Most companies expect to update their AI models quarterly per a survey conducted in the middle of 2023. This is likely to keep a good and regular schedule without overloading those working on updating the models. Only around two percent of respondents had no plans to update their models. In the fast moving environment of AI, it would likely leave a model critically behind if there was no data updates.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

(Source: about:blank)

Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

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Cloud Artificial Intelligence (AI) Market Size 2024-2028

The cloud artificial intelligence (ai) market size is forecast to increase by USD 12.61 billion at a CAGR of 24.1% between 2023 and 2028.

The market is experiencing significant growth, driven by the emergence of technologically advanced devices and the increasing adoption of 5G and mobile penetration. These factors enable the integration of AI technologies into various applications, leading to improved efficiency and productivity. However, the market also faces challenges from open-source platforms, which offer free AI solutions, making it difficult for market players to compete on price. Despite this, the market is expected to continue its growth trajectory, driven by the increasing demand for AI solutions in various industries, including healthcare, finance, and retail. Organizations are leveraging cloud-based AI solutions to gain insights from their data, automate processes, and enhance customer experiences.The market analysis report provides a comprehensive overview of these trends and challenges, offering valuable insights for stakeholders looking to capitalize on the growth opportunities In the cloud AI market.

What will be the Size of the Cloud Artificial Intelligence (AI) Market During the Forecast Period?

Request Free SampleThe market is experiencing robust growth, driven by the increasing adoption of machine learning (ML), deep learning, neural networks, and generative AI technologies. These advanced algorithms are revolutionizing various industries by emulating human intelligence in speech recognition, digital media, diagnostics, cybersecurity, and business decision-making. Hyperscale cloud platforms are becoming the preferred infrastructure for AI applications due to their ability to handle massive data processing requirements. Cloud AI solutions are transforming IT services by automating routine tasks, enhancing data analytics, and improving human capital management. They offer significant cost savings by eliminating the need for expensive hardware and maintenance. Moreover, AI-driven cloud management and data management solutions enable predictive analytics, personalization, productivity, and security enhancements.In addition, AI is playing a pivotal role in threat detection and cybersecurity, ensuring business continuity and data protection. Overall, the cloud AI market is poised for exponential growth, as organizations continue to leverage AI to gain a competitive edge In their respective industries.

How is this Cloud Artificial Intelligence (AI) Industry segmented and which is the largest segment?

The cloud artificial intelligence (ai) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ComponentSoftwareServicesGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanSouth AmericaMiddle East and Africa

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) software replicates human learning and behavior, revolutionizing various business sectors. AI development involves creating new software or enhancing existing solutions to deliver analytics results and trigger actions based on them. Applications of AI include automating business processes, personalizing services, and generating industry-specific insights. The digitization trend has driven industrial transformations, with healthcare being a prime example. According to BDO's Healthcare Digital Transformation Survey, 93% of US healthcare organizations adopted digital transformation strategies in 2021, integrating AI, computing, and enterprise resource planning software. AI functionality encompasses speech recognition, machine learning (ML), deep learning, neural networks, generative AI, automation, decision-making, and more.Hyperscale cloud platforms, IT services, infrastructure, data analytics, human capital management, cost savings, cloud management, data management, predictive analytics, personalization, productivity, security, threat detection, integration, talent gap, and chatbots are significant AI applications. AI tools process data, power business intelligence, and enable lower costs through ML-based models and GPUs. Enterprise datacenters, virtualization, public clouds, private clouds, and hybrid cloud solutions leverage AI for non-repetitive tasks. AI streamlines workloads, automates repetitive tasks, monitors and manages IT infrastructure, and offers dynamic cloud services. AI is transforming industries, from retail inventory management to financial organizations, providing competitive advantages through cost savings and improved decision-making capabilities.

Get a glance at the Cloud Artificial Intelligence (AI) Industry repo

The global number of AI tools users in the 'AI Tool Users' segment of the artificial intelligence market was forecast to continuously increase between 2024 and 2030 by in total 414.7 million (+131.91 percent). After the tenth consecutive increasing year, the number of AI tools users is estimated to reach 729.11 million and therefore a new peak in 2030. Notably, the number of AI tools users of the 'AI Tool Users' segment of the artificial intelligence market was continuously increasing over the past years. The Statista Market Insights cover a broad range of additional markets.

Integrated Systems Health Management includes as key elements fault detection, fault diagnostics, and failure prognostics. Whereas fault detection and diagnostics have been the subject of considerable emphasis in the Artificial Intelligence (AI) community in the past, prognostics has not enjoyed the same attention. The reason for this lack of attention is in part because prognostics as a discipline has only recently been recognized as a game-changing technology that can push the boundary of systems health management. This paper provides a survey of AI techniques applied to prognostics. The paper is an update to our previously published survey of data-driven prognostics.

Convert websites into useful data Fully managed enterprise-grade web scraping service Many of the world's largest companies trust ScrapeHero to transform billions of web pages into actionable data. Our Data as a Service provides high-quality structured data to improve business outcomes and enable intelligent decision making

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The market for artificial intelligence grew beyond 184 billion U.S. dollars in 2024, a considerable jump of nearly 50 billion compared to 2023. This staggering growth is expected to continue with the market racing past 826 billion U.S. dollars in 2030. AI demands data Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together these bodies pose significant challenges to data-hungry AI companies. AI could boost productivity growth Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on a variety of factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

According to Cognitive Market Research, the global Artificial Intelligence in Retail market size is USD 4951.2 million in 2023and will expand at a compound annual growth rate (CAGR) of 39.50% from 2023 to 2030.

Enhanced customer personalization to provide viable market output
Demand for online remains higher in Artificial Intelligence in the Retail market.
The machine learning and deep learning category held the highest Artificial Intelligence in Retail market revenue share in 2023.
North American Artificial Intelligence In Retail will continue to lead, whereas the Asia-Pacific Artificial Intelligence In Retail market will experience the most substantial growth until 2030.

Enhanced Customer Personalization to Provide Viable Market Output

A primary driver of Artificial Intelligence in the Retail market is the pursuit of enhanced customer personalization. A.I. algorithms analyze vast datasets of customer behaviors, preferences, and purchase history to deliver highly personalized shopping experiences. Retailers leverage this insight to offer tailored product recommendations, targeted marketing campaigns, and personalized promotions. The drive for superior customer personalization not only enhances customer satisfaction but also increases engagement and boosts sales. This focus on individualized interactions through A.I. applications is a key driver shaping the dynamic landscape of A.I. in the retail market.

January 2023 - Microsoft and digital start-up AiFi worked together to offer Smart Store Analytics. It is a cloud-based tracking solution that helps merchants with operational and shopper insights for intelligent, cashierless stores.

Source-techcrunch.com/2023/01/10/aifi-microsoft-smart-store-analytics/

Improved Operational Efficiency to Propel Market Growth

Another pivotal driver is the quest for improved operational efficiency within the retail sector. A.I. technologies streamline various aspects of retail operations, from inventory management and demand forecasting to supply chain optimization and cashier-less checkout systems. By automating routine tasks and leveraging predictive analytics, retailers can enhance efficiency, reduce costs, and minimize errors. The pursuit of improved operational efficiency is a key motivator for retailers to invest in AI solutions, enabling them to stay competitive, adapt to dynamic market conditions, and meet the evolving demands of modern consumers in the highly competitive artificial intelligence (AI) retail market.

January 2023 - The EY Retail Intelligence solution, which is based on Microsoft Cloud, was introduced by the Fintech business EY to give customers a safe and efficient shopping experience. In order to deliver insightful information, this solution makes use of Microsoft Cloud for Retail and its technologies, which include image recognition, analytics, and artificial intelligence (A.I.).

Source-www.ey.com/en_gl/news/2023/01/ey-announces-launch-of-retail-solution-that-builds-on-the-microsoft-cloud-to-help-achieve-seamless-consumer-shopping-experiences

Market Dynamics of the Artificial Intelligence in the Retail market

Data Security Concerns to Restrict Market Growth

A prominent restraint in Artificial Intelligence in the Retail market is the pervasive concern over data security. As retailers increasingly rely on A.I. to process vast amounts of customer data for personalized experiences, there is a growing apprehension regarding the protection of sensitive information. The potential for data breaches and cyberattacks poses a significant challenge, as retailers must navigate the delicate balance between utilizing customer data for AI-driven initiatives and safeguarding it against potential security threats. Addressing these concerns is crucial to building and maintaining consumer trust in A.I. applications within the retail sector.

Impact of COVID–19 on the Artificial Intelligence in the Retail market

The COVID-19 pandemic significantly influenced artificial intelligence in the retail market, accelerating the adoption of A.I. technologies across the industry. With lockdowns, social distancing measures, and a surge in online shopping, retailers turned to A.I. to navigate the challenges posed by the pandemic. AI-powered solutions played a crucial role in optimizing supply chain management, predicting shifts in consumer behavior, and enhancing e-commerce experiences. Retailers lever...

A 2024 survey carried out in the United States showed that nearly one in two consumers would not allow artificial intelligence (AI) to access their personal data for personalization. While 16 percent of the surveyed consumers were not too sure about it, about the same percentage of shoppers would allow AI technologies to access their information details to get a more convenient and personalized shopping experience.

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.
The major contributions have been materialized in the form of novel algorithms.
Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.
Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.
Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".

If you use the data, implementation, or any details of the paper, please cite!

BIBTEX:

_

@inproceedings{nedelkoski2020multi,
 title={Multi-source Distributed System Data for AI-Powered Analytics},
 author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej},
 booktitle={European Conference on Service-Oriented and Cloud Computing},
 pages={161--176},
 year={2020},
 organization={Springer}
}

_

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.

Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

According to a survey conducted at the EmTech Digital conference in March 2019, U.S. business leaders shared their opinions on trust issues with regard to AI data quality and privacy. Nearly half of respondents reported a lack of trust in the quality of AI data in their companies, showing that there is still a long way to go to get quality AI data.

Market Analysis: AI Data Management PlatformThe global AI Data Management Platform (DMP) market is projected to reach USD 89,590 million by 2033, exhibiting a CAGR of XX% during the forecast period. The rise of Big Data, cloud computing advancements, and the growing adoption of AI have driven the market expansion. AI DMPs provide organizations with centralized platforms to manage, analyze, and extract insights from vast amounts of data.Market Dynamics and TrendsMajor market drivers include the increasing need for efficient data management, the integration of AI technologies, and the growing adoption of cloud-based solutions. Healthcare & Life Sciences and BFSI sectors are expected to witness significant growth, fueled by the need for secure and efficient data management in these industries. The shift towards hybrid and multi-cloud deployments is also expected to drive market growth. However, data security and privacy concerns and the high costs of implementation pose challenges for market growth. Key market players include AWS, Microsoft, IBM, and Salesforce, who offer comprehensive AI DMP solutions.

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

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Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new markets. With continuous data updates, Success.ai ensures you’re always working with the freshest information.

Key Use Cases:

• Targeted Lead Generation: Build accurate lead lists by filtering data by company size, industry, or location. Target decision-makers in key industries to streamline your B2B sales outreach.
• Account-Based Marketing (ABM): Use B2B company data to personalize marketing campaigns, focusing on high-value accounts and improving conversion rates.
• Investment Research: Track company growth, funding rounds, and employee trends to identify investment opportunities or potential M&A targets.
• Market Research: Enrich your market intelligence initiatives by gain...

As the frenzy around generative artificial intelligence intensifies, The Information has built a database of more than 100 companies making software and services that use generative AI. Investors are jockeying to join the action: Together, the startups on our list have raised more than $20 billion. Our data comes from our reporting, founders, investors and PitchBook, which provides private market data. We will regularly update the database with more companies and more information about how they are growing.

Round 2 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1104 trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

The data presented in this data project were collected in the context of the research project “AI TRACE - Synaesthetic Engagement of Artificial Intelligence with Digital Arts and its Audience”. The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.)under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 782). AI TRACE aimed at developing an ethically compliant behavioural analysis and visualization tool in the form of a metalanguage that can be used in the museum sector to track, analyse and present data collected from exhibition visitors in the form of a personalized 3D digital object. AI TRACE showcases Artificial Intelligence subsystems. The data presented in this data project were collected during the Preparatory Activity event that took place in October 2021 during the 17th edition of the Athens Digital Arts Festival (ADAF). The research activity was hosted at the new premises of the Museum of Modern Greek Culture, at a specially designed exhibition space. The purpose of this activity was to collect data for methodological testing and for feeding the AI subsystem. The data files derived from the research activities and provided here are:

1. Data that was gathered from a pre-visit questionnaire which was completed by 231 survey participants. The questionnaire explores visitors' motivations for visiting and preferences of a digital arts exhibition, their relationship with digital arts, their mood and attitude before the visit, and certain demographic characteristics. For the purpose of the research, visitors of the Athens Digital Arts Festival who voluntarily participated in the survey were approached. The dataset can be used to analyse various characteristics of museum visitors, such as their motivation to visit, their interests and their relationship with art, in relation to their personal characteristics (gender, age, status).
2. Timing-and-tracking (T&T) data -more specifically time spent in each exhibit and trajectory- which were extracted from video recordings in the exhibition space. Regular festival visitors were recruited at the entrance of the exhibition area by the research assistants. Participants (N=273) were asked to sign an informed consent form before filling the online anonymous questionnaire and entering the exhibition space. A custom-made software was used to blur their faces during video recording for data anonymization purposes. The tracking data were extracted via BORIS (Behavioral Observation Research Interactive Software), an open-source event logging software for video/audio coding and live observations that is developed by the University of Torino. The datasets can be used to analyse visitors’ behavior in relation to demographic characteristics, their motivations, interests and relation to arts, as well as their pre-visit mood and attitudes.
3. The data file containing both questionnaire data and timing-and-tracking (T&T) data for a subset of the sample (N=223). This data can be used in combination to analyse the behaviour of museum visitors in relation to their demographic characteristics, motivations for visiting, their preferences and their relationship with digital arts, as well as in relation to their mood and attitude before the visit.

According to Cognitive Market Research, The Global Artificial intelligence AI in Supply Chain and Logistics market size is USD 1.9 million in 2024 and will expand at a compound annual growth rate (CAGR) of 50.50% from 2024 to 2031.

North America Artificial intelligence AI in Supply Chain and Logistics held the major market of around 40% of the global revenue with a market size of USD 0.76 million in 2024 and will grow at a compound annual growth rate (CAGR) of 48.7% from 2024 to 2031.
Europe Artificial intelligence AI in Supply Chain and Logistics accounted for a share of around 30% of the global market size of USD 0.57 million in 2024.
Asia Pacific Artificial intelligence AI in Supply Chain and Logistics held the market of around 23% of the global revenue with a market size of USD 0.44 million in 2024 and will grow at a compound annual growth rate (CAGR) of 52.5% from 2024 to 2031.
South America Artificial intelligence AI in Supply Chain and Logistics market of around 5% of the global revenue with a market size of USD 0.10 million in 2024 and will grow at a compound annual growth rate (CAGR) of 49.9% from 2024 to 2031.
Middle East and Africa Artificial intelligence AI in Supply Chain and Logistics held the major market of around 2% of the global revenue with a market size of USD 0.04 million in 2024 and will grow at a compound annual growth rate (CAGR) of 50.2% from 2024 to 2031.
The sales of software in AI for supply chain and logistics are projected to rise due to increased demand for scalable, customizable solutions offering real-time analytics, predictive insights, and seamless integration capabilities.
The sales of machine learning in AI for supply chain and logistics are poised to surge owing to its ability to optimize operations, forecast demand accurately, and automate decision-making processes, improving efficiency and profitability.

Increasing Availability of Big Data and Analytics Tools to Propel the Market Growth

The increasing availability of big data and analytics tools is poised to propel significant growth in the AI for supply chain and logistics market. As the volume, velocity, and variety of data generated within supply chains continue to expand, businesses are recognizing the value of leveraging advanced analytics and AI-driven insights to optimize their operations. These tools enable companies to extract valuable insights from vast datasets, improving decision-making, forecasting accuracy, and overall supply chain performance. By harnessing the power of big data analytics, organizations can uncover hidden patterns, identify emerging trends, and predict future demand more accurately. Moreover, the integration of AI with analytics tools facilitates the automation of repetitive tasks and the identification of optimization opportunities, leading to enhanced efficiency and cost savings. Thus, the increasing availability and adoption of big data and analytics tools are expected to drive substantial market growth in the AI for supply chain and logistics sector.

Market Restraints of the Artificial intelligence AI in Supply Chain and Logistics

Data Security Concerns to Limit the Sales

Data security concerns pose a significant restraint on the sales of AI for supply chain and logistics solutions. As these systems rely heavily on vast amounts of sensitive data, including customer information, trade secrets, and operational details, the risk of data breaches, cyberattacks, and unauthorized access becomes a prominent issue. Heightened regulatory scrutiny, such as GDPR and CCPA, adds further complexity and compliance challenges to data handling practices within supply chains. Organizations must invest heavily in robust cybersecurity measures, encryption techniques, and access controls to safeguard sensitive data, which can significantly increase implementation costs. Moreover, the reputational damage and financial repercussions resulting from data breaches can deter potential buyers from adopting AI solutions, particularly in industries where data privacy and confidentiality are paramount. Addressing these concerns through stringent security protocols and transparent data governance practices is crucial to fostering trust and driving wider adoption of AI in supply chain management.

Impact of Covid-19 on the Artificial intelligence AI in supply chain and logistics Market

The COVID-19 pandemic has accelerated the adoption of Artificial Intelligence (AI) in ...

The China AI Exports Database (CAIED) tracks Chinese government-supported development finance projects that utilized or enabled Artificial Intelligence (AI) technology in the Global South between 2000 and 2017. The dataset captures 155 projects for AI applications or infrastructure across 65 low- and middle-income countries funded by Chinese official sector institutions and the military worth $4.5 billion.

The Information’s Generative AI Database includes more than 300 companies seeking to capitalize on the enthusiasm around OpenAI's ChatGPT. But building an artificial intelligence company is expensive and few companies are generating meaningful revenue, suggesting some won't survive. Already, startups such as Character, Adept and Inflection have effectively sold themselves to tech giants. The Information sought to identify the most likely targets for future takeovers: companies that have not raised money in at least two years and those working on particularly expensive projects, such as robotics or building AI models. The data come from our own reporting and PitchBook, which provides private market data. We will update the list periodically. See The Information’s Generative AI Database here. See related article 78 Artificial Intelligence Startups That Could Be for Sale This Year.

Round 6 Test Dataset This is the test data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 480 sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.