantial experience in the sector and a long history of supporting sophisticated planning environments. The customer’s reaction was revealing: they questioned how a vendor with that level of relevant experience and capability had not appeared in the Magic Quadrant.

That question captures the problem. We had not initially been assessed and found unsuitable. We had largely been invisible to the original selection mechanism.

Had we not entered the process through another route, that organisation could have completed its evaluation without ever seeing a potentially strong alternative.

There is also a useful historical precedent. In Gartner’s 2022 Supply Chain Planning Magic Quadrant research, Orchestr8 appeared among the Honourable Mentions. Gartner noted that Orchestr8 customers were deployed across 27 countries and multiple manufacturing and distribution industries, but that the company had not met the report’s market-momentum inclusion criterion.

That is not an argument that Orchestr8, or any individual vendor, is entitled to a place in a Magic Quadrant. It illustrates something more important: exclusion from the Quadrant and suitability for a particular enterprise requirement are two entirely different questions.

AI is making the economics more important too.

There is another reason enterprises should widen their field of view. The economics of supply chain planning technology are changing.

Gartner’s own Predicts 2026 research for supply chain planning warns that planning leaders face rising technology-solution total cost of ownership as advanced automation and generative AI reshape the market.

We are also seeing new commercial models emerge around AI. SAP, for example, makes some AI-assisted IBP capabilities available through its Joule Premium for Supply Chain Management package and AI Units. Kinaxis has announced next-generation pricing that expands usage-based pricing to reflect growing AI usage, introducing Maestro Activity Units covering areas including AI tasks and automations, scenarios, recalculations and data exports.

This does not mean these pricing models are unreasonable. AI carries compute costs and can deliver considerable value.

But it does mean buyers should be asking a broader question. If sophisticated planning capabilities are becoming more expensive, and if AI introduces additional consumption or premium licensing layers, can enterprises still afford to restrict meaningful competition to a handful of familiar platform vendors?

For very large organisations in particular, even relatively small differences in enterprise software pricing can translate into substantial amounts over a multi-year programme. Greater choice matters.

Use the Magic Quadrant, but do not let it become the market.

None of this is an argument for abandoning Gartner. Quite the opposite. The Magic Quadrant can provide an extremely useful assessment of established vendors. Gartner’s accompanying Critical Capabilities research goes further into functional fit, including AI planning and decision automation, scenario management, architecture and other specific SCP capabilities.

The mistake occurs when an analytical tool designed to inform selection becomes the boundary around selection.

A better procurement approach would use the Magic Quadrant to identify established candidates and deliberately create an innovation path alongside it.

Every significant SCP evaluation could reserve places for credible vendors outside the Quadrant. Those companies should then face the same serious scrutiny as everyone else: architecture, scalability, security, functional capability, implementation evidence, customer references, financial viability and measurable outcomes.

They should not receive an easier evaluation because they are small. But they should receive an evaluation.

For organisations looking at AI-enabled supply chain planning in particular, the longlist should also ask a different set of questions. What can the technology do today? What is genuinely native rather than roadmap? How quickly can it be deployed? Can users interrogate plans and run scenarios directly? How transparent are the AI and optimisation outputs? What will those capabilities actually cost at enterprise scale?

Those questions may lead buyers back to the familiar vendors. They may also uncover something better suited to the problem.

The market is moving faster than the shortlist.

The enterprise technology industry has spent decades developing methods to reduce vendor risk. That discipline remains important.

But there is another form of risk that receives far less attention: the risk of never evaluating the technology that could have produced a better outcome.

In a relatively static market, that may matter less. In a market being reshaped by AI, machine learning, optimisation and agentic technologies, it matters enormously.

Analyst recognition can tell a buyer a great deal about a vendor’s market position, execution and maturity. It cannot tell them that every important idea sits inside the Quadrant.

The most sophisticated enterprises should therefore treat analyst research as an input to discovery, not the end of discovery.

Because when the shortlist becomes the market, innovation outside the shortlist becomes invisible. And ultimately, it is the buyer who loses.