As artificial intelligence (AI) becomes increasingly integral to global economies and societies, the need for effective AI governance has never been more urgent. “In 2026, AI governance will be about much more than regulatory compliance,” said Dera Nevin, managing director in the technology segment at the international firm FTI Consulting, in January. “‘It will be integral to doing good business.”

“Organizations that build governance into how they develop and deploy AI will gain a competitive edge and be better positioned to reduce related regulatory and litigation exposures,” Nevin told Governance Intelligence, a digital content and events company.

In that regard, a December 2025 report by the World Bank explores the emerging landscape of AI governance, showcasing an overview of key considerations, challenges, and global approaches to regulating AI. “The rapid advancement in AI technologies, coupled with their widespread adoption across many sectors such as healthcare, finance, agriculture, and public administration, presents both unprecedented opportunities and significant risks,” the report says.

According to the report, AI could contribute $13 trillion to the world’s economy by 2030 and increase global GDP by 1.2%.

Effective governance is increasingly critical as the use of AI — particularly generative AI — explodes. ChatGPT, for example, reached 100 million users within two months of its release in November 2022. “This unprecedented rate of adoption not only showcases the transformative potential of AI, but also sets the stage for a major shift in global connectivity and economic systems,” the report notes.

It emphasizes that robust governance frameworks are increasingly required to ensure AI is developed and deployed in an ethical, open, and accountable manner. As countries navigate this complex landscape, the report highlights the need to “encourage innovation by mitigating risks like bias, privacy violations, and lack of transparency.”

Enabling foundations

According to the report, there are important enablers for AI ecosystems. These include reliable digital infrastructure, a sufficient and stable power supply, supportive policies, and investment in local talent.

Successful deployment of AI in a country requires a robust digital and data infrastructure to support the development, deployment, and scaling of applications across various sectors. “Key components … include high-speed internet, data storage and management systems, and computational power.”

A case in point is India’s AI Mission, with a budget of $1.38 billion that aims to strengthen computational infrastructure and democratize access to AI resources. It focuses on innovation, ethical AI practices, and socio-economic transformation. S. Krishnan, secretary of India’s Ministry of Electronics and Information Technology, told the World Economic Forum in January 2025, “To enable this growth and become a global tech powerhouse, we need collaboration from all stakeholders to leverage Fourth Industrial Revolution technologies for critical challenges on health, education, smart cities, and agriculture.”

Key components of India’s AI mission include a high-end AI computing ecosystem that establishes a scalable infrastructure of more than 10,000 graphics processing units (GPUs) via public-private partnerships to support startups and research. In addition, India aims to create an AI marketplace as a one-stop platform offering AI-as-a-service and pre-trained models to accelerate innovation. Another key component is an application development initiative that promotes impactful AI solutions in critical sectors for large-scale socioeconomic benefits.

Local ecosystems

Building strong and successful local AI ecosystems requires key pillars like research and development (R&D), which is essential for driving innovation. To strengthen R&D efforts, a mix of government funding, private sector investment, and academic collaboration is critical. Public-private partnerships (PPPs) can play a major role by pooling resources, sharing expertise, and enabling large-scale AI initiatives, the World Bank report says.

The report adds that countries should have a vibrant startup ecosystem that provides strong support for startups, ensures access to funding, and offers shared infrastructure such as incubators and accelerators. It also involves creating tools that foster collaboration among industry players, academia, local organizations, and community stakeholders.

“Governments can lead through internal AI adoption or by offering subsidies to address pressing challenges in key sectors like healthcare, education, the environment, and energy.” For instance, India’s AI for All initiative — a self-learning online program — aims to raise public awareness and promote inclusive development, while spotlighting AI startups tackling challenges in healthcare, language translation, and agriculture, the report showed.

Similarly, governments can create an enabling environment for AI investment through supportive policies, seed funding, co-financing, incentives, and even public procurement measures such as pre-certifying AI vendors, as seen in Canada’s list of AI suppliers.

Human capital

Building strong AI ecosystems requires investing in people. Governments need to prioritize capacity building within institutions to ensure they have the expertise to effectively regulate and govern AI. “Education and training programs should go beyond technical skills to include ‘soft skills’ — such as judgment, critical thinking, and emotional intelligence — that AI cannot easily replicate.”

Lack of digital literacy remains a major barrier to AI adoption, particularly in low-income countries. Addressing this foundational challenge would enable broader participation in AI-driven economic opportunities, says the report.

Governments must adapt education and training systems to “prepare workforces for the global AI value chain while mitigating risks of job displacement due to automation.” It highlights the potential of opportunities across skill levels — from data collection and preparation to machine learning research and managing cloud infrastructure.

While some outsourced jobs may face automation, countries can focus AI adoption on technologies that leverage labor and meet domestic needs. According to the report, this will require a dual approach: “upskilling current workers to enhance existing capabilities and reskilling individuals for emerging opportunities.” At the same time, capacity building within government institutions remains essential to ensure effective governance and regulation of AI technologies. Additionally, it is crucial to focus on capacity building within government institutions to ensure they have the expertise required to effectively regulate and govern AI technologies.

Governing AI challenges

The report says it can be challenging to keep pace with rapid technological advancements and AI development. Stanford University’s AI Index Report 2024 showed that investment in generative AI accelerated to $25.2 billion in 2023, with applications spanning customer support, healthcare, autonomous vehicles, fintech, drones, legal tech, and manufacturing.

According to the report, this rapid evolution makes it hard for governance frameworks to keep up by developing new laws and policies, a process that could take months or even years, “during which AI technologies continue to advance, creating governance gaps.”

Another challenge is the limited technical expertise within governments, which creates significant knowledge gaps. This, in turn, hinders policymaking processes, the report notes. In addition, higher salaries in the private sector are fueling a brain drain, “with a significant proportion of AI talent opting for private or international roles over government positions,” said the report. This talent shift further limits the public sector’s ability to build in-house expertise and effectively govern emerging technologies.

The report shows “only 0.7% of new AI PhD graduates in the United States and Canada choose to work in government roles. This lack of expertise makes it difficult to draft effective policy, regulatory, and governance measures.”

Notably, AI is being utilized in different sectors, each with unique requirements, risks, and operational contexts. The healthcare sector, for example, prioritizes patient privacy and safety, necessitating stringent regulations to protect sensitive health data and ensure the reliability of AI-driven diagnostic tools. On the other hand, the financial sector focuses on detecting fraud, managing risk, and ensuring compliance with financial regulations.

“These sector-specific differences highlight the importance of developing customized governance frameworks to ensure responsible and effective AI implementation across diverse domains,” the report says.

Challenges include coordinating multiple stakeholders, which is resource‑intensive and demands strong communication mechanisms. The report adds that ensuring governance keeps pace with rapid technological change — without over‑regulating — remains a key concern.

Striking the right balance in AI governance is critical. Frameworks must promote innovation while effectively mitigating risks, which is a “delicate task,” according to the report. Overly stringent regulations can overwhelm startups with “limited compliance resources, while insufficient governance leaves individuals and society vulnerable to serious risk.”

In that regard, the report emphasized that governing AI means tackling complex ethical, technical, and socio-economic challenges. Therefore, policymakers “must create adaptable governance frameworks that set clear guidelines and safeguards, enabling responsible progress rather than hindering it,” the report noted.

Dimensions for governance

According to the report, there are key dimensions for AI governance. These include ensuring “governance interventions are effective in managing risks and achieving policy objectives without imposing unnecessary burdens, particularly on smaller entities.”

Another key dimension is to apply a human-centric approach to AI governance, placing “the needs and values of people and communities at the center of AI governance and deployment.” The report says human-centric AI governance requires rules that prioritize fundamental rights and consumer interests.

This also requires AI regulatory frameworks to remain agile and adaptive, enabling rapid, iterative adjustments in response to technological and market shifts, the report says.

Private sector leading

As cutting‑edge AI development is largely driven by a small number of major technology companies, the private sector holds a central role in advancing responsible practices. The report says policymakers “need to consult with industry players and bodies to develop a robust AI governance roadmap.”

At the same time, however, “ultimately responsibility for AI policy should remain with the state, acting in the interests of all consumers and stakeholders, and should not be inappropriately delegated to private actors.”

Notably, the report highlights the potential of public–private AI partnerships, which can be valuable when governments require specialized technical expertise from AI practitioners. They can support regulators through training on “the harms posed by the latest AI models and help democratize AI development.”