The Global AI Governance Market size is expected to reach $562.9 Million by 2028, rising at a market growth of 34.5% CAGR during the forecast period

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New York, March 22, 2023 (GLOBE NEWSWIRE) — Reportlinker.com announces the release of the report “Global AI Governance Market Size, Share & Industry Trends Analysis Report By Component, By Vertical, By Organization size, By Deployment Mode, By Regional Outlook and Forecast, 2022 – 2028” – https://www.reportlinker.com/p06435182/?utm_source=GNW
Therefore, a legislative framework should exist to ensure that Machine Learning (ML) technologies are correctly examined and systematically developed to support humanity’s fair acceptance of AI technology, according to the philosophy underlying AI governance.

In the context of AI (Artificial Intelligence) governance, bias, ROI (return on investment) risk, and algorithm efficacy are assessed and tracked. AI governance’s primary goal is to bridge the ethical responsibility and technical progress gaps. The expansion of the AI governance business is driven mainly by the increase in government initiatives to employ Al technology, rapid and simple access to historical datasets, and the convenience of data storage.

Therefore, a legislative framework should exist to ensure that Machine Learning (ML) technologies are adequately examined and systematically developed to support humanity’s fair acceptance of AI technology, according to the philosophy underlying AI governance. AI (Artificial Intelligence) governance includes evaluation and monitoring of algorithm performance, ROI risk, and bias. AI governance’s primary goal is to bridge the ethical responsibility and technical progress gaps.

The workforce will need a high level of trust in AI governance to close gaps and boost their confidence in automated systems while making routine, life-altering choices. Because of AI’s rising advantages, organizations and governments from all over the globe are adopting a range of steps to embrace AI and machine learning technology and position themselves as market leaders. Councils, new norms and legislation, and AI governance solutions are now being developed by governments all around the globe. As a result, a better degree of adherence to technology regulation is anticipated to give profitable chances for the AI governance market projection. AI may also drastically eliminate gender-based discrimination.

COVID-19 Impact Analysis

Chief risk officers and their teams have had to reevaluate outdated methods and assumptions used to manage and monitor risk due to the present pandemic crisis. The worldwide impact of COVID-19 has shown the significance of interconnection in international collaboration and the challenges posed by antiquated technology for efficient governance. Consequently, many governments have been hurrying to find, assess, and buy trustworthy AI-powered solutions. In addition, the necessity to operationalize ethical AI concepts has increased as a result of COVID-19. Taking all of this into account, the pandemic would have significantly helped the market for AI governance.

Market Growth Factors

Fast and convenient access to the historical dataset and ease of data storage

An important aspect influencing AI advancement is the accessibility of previous datasets. Healthcare facilities and governmental organizations are creating unstructured data that is available to the research community now that data storage and recovery are more economical. As a result, researchers now have access to enormous databases covering anything from historical rainfall patterns to clinical imaging. By granting access to massive datasets, next-generation computer architectures let academics and information scientists develop more swiftly. These elements have fueled the market for AI governance.

Growing government usage of the Al technology

Due to the growing advantages of Al, organizations, and governments throughout the globe are starting initiatives to embrace Al and ML technologies and position themselves as market leaders. Government entities from several countries establish councils, new laws and regulations, and frameworks to adopt Al governance solutions. Increasing public trust in Al technology and protecting civil liberties and private information are the key objectives of governments deploying Al governance solutions. In addition, to identify risk problems for Al technology, several companies have formed committees in collaboration with providers of Al solutions, academic institutions, and research centers.

Market Restraining Factors

Establishing comprehensive ai ethical standards.

Over the past few years, numerous principles and guidelines have been established by various governmental agencies, professional organizations, regulatory systems, and AI companies. These guidelines serve as a foundation for developing robust AI regulations and aid in identifying the most important ethical concerns that need to be addressed. However, there are no all-inclusive rules, even if some ideas have been widely accepted. For instance, if laws are created to safeguard one significant value protected by principles, another value may suffer, which might be detrimental to social ethics. Establishing comprehensive ethical standards for AI that consider all ethical principles might thus be a constraining factor for the state of AI governance solutions today.

Component Outlook

Based on the component, the AI governance market is divided into solution and service categories. The solution segment dominated the AI governance market share with maximum revenue share in 2021. Many elements, including growing dependency, user desire for AI-based solutions, and others, bring this on. The platform and software tools that would provide end-to-end AI governance solutions to AI developers, business users, data scientists, and IT architects in several sectors are referred to as the AI governance solution. These facilitate the creation, management, and use of AI solutions and enable enterprises to use a variety of AI and associated skills.

Deployment Mode Outlook

Based on the deployment mode, the AI governance market is divided into on-premise and cloud. In 2021, the cloud segment recorded a remarkable revenue share in the AI governance market. This is because AI services delivered through the cloud are the perfect option for many businesses. To access computation, they don’t need to create a vast data center; instead, they may leverage the already established infrastructure. In reality, the availability of several plug-and-play AI cloud services from cloud providers, as well as access to sufficient computational capacity and pre-trained models to start AI applications, is one reason AI has grown so prevalent.

Organization Size Outlook

Based on the Organization size, the AI governance market is segmented into large enterprises and small and medium-sized enterprises (SMEs). The large enterprise segment projected the maximum revenue share in the AI governance market in 2021. As businesses want to ensure that their AI systems safeguard sensitive data, the emphasis on data privacy and security is growing. Furthermore, implementing AI governance to decrease risk is anticipated to increase as big organizations are increasingly vulnerable to cyberattacks and other security challenges. This would encourage market development in this niche.

Vertical Outlook

Based on industry vertical, the AI governance market is divided into the BFSI, government & defense, healthcare & life sciences, media & entertainment, retail, IT & telecom, automotive, and others. The retail segment covered a considerable revenue share in the AI governance market in 2021. The retail industry is going through a substantial shift. Customers’ channel choices and purchasing behaviors alter as they grow more digitally literate. Concerns concerning data have arisen as a result of the transition to digital. Proper AI governance is essential to improve overall retail performance, address common issues, and maintain an edge against more potent retail rivals and agile direct-to-consumer businesses.

Regional Outlook

Based on the geography, the AI governance market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America region led the AI governance market by generating maximum revenue share in 2021. This is due to the US having a significant industrial base, government programs to support research, and considerable purchasing power supporting the expansion of the AI governance market. Additionally, several significant AI governance solution vendors are launching new services & solutions.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Microsoft Corporation, Google LLC (Alphabet Inc.), IBM Corporation, SAP SE, Amazon Web Services, Inc. (Amazon.com, Inc.), Salesforce.com, Inc., Meta Platforms, Inc., SAS Institute, Inc., Tibco Software, Inc. (Vista Equity Partners), and QlikTech International AB.

Recent Strategies deployed in AI Governance Market

Partnerships, Collaborations, and Agreements

Oct-2022: Microsoft collaborated with Haleon, a UK-based healthcare company, engaged in oral health, respiratory, pain relief, supplements, etc. This collaboration involves working towards increasing the Microsoft Seeing AI app’s functionality and providing users with more in-depth information regarding 1,500+ Haleon products.

Sep-2022: Amazon Web Services teamed up with SK Telecom, a South Korea-based telecommunications company, primarily into digital infrastructure services, AI, mobile network operator, etc. The collaboration involves combining SK Telecom’s experience in AI, with AWS’s flexibility, and scalability, which would help advance innovation in AI, and would prove economical for the consumers to develop, use and scale computer vision applications, which strengthens productivity, and plant safety.

Jul-2022: Meta platforms came into partnership with Oasis Labs, a company providing privacy-related software. This acquisition would bring fairness to AI models with the help of privacy technologies. This would benefit the users present worldwide and benefit the whole of society.

May-2022: Amazon Web Services, Inc. teamed up with STMicroelectronics, a global company engaged in the development and creation of semiconductor technologies. In this Collaboration, both companies would securely connect Internet of Things devices to the AWS cloud.

Dec-2021: Amazon Web Services, Inc. collaborated with Meta, an American multinational technology conglomerate to provide cloud services to AWS. Under this collaboration, both companies would work together to enhance the functioning of customers running PyTorch on AWS and boost how developers create, train, deploy and operate machine learning/artificial intelligence models.

Nov-2021: Google Cloud teamed up with Qualcomm Technologies, Inc., a multinational corporation based in California. This collaboration would boost neural network development and differentiation for the Snapdragon Ride™ Platform, Snapdragon® mobile, ACPC, and XR platforms, and Qualcomm Technologies’ IoT platforms by using Google Cloud Vertex AI Neural Architecture Search (NAS) with the Qualcomm® Artificial Intelligence (AI) Engine.

Sep-2020: Microsoft teamed up with AT&T, a telecommunication holding company. Through this collaboration, companies would aim to provide an integrated IoT solution to allow companies to smoothly connect machines to the cloud with highly protected network connectivity.

Oct-2020: SAS formed a partnership with TMA Solutions, a privately owned software outsourcing company. This partnership aimed to provide analytics solutions in AI and Data Analytics to companies in Vietnam via its cloud-native analytics platform, which would offer business insights for decision-making.

Jun-2020: Salesforce extended its partnership with Snowflake, a cloud computing-based data warehousing company. Following this partnership, the companies would introduce a data marketplace, new data integration tools, and other capabilities in order to assist organizations in migrating Salesforce data to the Snowflake platform for collaboration and analysis.

Acquisition and Mergers

Oct-2022: Google completed the acquisition of Alter, an artificial intelligence (AI) avatar startup engaged in helping brands and creators express themselves. Through this acquisition, Google would improve both the quality and quantity of the content provided to consumers.

Jul-2022: IBM took over Databand.ai, an Israel-based database software company, primarily into providing data observability platform. This acquisition reinforces IBM’s data, automation, and AI software offerings, and enables IBM to offer an all-inclusive set of observability capabilities.

Jul-2022: IBM took over Databand, a startup developing an observability platform. This acquisition aimed to deliver the customers and partners with the technology they require to offer trustworthy data and AI at scale.

Mar-2022: Microsoft took over Nuance Communications, a leader in conversational AI and ambient intelligence across industries. This acquisition aimed to assist providers in providing more affordable, efficient, and accessible healthcare, and assist companies in every industry develop more customized and meaningful customer experiences.

Sep-2021: SAP took over SwoopTalent’s intellectual property. Following this acquisition, the company would embed data and machine learning technology of SwoopTalent across SAP SuccessFactors solutions.

Apr-2021: IBM took over Turbonomic, a company engaged in offering tools to manage application performance. With this move, IBM would enhance its footprint by offering enterprises AI-based services to manage their workloads and networks.

Mar-2021: Microsoft took over The Marsden Group, a global technology group, providing industrial technology. This acquisition strengthened Microsoft’s potential to create customer value by experimenting and providing deep industry solutions formed on edge, Microsoft cloud, and AI.

Product Launches and Product Expansions

Jun-2022: Google added new features to its previously launched product Vertex. The addition of new features in Vertex AI would boost the deployment of machine learning models in organizations and democratize AI so more people can distribute models in production, driving business impact and continuous monitoring with AI.

Sep-2021: TIBCO unveiled TIBCO Cloud Composer, TIBCO Cloud Discover, and TIBCO LABS Gallery, additions in its TIBCO Cloud portfolio. The new product would enable partners and customers to bring innovations in connecting and developing new applications or defining data, digital strategies, and data management.

Jun-2021: IBM introduced IBM Cloud Pak for Network Automation, a hybrid cloud AI-powered automation software intended to use by communications service providers (CSPs), and can run in varied environments. This product enables IBM to provide better services to its clients in the telco industry, by using AI-powered automation which solves the problem of limited automation and less real-time visibility.

Scope of the Study

Market Segments covered in the Report:

By Component

• Solution

• Services

By Vertical

• BFSI

• Healthcare & Lifesciences

• Media & Entertainment

• IT & Telecom

• Retail

• Government

• Automotive

• Others

By Organization size

• Large Enterprises

• SMEs

By Deployment Mode

• On-premise

• Cloud

By Geography

• North America

o US

o Canada

o Mexico

o Rest of North America

• Europe

o Germany

o UK

o France

o Russia

o Spain

o Italy

o Rest of Europe

• Asia Pacific

o China

o Japan

o India

o South Korea

o Singapore

o Malaysia

o Rest of Asia Pacific

• LAMEA

o Brazil

o Argentina

o UAE

o Saudi Arabia

o South Africa

o Nigeria

o Rest of LAMEA

Companies Profiled

• Microsoft Corporation

• Google LLC (Alphabet Inc.)

• IBM Corporation

• SAP SE

• Amazon Web Services, Inc. (Amazon.com, Inc.)

• Salesforce.com, Inc.

• Meta Platforms, Inc.

• SAS Institute, Inc.

• Tibco Software, Inc. (Vista Equity Partners)

• QlikTech International AB

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