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Announcing Gartner’s New Case-Based Research on Machine Vision

By Tuong Huy Nguyen | August 18, 2021 | 0 Comments

In July, we published our first in a series of notes (full reports available to subscribing Gartner clients) from our machine vision case-based field research.

Machine vision is a subset of computer vision that is the practical application of computer vision to all industries. Computer vision is a process that involves capturing, processing and analyzing real-world images and videos to allow machines to extract meaningful, contextual information from the physical world. There are numerous computer vision applications, including machine vision, optical character recognition, image recognition, pattern recognition, facial recognition, object detection and classification.

With the machine vision case-based field research, we reached out to over 130 tech providers across manufacturing, healthcare, automotive, agriculture, and logistics and supply chain. As a result, we examined nearly 50 tech providers  (see figure below for sample) and over 100 adopters. Here are some of our major findings:

  • The maturation of machine vision products has made it more affordable and accessible to technology buyers. In turn, it’s promoting new business models and generating compelling business value for some early adopters, and providing new sources of differentiation and competitive advantage to users.
  • Machine vision is delivering outstanding business value to some early adopters, but scaling solutions is often challenging due to the requirement for a high level of customization, iterative approaches and solid service support.
  • Lack of expertise and technical knowledge limits machine vision adoption within and between organizations
  • Usability and usefulness are key features to empowering workers with machine vision tools
  • The most prevalent business values recognized by adopters of machine vision are task automation and augmentation or replacement of parts of the workforce, followed by operational efficiency and data analytics.


Here are our Machine Vision Tech Innovators for 2021:

Tech Innovators in Machine Vision
Technology, product and market innovations in machine vision



Emerging Technologies: Top Use Cases in Machine Vision examines the top emerging use cases (across the industries mentioned above) by volume and includes illustrative client stories.

Emerging Technologies: Machine Vision Adoption Patterns Driving Business Values  quantifies our findings and analyzes patterns in how machine vision is delivering business value to end-user organizations.

Emerging Technologies: Tech Innovators in Machine Vision – Supply-Side profiles three vendors whose innovation is based on the availability of new technologies and the price/performance of hardware and software capabilities.

In Emerging Technologies: Tech Innovators in Machine Vision – Business Models and Applications, we profile four vendors whose innovation related to specific customer requirements and tailored to address customer pain points.

The four vendors profiled in Emerging Technologies: Tech Innovators in Machine Vision – Demand-Side show innovation through business models and/or application of technologies in this case-based field research

A big THANK YOU to Nick Ingelbrecht, Kiyomi Yamada and Michael Porowski who conducted the 6-month case-based field research effort on machine vision. Mr. Ingelbrecht is the lead author for the Top Use Cases analysis. Ms. Yamada is the lead author on the Adoption Patterns analysis.  Mr. Porowski is the lead author for the upcoming Provider Patterns analysis.  The team’s dedication to quality is truly remarkable.

We plan on delivering additional machine vision articles in 2021 generated from our case-based field research effort; diving into multiple topics including edge CV, product delivery for MV solutions, and opportunities for MV providers.

As always, your comments/feedback are much appreciated.

The Gartner Blog Network provides an opportunity for Gartner analysts to test ideas and move research forward. Because the content posted by Gartner analysts on this site does not undergo our standard editorial review, all comments or opinions expressed hereunder are those of the individual contributors and do not represent the views of Gartner, Inc. or its management.

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