Global Embedded AI Market Size, Share & Industry Trends Analysis Report By Vertical, By Data Type (Numeric Data, Sensor Data, Image & Video Data, Categorical Data, and Others), By Offering, By Regional Outlook and Forecast, 2023 - 2030

Global Embedded AI Market Size, Share & Industry Trends Analysis Report By Vertical, By Data Type (Numeric Data, Sensor Data, Image & Video Data, Categorical Data, and Others), By Offering, By Regional Outlook and Forecast, 2023 - 2030

The Global Embedded AI Market size is expected to reach $22.4 billion by 2030, rising at a market growth of 13.8% CAGR during the forecast period.

The manufacturing sector benefits considerably from embedded AI's intelligent automation, process optimization, and predictive analytics. Thus, the manufacturing segment acquired $514.9 million revenue in 2022, as the creation and implementation of embedded AI systems in manufacturing have become more straightforward because of developments in AI algorithms, deep learning, and machine learning methods and help organizations save and control labor expenditures. Additionally, to enhance patient outcomes and save expenses, embedded AI devices are being utilized in the healthcare sector. Due to this, the healthcare & life science segment is poised to register approximately 1/5th share of the market by 2030 For instance, medical gadgets that analyze patient data using AI algorithms may assist clinicians in coming up with more precise diagnoses and better treatment options.

The major strategies followed by the market participants are Partnerships as the key developmental strategy to keep pace with the changing demands of end users. For instance, In April, 2023, Siemens collaborated with Microsoft, to help industrial organizations in simplifying workflows, overcoming silos, and collaborating in more ways that speed up customer-centric innovation. Moreover, In June, 2023, IBM entered into an expanded partnership with Adobe to improve stakeholder visibility, accelerate task completion, maximize collaboration, and maximize innovation across all design and creative projects.

Based on the Analysis presented in the KBV Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Market. In June, 2023, Google Cloud extended its partnership with HCLTech to develop combined solutions that enable clients to unlock the value of data and realize the full potential of generative AI. Companies such as Qualcomm, Inc., Oracle Corporation, Siemens AG are some of the key innovators in the Market.

Market Growth Factors

Increase in demand for energy-efficient and powerful processors

More substantial business potential is created by increased demand for more potent and energy-efficient processors that can handle complicated AI algorithms. AI algorithms are becoming more complicated and resource-intensive. Therefore, there is a rising requirement for processors which can effectively manage these demands. For embedded AI solution providers, the need for more potent processors, like high-performance CPUs, GPUs, and specialized AI accelerators, creates chances to deliver cutting-edge hardware. Suppliers should concentrate on creating sophisticated processors, edge computing capabilities, energy-efficient solutions, and collaborations to capitalize on the expanding market need and supply high-performance embedded AI solutions that satisfy customers' changing expectations.

Increasing need for autonomous, intelligent systems to provide an individualized experience

The market adoption of these solutions will be boosted by the growing requirement for improved technologies that can provide consumers tailored and adaptable experiences. The incorporation of AI capabilities into different embedded systems is the result of the need for tailored experiences. Using these solutions, devices and apps may evaluate user information, preferences, and behavior to provide personalized recommendations, suggestions, and reactions. This increases user engagement and happiness. By enabling devices and systems to comprehend user preferences, adapt to shifting circumstances, make wise choices, and provide customized experiences, these solutions ultimately increase user pleasure and stimulate market expansion.

Market Restraining Factors

Insufficient computing power and model optimization

Many times, resource-constrained devices with restricted processing speed, memory, and energy are used by embedded AI systems. The performance of AI algorithms may be constrained by insufficient processing resources, resulting in longer inference times, decreased accuracy, and a less satisfactory user experience. The adoption of these solutions is hampered when computational constraints prevent AI models from successfully running on embedded devices because they may not fulfill the performance standards of the intended applications. Overcoming these obstacles will allow the deployment of more potent and effective AI applications on resource-constrained devices and speed up the acceptance of embedded AI solutions in numerous fields as the market continues to develop in these areas.

Vertical Outlook

By verticals, the market is bifurcated into BFSI, IT & ITeS, retail & e-commerce, manufacturing, energy & utilities, healthcare & life sciences, transportation & logistics, media & entertainment, telecom, automotive, and others. The proliferation of connected devices, the Internet of Things (IoT), and the rising demand for high-speed data services all contribute to a significant increase in data traffic in the telecom sector. By intelligently analyzing and selecting network resources, lowering congestion, and improving overall network performance, embedded AI may assist manage and optimize this data flow.

Data Type Outlook

On the basis of data type, the market is segmented into sensor data, picture and video data, numeric data, and categorical data. The sensor data segment acquired a substantial revenue share in the market in 2022. Embedded AI may use sensor data to provide customized experiences. Fitness trackers, for instance, gather information on heart rate, sleep habits, and activity levels to provide individualized health and wellness suggestions. Integrating sensor data with embedded AI offers various advantages in various fields, from increased operational efficiency and cost savings to better safety and tailored experiences, all of which are important drivers of the market development in this niche.

Offering Outlook

Based on offerings, the market is segmented into hardware, software, and services. The hardware segment registered the highest revenue share in the market in 2022. In order to implement AI applications on embedded systems, hardware is crucial since it offers the necessary processing power and specialized abilities. It lets devices to process, analyze, and make decisions locally without constantly connecting to or relying on cloud-based AI services.

Regional Outlook

Region wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North American region led the market by generating the maximum revenue share in 2022. In terms of implementing and expanding embedded AI technologies, North America is a pioneering location. The existence of cutting-edge AI technology businesses, strong R&D skills, and an established market ecosystem all play a role in the region's fast expansion of embedded AI solutions. Overall, the use of embedded AI is accelerating across sectors in North America due to technology breakthroughs, the growth of IoT, a supporting ecosystem, and rising public awareness of its advantages.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google LLC (Alphabet Inc.), IBM Corporation, Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), NVIDIA Corporation, Intel Corporation, Qualcomm, Inc., Salesforce, Inc., Siemens AG and Oracle Corporation.

Recent Strategies Deployed in Embedded AI Market

Partnerships, Collaboration and Agreements:

Jul-2023: Intel Foundry Services joined hands with Arm, a semiconductor and software designer, enabling chip providers to build low-power compute system-on-chips (SoCs) on the Intel 18A process. The collaboration would expand the market potential for IFS and give any fabless company that wants to use best-in-class CPU IP and the strength of an open system foundry with cutting-edge process technology.

Jun-2023: Google Cloud extended its partnership with HCLTech, a leading global technology company. With this partnership, HCLTech will develop a Google Cloud Generative AI Center of Excellence (GenAI CoE). Moreover, both companies would develop combined solutions that enable clients to unlock the value of data and realize the full potential of generative AI.

Jun-2023: IBM entered into an expanded partnership with Adobe, an American multinational computer software company. The partnership would enable the clients to improve stakeholder visibility, accelerate task completion, maximize collaboration, and maximize innovation across all design and creative projects.

Jun-2023: IBM teamed up with Will.i.am and FYI (Focus Your Ideas) to leverage the transformative power of secure and trustworthy generative AI for creatives. IBM and FYI would work together to integrate Watsonx, a new AI and data platform for business, to advance AI and bring accountability, transparency, and in the creative process.

Jun-2023: Salesforce formed a partnership with Google Cloud, a suite of cloud computing services offered by Google, to help organizations utilize data and AI to offer more personalized clients experiences, comprehend consumer behavior better, and run more efficient campaigns across marketing, sales, service, and commerce at a reduced cost.

May-2023: Google Cloud joined hands with Teradata, a cloud database and analytics-related software, products, and services. By combining vast and reliable analytics data sets prepared by Teradata and Vertex AI to provide its customers the ability to scale their AI/ML initiatives quickly.

May-2023: IBM joined hands with SAP, a software company, to offer new AI-driven insights and automation to help boost innovation. IBM would provide its IBM Watson to SAP for powering its digital assistant in SAP Start. The new AI capabilities in SAP Start will accelerate user productivity and predictive insights using IBM Watson AI solutions.

May-2023: Microsoft entered into a partnership with MicroStrategy, an analytics and business intelligence company. With this partnership, the company aimed to speed up the release of MicroStrategy's product innovations on Azure, including the integrations with Microsoft 365 and Azure OpenAI Service. Furthermore, the partnership would enable businesses to take quicker, more informed decisions and hasten the creation of new analytic apps.

May-2023: Microsoft formed a collaboration with SAP SE, a Germany-based software company. The collaboration aimed to bring together the power of Microsoft 365 Copilot with SAP SuccessFactors solutions. This would enable the clients to solve various business challenges.

May-2023: Microsoft teamed up with PwC and Icertis, a contract management software provider. The collaboration enables the companies to offer a platform for contract intelligence that enables C-suites to extract crucial business insights from data contained in contracts and utilize those insights to automate entire business operations.

May-2023: Intel teamed up with Boston Consulting Group, an international strategy, and general management consulting firm. The collaboration would allow the company to enable clients to build generative AI applications that are fully optimized across the stack and inside the specified security perimeter.

Apr-2023: NVIDIA entered into collaboration with IKERLAN, an embedded electronics and artificial intelligence provider. The collaboration aimed to increase the technological and knowledge capability of its teams in order to provide the industrial sector with the most cutting-edge artificial intelligence technology.

Apr-2023: Siemens collaborated with Microsoft following which Microsoft’s Collaboration platform Teams and the language models in Azure OpenAI Service as well as other Azure AI capabilities have been combined with Siemens’ Teamcenter software for product lifecycle management (PLM). The integration would enable industrial companies to drive innovation and efficiency throughout the design, engineering, manufacturing, and operational lifecycle of products. This would help industrial organizations in simplifying workflows, overcoming silos, and collaborating in more ways that speed up customer-centric innovation.

Apr-2023: Oracle extended its partnership with GitLab, a US-based technology company. The collaboration enables users to run AI and ML workloads along with GPU-enabled GitLab runners on the OCI, Oracle Cloud Infrastructure. Further, GitLab's vision for accuracy and speed perfectly aligns with Oracle's goals.

Mar-2023: Qualcomm Technologies entered into collaboration with Arrow Electronics, Inc., a manufacturer, and provider of electronic components and enterprise computing solutions. to better the growth and adoption of IoT technology and provide a more varied and international consumer base.

Mar-2023: Salesforce teamed up with Tata Group-owned Air India, to improve its customer experience. The company will deploy its technology that enables Air India to track customer interactions across its contact center and act proactively on problems and follow them through to resolution with the help of artificial intelligence technologies.

Jan-2023: Google Cloud partnered with TTEC Holdings, Inc, one of the largest global customer experiences (CX) technology and services innovators for end-to-end digital CX solutions. The partnership would allow the company to offer intuitive automation, unified customer data, proactive and preemptive service, and AI-powered decision-making at every customer interaction.

Dec-2022: NVIDIA formed a partnership with Deutsche Bank, one of the world's leading financial service providers. The company would combine Deutsche Bank’s financial industry expertise with its leadership in AI and accelerated computing to develop a wide range of AI-powered services.

Aug-2022: Oracle collaborated with Anaconda, a provider of the world’s most popular data science platform. With this collaboration, Oracle focused on enabling and embedding Anaconda’s repository into Oracle Cloud Infrastructure to provide protected open-source Python and R packages and tools. The collaboration provided data scientists with streamlined access to Anaconda, delivering high-performance machine learning and assisting them to ensure strong enterprise security and governance.

Jul-2022: Qualcomm collaborated with Mahindra, an automobile manufacturer, to improve Mahindra's Scorpio N’s embedded electronics.

Jun-2021: Intel joined hands with PathPartner Technology, a dominant product R&D organization. Under the collaboration, PathPartner Technology provided an Artificial Intelligence (AI) technology-based arc welding defect detection solution to the manufacturing industry. PathPartner utilized its expertise in building state-of-the-art AI and Machine Vision software on Intel edge processors to aid manufacturers in fruitfully adopting the defect detection technology and expanding it to address wider Industry 4.0 use cases. Intel developed its AI technology to address an expensive, age-old problem of manual defect detection in the robotic welding process. The AI uses a deep neural network-based inferencing engine to identify defects that are not possible for the human eye.

Jun-2021: Salesforce extended its partnership with AWS, an American cloud-based software company. Following this extended partnership, the companies would introduce a range of integration capabilities to facilitate data sharing and application building that cross the two platforms.

Feb-2021: Google formed a partnership with Ford, an American multinational automobile manufacturer. The partnership would allow the company to accelerate Ford's transformation and reinvent the connected vehicle experience for people's safety.

Product Launch and Product Expansions:

Sep-2022: NVIDIA released the NVIDIA IGX, an industrial-grade edge AI platform. This solution aimed to offer highly secure and low-latency AI inference to help in fulfilling the demand for instant insights from a range of devices and sensors for medical applications.

Mar-2022: NVIDIA rolled out Clara Holoscan MGX, a platform for the medical device sector. Through this launch, the company aimed to develop and install real-time AI applications at the edge, particularly manufactured to comply with regulatory standards.

Acquisitions and Merger:

Jun-2023: IBM announced an agreement to acquire Apptio Inc., a leader in financial and operational IT management and optimization (FinOps) software. The acquisition of Apptio will speed up the development of IBM's IT automation capabilities and give business executives the ability to maximize the return on their technological investments by combining Apptio's offerings combined with IBM's IT automation software.

Nov-2022: Siemens Digital Industries Software entered into an agreement to acquire Avery Design Systems, Inc., a leading simulation-independent verification IP supplier. The acquisition would enable the company to expand its leadership and verification solutions into areas such as High-Performance Computing, Edge, Networking, and 3DICs.

Mar-2022: Microsoft acquired Nuance Communications, a US-based company that provides speech recognition solutions, healthcare AI solutions, and omnichannel customer engagement. This acquisition enables healthcare services providers to have a better opportunity to provide economical, attainable healthcare services, and allows organizations in other industries to better serve the needs of their clients.

Jan-2022: Oracle took over Federos, a leading provider of cloud-enabled, AI-optimized network assurance, analytics, and automation software. With this acquisition, Oracle focused on empowering service providers with AI-optimized service and network analytics, assurance, and automated orchestration.

Jun-2021: IBM took over Turbonomic, an Application Resource Management (ARM) and Network Performance Management (NPM) software provider. This acquisition aimed to enable IBM to become the only company providing a one-stop shop of AI-powered automation capabilities, all built on Red Hat OpenShift to run anywhere.

Apr-2021: IBM signed an agreement to acquire myInvenio, an Italy-based business software company, specializing in process mining software solutions. The acquisition would equip clients with data-driven software that enables them to recognize the most effective business processes to automate using AI. Moreover, the acquisition further reinforces IBM's AI and hybrid cloud plans.

Scope of the Study

Market Segments covered in the Report:

By Vertical

• BFSI
• Healthcare & Lifesciences
• IT & Telecom
• Automotive
• Media & Entertainment
• Energy & Power
• Transportation & Logistics
• Government & Defense
• Manufacturing
• Others

By Data Type

• Numeric Data
• Sensor Data
• Image & Video Data
• Categorical Data
• Others

By Offering

• Hardware
• Processors
• Memory Units
• AI Accelerators
• Others
• Software
• Edge Computing Platforms
• AI & ML Framework
• AI Middleware & Others
• Services

By Geography

• North America
• US
• Canada
• Mexico
• Rest of North America
• Europe
• Germany
• UK
• France
• Russia
• Spain
• Italy
• Rest of Europe
• Asia Pacific
• China
• Japan
• India
• South Korea
• Singapore
• Malaysia
• Rest of Asia Pacific
• LAMEA
• Brazil
• Argentina
• UAE
• Saudi Arabia
• South Africa
• Nigeria
• Rest of LAMEA

Companies Profiled

• Google LLC (Alphabet Inc.)
• IBM Corporation
• Microsoft Corporation
• Amazon Web Services, Inc. (Amazon.com, Inc.)
• NVIDIA Corporation
• Intel Corporation
• Qualcomm, Inc.
• Salesforce, Inc.
• Siemens AG
• Oracle Corporation

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