The global AI governance market is projected to grow at a compound annual growth rate (CAGR) of 45.3% during the forecast period, increasing from an estimated USD 890.6 million in 2024 to USD 5,776.0 million by 2029. This growth is largely driven by rising regulatory compliance pressures encouraging organizations to implement governance frameworks, heightened awareness of risk mitigation leading to greater investment in AI governance tools, increased adoption of AI governance in regulated industries, and growing demand for trust and transparency within the AI governance market.
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Regulatory pressure and demands for compliance are driving the AI governance market as governments around the world roll out tougher regulations related to AI. For example, the European Union’s AI Act had subjected risk assessments and compliance audits to AI systems, particularly in high-risk sectors like health and finance, thereby increasing demand for the governance framework. Organizations also run the risk of facing reputational damages linked with prejudiced or harmful AI output. A notable example is the controversy caused by OpenAI GPT models, which flagged misinformation and biased data concerns, making businesses adopt robust AI guard rails. On similar note, Amazon’s discontinuation of its biased AI recruiting tool demonstrate the reputational and financial risks of ungoverned AI. Another major reason for market expansion is the uptick in AI adoption across highly regulated industries, especially BFSI and healthcare. Industries operating in these sectors are under immense regulatory pressure to comply with dynamic regulations, leading to increased affinity towards AI governance tools.
By product type, data governance tools will account for largest market share in 2024 owing to robust data provenance and lineage capabilities.
Data governance tools are poised to account for the largest market share in the AI governance market, as these tools help an organization track data quality, provenance, and bias within AI development training data. This is important in order to prevent bias results being generated from AI systems. For example, data governance tools may apply profiling techniques to the dataset in order to ensure fairness, and also put in place data lineage to indicate potential problems with data sourcing. As an increasing number of AI regulations call for documentation, tracking and record keeping especially on the data that feeds AI systems, data governance has become paramount. Data governance also assists enterprises in compliance with regulations through robust AI data traceability and accuracy. Additionally, the metadata repository feature in data governance tools offer centralized catalogs and controls of metadata for data visibility across an organization to ensure trustworthy and responsible AI implementation.
The demand for ethical AI use across ML platforms and generative AI models will push software & technology providers as the fastest growing end user segment during the forecast period
Software & technology providers are poised to become the fastest growing end user segment in the AI governance market, buoyed by rapid adoption of AI governance tools to make their AI systems trustworthy and ethical. The rising regulatory scrutiny and the expanding reach of data privacy laws like GDPR and CCPA has also accelerated governance frameworks being adopted across such players. For instance, Microsoft has created an internal AI ethics working group to implement strong ethical guardrails across its AI offerings. On a similar note, Google has formed AI governance framework for developing fair, explainable, and ethical AI solutions. There are also expectations from stakeholders who demand that technology companies create AI responsibly. With AI regulations likely to disrupt every software vendor, incorporating ethical norms and regulation is now of extraordinary importance for technology businesses to maintain the brand’s trust and growth.
North America is set to hold the largest market share in 2024, fueled by a strong regulatory environment and increasing investments in responsible AI deployment
North America has emerged as the largest regional market for AI government adoption. Federal funding on AI governance in North America crossed USD 1 billion in 2023, indicating a growing interest in responsible AI research. Industries with strict regulations such as healthcare and banking are leading in the implementation of governance, with 45% of healthcare providers mentioning regulatory compliance as a key business requirement. Businesses are forced to implement governance frameworks due to rising regulatory requirements like NIST’s AI Risk Management Framework and the California Consumer Privacy Act (CCPA). More than half of businesses expect more stringent AI rules in the next five years, with 62% citing data privacy compliance as a main factor for implementing governance. Also important is consumer confidence, as 78% of American consumers favor brands that utilize ethical AI. Businesses such as Google and Microsoft are implementing governance to guarantee transparency and establish trust. Additionally, organizations are prioritizing fairness in their AI systems and have turned to tools like IBM’s AI Fairness 360 to address the need to mitigate AI bias, with 56% of businesses doing so. Moreover, financial institutions are particularly focused on risk management, giving priority to governance for addressing AI-related risks.
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Unique Features in the AI Governance Market
Regulatory pressures are a significant force shaping the AI governance landscape. Governments and regulatory bodies worldwide are enacting policies and laws to ensure responsible AI usage, which has prompted organizations to adopt AI governance frameworks.
Organizations are increasingly aware of the risks associated with AI, such as bias, data privacy concerns, and unintended consequences. This heightened awareness has led to a surge in investment in AI governance tools that help mitigate these risks. These tools ensure that AI systems are transparent, accountable, and designed to minimize negative outcomes, making risk management a crucial differentiator in the market.
The AI governance market is seeing significant growth in highly regulated industries, such as healthcare, finance, and defense, where compliance, safety, and security are critical. In these sectors, the adoption of AI governance frameworks is crucial for ensuring that AI applications meet strict regulatory requirements, and this trend is pushing the demand for specialized governance solutions tailored to industry-specific needs.
A unique feature of the AI governance market is the increasing emphasis on trust and transparency. As AI systems become more pervasive, stakeholders—ranging from end-users to regulators—are demanding clearer explanations of how AI models make decisions. Ensuring that AI systems are interpretable, explainable, and transparent is becoming a core requirement, and this is driving the development of governance solutions that focus on building trust in AI technologies.
Major Highlights of the AI Governance Market
One of the major highlights of the AI governance market is the crucial role that regulatory compliance plays in driving its adoption. As governments and international bodies implement strict regulations surrounding AI, organizations are compelled to establish robust governance frameworks. These frameworks help businesses comply with emerging rules and guidelines related to AI ethics, bias prevention, data privacy, and accountability, ensuring they avoid legal risks and penalties.
Organizations are increasingly aware of the risks associated with AI, such as potential biases, discrimination, and privacy violations. This awareness has spurred significant investment in AI governance tools that help mitigate these risks. Companies are investing in solutions that monitor AI systems to ensure they adhere to ethical standards, perform risk assessments, and enhance accountability, making risk management a critical highlight of the market.
A standout highlight of the AI governance market is its rapid adoption in regulated industries such as healthcare, finance, and insurance. In these sectors, where compliance and risk management are of paramount importance, the need for strong AI governance frameworks is particularly acute.
The demand for trust and transparency in AI operations is another major market highlight. Stakeholders, including customers, employees, and regulators, are increasingly focused on understanding how AI systems make decisions. AI governance solutions that offer greater transparency, explainability, and fairness in AI processes are becoming critical in building trust among users, driving further adoption of governance frameworks across industries.
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Top Companies in the AI Governance Market
Some leading players in the AI governance market include Microsoft (US), IBM (US), Google (US), Salesforce (US), SAP (Germany), AWS (US), SAS Institute (US), FICO (US), Accenture (Ireland), Qlik (US), H2O.AI (US), Alteryx (US), DataRobot (UK), Dataiku (US), Domino Data Lab (US), SparkCognition (US), Collibra (US), OneTrust (US), Quest Software (US), and Fiddler AI (US). These players have adopted various organic and inorganic growth strategies, such as new product launches, partnerships and collaborations, and mergers and acquisitions, to expand their presence in the AI governance market.
Microsoft
Microsoft has established a robust presence in the AI governance market through its strategic focus on responsible AI. The approach to AI governance from the firm emphasizes openness, fairness, and accountability, which is then fortified by tools such as Microsoft’s AI Responsible Innovation and Azure OpenAI services. Major competencies include embedding AI values into the development of its products, preserving outstanding data security and especially, providing support for achieving AI standards such as the Microsoft Responsible AI Standard. Horizontal integration provides the possibility for buying companies such as Nuance, which would increase the strength of its healthcare AI capability, for example. Liaisons with OpenAI and governments extend its reach while ensuring that it is regulating by worldwide standards. Collaborations and partnerships make sure that its solutions are not only technologically advancing but also aligned with the greatest ethical standards in finance, health care, and public services. These efforts position the company in a good spot regarding dominance in AI governance as both vertical and horizontal strategies build on its impact.
Google has impacted the AI governance field through strategic efforts towards responsible AI development, transparency, and promotion of global policies. One of the major strategies includes partnership with international organizations in ways that call for an ethical governance structure for AI. Through its machine learning, natural language processing, and cloud infrastructure expertise, Google can influence AI governance through the development of robust tools for auditing and AI risk management. Key activities would encompass the establishment of the partnership on AI and launching products such as Google Explainable AI to build transparency. The company is engaging in horizontal integration through the acquisition of AI ethics start-ups and vertical integration through creating governance mechanisms of AI in its core products like Google Cloud and AI-related solutions. In addition, engagement with academic institutions and AI research labs makes it likely to develop standard international standards for AI governance.
IBM
With strategic initiatives like developing watsonx.governance, ensuring its compliance, explainability, and lack of bias, IBM has successfully placed itself in the AI governance market. High competencies include advanced AI technology, data management skills, and industry expertise – all these ability are provided to deliver scalable, sectoral AI solutions. Core activities include governance frameworks development, managing the full lifecycle of AI, and regulatory compliance. IBM invests in joint ventures and partnerships with industry leaders and academic institutions for increased AI governance standards. The company employs vertical integration wherein AI governance is ensured to be embedded with its broader cloud and data-based systems. The same delivers an end-to-end solution to its enterprise clients. A broad approach, therefore, would be the ethical deployment of AI across several industries by strengthening the position of IBM on responsible AI.
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