Research Director, Computer Vision and AI
Leaders
MicrosoftFeatured Vendor
AWS
Clarifai
Cogniac
Chooch
Major Players
Tencent
Matroid
MathWorks
IBM
H2O.AI
Hitachi Vantara
Contenders
IronYun
Scandit
Deepomatic
The human vision system, which gathers and interprets information through sight, remains a critical aspect as part of one’s life as both a consumer and an employee (i.e., working as part of a private business or government entity). Vision is critical to performing routine tasks like navigating roadways and sidewalks; identifying, classifying, and interacting with objects and environments; and engaging with computers and digital devices. Human vision continues to develop and be fine-tuned by technology to support an ever-increasing range of dynamic events and human experiences. Yet, as our society continues to invest in R&D to advance and deploy new technology and automation techniques, there are increasing opportunities for businesses and consumers to leverage or pair (i.e., in a cooperative or human-in-the-loop manner) human sight with computer-driven sight (referred to as computer vision [CV] or computer vision artificial intelligence [CV AI]) to take the next step in delivering improved productivity, efficiency, safety, sustainability, and inclusivity.
CV has been a strong beneficiary of academic and commercialization investments to advance the fields of deep learning– and machine learning (ML)–based approaches to AI. These advancements, which have largely occurred over the past five years, look to abstract the human intelligence schema and system to interpret unstructured data in the forms of images, videos, and sensor data (e.g., radar, lidar) through complex neural networks. To develop this neural network architecture, CV technology user organizations require massive amounts of use case–specific or even generalizable training data, as well as extensive computational resources (including GPUs, TPUs, and hardware- and software- based accelerators) to train, build, and validate models that can “learn” details and characteristics from new, unstructured visual-based inputs. This approach to solving CV AI has led to breakthroughs where computers are now able to surpass the quality and efficiency of humans for multiple discrete use cases, along with delivering differentiated benefits versus humans in the areas of scale, repeatability, longevity, attentiveness, and subjectivity (to name a few).
Although deep learning–based CV is a very new technology area, IDC has seen tremendous progress in its use by organizations of all sizes and across all verticals. This includes support for (or even potentially enabling new) business and consumer use cases that can deliver insights in the areas of:
This IDC MarketScape focuses on one aspect of the CV ecosystem, CV software platform providers. These essential vendors make up the foundation of growth and potential of CV, and they enable customers to understand, experiment, develop, train, validate, deploy, and manage CV models for a near-infinite list of potential use cases. These providers are critical to helping customers extract the complexity of working with, utilizing, and managing CV deployments, as well as helping them understand how cutting-edge AI research techniques and approaches equate ultimately to business value. In many cases, these providers offer different low-code and no-code user interface/user experience (UI/UX) options to support organizations with a mix of potential user personas ranging from AI/ML technical specialists (e.g., data scientists, ML engineers) to traditional IT personnel (e.g., developers and computer programmers) and even line-of-business users (e.g., payroll and accounting staff).
As part of this IDC MarketScape process, IDC spoke with dozens of end-user organizations that are investing in CV platform providers to help them develop and deploy applications. These organizations, which all varied in terms of CV deployment maturity, were almost universally aligned on the tangible, business benefits provided by these CV solutions, as well as (more importantly) recognized that they should have prioritized and invested in CV earlier. These conversations reinforce the need for organizations (broadly) to think through how CV can be used to improve business, consumer, and partner interactions and capabilities both at a strategic, governance level and at a specific use case level.
IDC offers the following advice to technology buyers considering CV:
This section briefly explains IDC’s key observations resulting in a vendor’s position in the IDC MarketScape. While every vendor is evaluated against each of the criteria outlined in the Appendix, the description here provides a summary of each vendor’s strengths and challenges.
After a thorough evaluation of Microsoft’s strategies and capabilities, IDC has positioned the company in the Leaders category in this 2022 IDC MarketScape for worldwide general-purpose computer vision AI software platforms.
Microsoft offers a wide range of services and tools for technology buyers looking to build, customize, and deploy CV AI across a global footprint. Microsoft’s strategy and vision begin with an ethos of providing tools, capabilities, and services that allow each user profile to access CV and AI in the manner that they want. Microsoft wraps this strategy with three main focus areas that it uses to drive current and future R&D. First, Microsoft develops solutions that continue to bring cutting-edge AI research to customers. Second, Microsoft ensures that its platform is built to support mission-critical use cases and deployments. Third, Microsoft makes responsible AI, defined as inclusive, fair, transparent, accountable, reliable, safe, private, and secure at the center of all its AI activities. Microsoft’s CV strategy to date has been extremely successful as customers are utilizing its capabilities to design, build, and deploy solutions that deliver tangible business value.
Microsoft’s current CV portfolio mirrors that of other cloud service providers, as it consists of a three- tiered strategy of Azure AI, Azure Cognitive Service, and Azure Applied AI Services. Azure AI consists of the infrastructure building blocks and frameworks that underpin all the higher-level services created by Microsoft. Azure Cognitive Services represents Microsoft’s primary CV service layer and contains a portfolio of prebuilt models (accessible via APIs) as well as an end-to-end CV model development and deployment platform for customers. Azure Applied AI Services represent areas of investment by Microsoft to build out domain or task-specific AI solutions (e.g., Azure Form Recognizer for IDP). In addition to these focus areas, the Microsoft team has ensured that its portfolio supports multiple user personas from data scientists and ML engineers to developers and line-of-business users with a low- code/no-code UI/UX.
The Microsoft team fully understands that the business value of CV comes from its ability to be integrated into business processes and applications. In addition to the extensive ecosystem of service providers and ISVs that it has enabled on its platform to help customers, and programmable APIs for developer engagement, Microsoft has worked to empower line-of-business users to integrate CV into other Microsoft products via its no-code Power Platform suite (Power Apps, Power Automate, Power BI, Power Virtual Agents).
Microsoft’s comprehensive portfolio and strong strategic direction make it a vendor of strong consideration for any organization looking to experiment, learn, or expand its use of CV and AI more broadly:
The criteria used for the selection of IT suppliers that were evaluated included the following:
For the purposes of this analysis, IDC divided potential key measures for success into two primary categories: capabilities and strategies.
Positioning on the y-axis reflects the vendor’s current capabilities and portfolio of services and how well aligned the vendor is to customer needs. The capabilities category focuses on the capabilities of the company and product today, here, and now. Under this category, IDC analysts will look at how well a vendor is building/delivering capabilities that enable it to execute its chosen strategy in the market.
Positioning on the x-axis, or strategies axis, indicates how well the vendor’s future strategy aligns with what customers will require in three to five years. The strategies category focuses on high-level decisions and underlying assumptions about offerings, customer segments, and business and go-to- market plans for the next three to five years.
The size of the individual vendor markers in the IDC MarketScape represents the market share of each individual vendor within the specific market segment being assessed.
IDC MarketScape criteria selection, weightings, and vendor scores represent well-researched IDC judgment about the market and specific vendors. IDC analysts tailor the range of standard characteristics by which vendors are measured through structured discussions, surveys, and interviews with market leaders, participants, and end users. Market weightings are based on user interviews, buyer surveys, and the input of IDC experts in each market. IDC analysts base individual vendor scores, and ultimately vendor positions on the IDC MarketScape, on detailed surveys and interviews with the vendors, publicly available information, and end-user experiences to provide an accurate and consistent assessment of each vendor’s characteristics, behavior, and capability.
IDC defines a computer vision (CV) software platform as a set of commercialized software tools and technologies that enable customers to design, train, build, validate, deploy, and manage CV artificial intelligence/machine learning (AI/ML) models. These models, when deployed, can derive data-based insights and inferences from unstructured images, videos, and/or sensor data (e.g., lidar, radar, hyperspectral).
General-purpose software platforms are defined as platforms purposely designed to support the broadest range of potential use cases. Although these platforms may contain specialized functions and integrations for a given domain, vertical, or use case, these general-purpose platforms should include capabilities that can broadly address or be applied to most, if not all, use cases.
