The key players operating in the Composite Al Market are te AI Market is estimated to grow from USD 0.9 billion in 2023 to USD 4.4 billion by 2028, at a CAGR of 36.5% during the forecast period. Integration with edge computing and IoT for real-time decision-making and rising demand for explainable AI and trustworthiness across major sectors offer opportunities to the end users to leverage composite AI solutions. Moreover, the growing intricacy of AI applications for better performance and increasing demand for more customized and flexible solutions to improve efficiency and productivity boost the market growth in coming years. , The composite AI market report covers the composite AI technology roadmap till 2030, with insights around the initiation, development, and commercialization of technologies across AI-driven autonomous systems, AI ethics, and responsible AI. Some of the key findings from the technology roadmap include: , Composite AI Market Short-term Technology Roadmap (2023-2025) , Advancements in explainable AI to foster trust in Composite AI solutions , Commercialization of Composite AI enhancing human intelligence in a wide range of applications , Composite AI Market Mid-term Technology Roadmap (2026-2028) , Development of AI ethics and responsible AI to shape the development, deployment, and adoption of composite AI solutions in the market , Integration of personalized AI assistants with Composite AI solutions to provide tailored and intelligent experiences to users , Composite AI Market Long-term Technology Roadmap (2029-2030) , Advanced neural networks and models for developing intelligent and effective composite AI applications across a wide range of industries and use cases , The emergence of next-generation composite AI platforms , Composite AI Market Growth Dynamics , Driver: Growing intricacy of AI applications for better performance and accuracy , AI applications are becoming more complex, requiring the integration of multiple AI technologies and models to solve intricate problems. Currently, organizations are facing the reality that training a massive neural network using ML does not always scale to solve problems of increased complexity. The pure ML approach works for many classification and recognition tasks but is not always sufficient for solving deeper understanding problems. ML also generates an unending need for training data and computation power. Composite AI solutions provide a way to leverage the strengths of different AI algorithms and components, enabling organizations to tackle complex challenges and achieve better performance. Furthermore, composite AI solutions can leverage the strengths of various AI models and algorithms, resulting in improved performance and accuracy compared to standalone AI approaches. Organizations can achieve more robust and precise results by combining different techniques, leading to enhanced decision-making and problem-solving capabilities. , Restraint: Concerns related to data privacy and security , Some organizations may hesitate to adopt composite AI solutions due to lacking trust in AI technology or a limited understanding of its capabilities and limitations. Concerns about data privacy, security, and potential biases in AI models can also create obstacles to implementation. The main privacy concerns around AI comprises of data breaches and unauthorized access to personal information. The collected data is collected and then processed to gain key insights which lead to the rise in high risk as it may fall into the wrong hands and can cause that data to get breached easily. . With the advancements in AI technology pave the way for more increase in the data breaches and security issues. For instance, generative AI technology can be exploited to create fake profiles or misused to generate unauthorized images. As per the latest stats, cybercrimes affect the security of 80% of businesses worldwide, and personal data falling into the wrong hands can have severe consequences. , Opportunity: Integration with edge computing and IoT for real-time decision-making , The proliferation of Internet of Things (IoT) devices and the need for real-time decision-making are driving the adoption of edge computing. Composite AI solutions integrated with edge devices can process and analyze data locally, reducing latency and enabling faster insights and responses. This integration presents opportunities for deploying composite AI solutions in edge computing environments. Edge computing is the key support for intelligent applications and 5G/6G Internet of Things (IoT) networks. It offers various advantages such as low latency, fast response, context-aware services, mobility, and privacy preservation. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. The adoption of new emerging technologies such as IoT, wireless sensor networks (WSNs), cloud/edge computing, and 5G/6G communication networks in various fields such as healthcare, agriculture, education, and transportation can bring many opportunities in improving people’s quality of life, thus building intelligent systems that deliver high-quality, innovative services to the consumers. In the IoT environment, many interconnected devices, such as sensors, mobiles, and memory units, lead to voluminous, heterogeneous, highly noisy, spatiotemporal-correlated, and real-time data streams that need intelligent learning for efficient data analysis and meaningful insight extraction. , Challenge: Data availability and quality , Composite AI solutions heavily rely on large and diverse datasets to train and optimize models. However, organizations may face challenges acquiring high-quality, labeled, and relevant data, especially for specific use cases or industries. Data privacy concerns and regulatory restrictions further complicate data access and sharing. The availability of high-quality data is considered critical, as it is used to train AI algorithms. When developing AI applications that can deliver value, the quality of the data fed into such algorithms is of great importance. Moreover, biased data during labeling and training can potentially result in biased AI applications, posing significant challenges to practitioners in leveraging their data assets into AI applications. , By offering, hardware to register at the highest CAGR during the forecast period , Composite AI hardware comprises hardware components and infrastructure that support the implementation and execution of composite AI solutions. It involves utilizing specialized hardware devices, processors, accelerators, and infrastructure configurations designed to handle composite AI workloads' computational requirements and complexities. Composite AI hardware plays a crucial role in enabling the efficient processing and execution of various AI models, algorithms, and techniques that are integrated within a composite AI solution. These hardware components are optimized to handle the computational demands of tasks such as machine learning, deep learning, natural language processing, computer vision, and more. , By application, product design & development to account for the largest market size during the forecast period , Product design and development business applications plays a crucial role in implementing composite AI solutions in the market. Product design & development applications provide tools and functionalities to generate innovative ideas and concepts. These applications enable businesses to explore new product possibilities and identify areas where composite AI can add value. Using composite AI in product design & development offers significant benefits for companies looking to enhance their product development capabilities, reduce costs, and bring innovative products to market more efficiently. , North America to account for the largest market size during the forecast period , North America is a leading region in adopting and growing composite AI solutions. The presence of advanced AI technology companies, robust R&D capabilities, and a mature market ecosystem contribute to the rapid growth of composite AI solutions in this region. Major industries such as healthcare, BFSI, retail, and manufacturing embrace composite AI to drive innovation, enhance customer experiences, and improve operational efficiency. . These factors are also responsible for adopting composite AI solutions across the region. Moreover, various industry verticals, such as telecom, healthcare, media and entertainment, retail and eCommerce, and BFSI, are leveraging composite AI solutions to enhance productivity and better performance. , Recent Developments: , In April 2023, Amazon SageMaker announced Collections, a new capability to organize machine learning models in the Amazon SageMaker Model Registry. Collections may gather relevant registered models and organize them hierarchically to improve model discoverability at scale. , In July 2022, AWS partnered with Hugging Face to make it easier for companies to leverage state-of-the-art machine learning models, and ship cutting-edge NLP features faster. Through this partnership, Hugging Face is leveraging Amazon Web Services as its preferred cloud provider to deliver customer services. , In May 2022, BlackSwan Technologies and Refinitiv entered a strategic agreement. The agreement enables next-generation customer risk assessment through an advanced compliance solution incorporating comprehensive financial crime data and ground-breaking AI technologies for KYC, transaction monitoring, and screening. , In March 2022, Microsoft announced the acquisition of Nuance. This acquisition will offer customers improved consumer, patient, clinician, and employee experiences and better productivity and financial results. , In September 2021, SAS announced an expansion for its SAS Viya platform, which analyzes data and builds AI models. , KEY MARKET SEGMENTS, By Offering: , Hardware , Processors , Memory Units , Networks , Other Hardware (Tensor Processing Units (TPUs), Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), and Central Processing Units (CPUs)) , Software , AI Development Platforms and Tools , ML Frameworks , AI Middleware , Other Software (Computer Vision Software, Data Management Tools, Monitoring software, and Security and Governance tools) , Services , Training and Consulting , System Integration and Implementation , Support and Maintenance , By Technique: , Conditioned Monitoring , Pattern Recognition , Data Processing , Proactive Mechanism , Data Mining & Machine Learning , Other Methods (AutoML and model building, model stacking & ensemble, and transfer learning) , By Application: , Product Design & Development , Quality Control , Predictive Maintenance , Security & Surveillance , Customer Service , Other Applications (Fraud Detection & prevention, and Supply Chain Management) , By Vertical: , BFSI , Retail and eCommerce , Manufacturing , Energy and Utilities , Transportation and Logistics , Healthcare and Life Sciences , Media and Entertainment , Government and Defense , Telecom , Other Verticals (Construction & real estate, Automotive, IT and ITeS, and education) , By Region: , North America , US , Canada , Europe , UK , Germany , France , Italy , Spain , Rest of Europe , Asia Pacific , China , Japan , India , South Korea , ANZ , ASEAN Countries , Rest of Asia Pacific , Middle East & Africa , UAE , Saudi Arabia , Israel , South Africa , Rest of Middle East & Africa , Latin America , Brazil , Mexico , Argentina , Rest of Latin America , KEY MARKET PLAYERS , IBM , SAS Institute , Microsoft , Google , AWS , Salesforce , BlackSwan Technologies , Oracle , OpenText , SAP , HPE , Pega s , NVIDIA , Intel , UiPath , Zest AI , Dynamic Yield , DataRobot , H2O.ai , Squirro , CognitiveScale , SparkCognition , Diwo , ACTICO , Kyndi , Nauto , Netra , Exponential AI .
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