Developing Thailand’s National Strategy for AI
All around the world, government leaders have begun to grasp the importance of AI as a unique and very powerful technology that will have massive impact on their futures, and many are now fully engaged in defining strategies and implementing policies to take advantage of AI’s exceptional capabilities, while attempting to protect their societies from the massive economic and social disruptions that it could readily cause. Indeed, when you look at what’s happening in dozens of nations you see a powerful wave forming.
And although they’re critically important for the future, many of these initiatives are prepared and implemented without much public notice, because technology policy just doesn’t have the juicy ring of scandal and discord that global media now thrive on. But we should be paying attention, because by all reckoning, AI will indeed be the defining technology of the coming decades, exerting enormous influence on how we live and work. It may even be the defining technology of the 21st century.
As we have mentioned in prior news updates, we’ve been working since December with leaders from throughout Thailand’s government to help them define the best strategies to adopt and regulate AI in a nation whose culture dates back beyond four millennia. Some government agencies have already done considerable work to define five year goals and initiatives, and our role has been to support specific ministries and agencies in developing targeted initiatives that will support and enhance the overall implementation.
We began our work by having extensive one-on-one conversations with more than twenty leaders from across Thailand’s government. By using an ethnographic approach, we were able to bring forth many insights about the role of a potentially (probably) disruptive technology, AI, that is being introduced into its magnificent culture. We then held a strategy workshop in Bangkok that honed in on key initiatives that ministries could use to promote accelerate AI adoption throughout the economy. We also conducted a detailed AI training to give participants hands-on experience with the powers (and limitations) of the technology as it is at present, and to help them understand where it’s headed.
All of these interactions provided an abundance of insights that we are now bringing together into a detailed report. While most of the report is confidential and proprietary, portions are general in nature and we can share them with you here in a slightly edited format.
Again, a key message we want you to take away from this discussion is how much thought is already going into the principles and policies that will be needed to manage AI’s impacts, which gives a strong indication of how strong the impact is likely to be. The AI wave is already large and it’s going to be massive, and we continue to advocate for all institutions – companies, schools, universities, and governments – to think through what’s occurring and get ready for the even bigger stuff that soon to arrive.
One section of the report defines a set of implementation principles for AI initiatives and policies that will enable AI-driven transformation that resonates with the nation’s unique cultural heritage and its social and economic landscapes. Among the ten topics covered in this section, we have removed references specific to Thailand so that we can excerpt portions in this newsletter:
Align with Both National and Global Contexts
Implement an Inclusive Strategic Framework for AI
Embed Responsible AI Principles
Clarify Data Ownership and Privacy Protections
Enable Secure and Responsible Data Sharing
Inter-Ministerial Coordination
Create the Essential Nationwide Digital Infrastructure
1. Align with Both National and Global Contexts
Conversations with leaders in multiple ministries made it clear that AI must not operate in isolation. Instead, it must harmonize with broader ministry agendas that prioritize moving the country from a resource-driven economy to one fueled by innovation and technology. Several interviewees, including policy experts, underscored the importance of aligning AI investments with the national strategies to ensure long-term impact on key sectors such as agriculture, healthcare, and manufacturing. This alignment helps channel resources more efficiently and avoids the pitfall of “random acts of AI”—isolated projects lacking cohesive policy backing.
Equally important is situating the nation within the rapidly evolving global AI arena. Discussions with private-sector leaders highlighted a sense of urgency. Advanced economies are already using AI to drive competitiveness, and the nation risks lagging behind if it does not move swiftly. However, rather than simply replicating Western or East Asian models, many advocated leveraging the nation’s unique cultural assets, particularly its Buddhist ethical principles. The result is a dual mandate: keep pace with international AI developments while carving out a leadership role in ethical and culturally informed AI adoption.
2. Implement an Inclusive Strategic Framework for AI
One of the most resonant themes throughout the interviews, particularly in community workshops in rural provinces, is the imperative to make AI work for everyone. Inclusivity, as a guiding principle, extends beyond equitable access to technology. It involves designing AI systems that are sensitive to linguistic, cultural, and socioeconomic differences. Stakeholders from the multiple agencies emphasized that SMEs, smallholder farmers, and rural healthcare providers often lack the resources to adopt AI. Thus, the strategic framework must incorporate micro-grants, localized training, and region-specific AI solutions (for instance, local-language AI chatbots) to bridge urban-rural divides.
Environmental and social sustainability is a recurring priority, reflecting growing awareness of climate challenges and resource constraints. Leaders from universities consistently underscored the need to assure that AI projects should not be pursued for short-term gains at the expense of environmental or societal well-being. Indeed, one agency is already exploring how AI can be integrated into ISO compliance and sustainability certifications, ensuring continuous environmental monitoring and real-time data analytics for greener operations. Sustainability here also ties back to cultural preservation: as rural communities modernize through AI, local traditions and ways of life must be safeguarded—a notion that resonates with Buddhist principles of harmony and balance.
The importance of innovation arose in every interview. Innovation, in this context, is not just about adopting foreign technologies but about fostering homegrown R&D, encouraging creative problem-solving, and enabling startups to flourish. AI-powered solutions for healthcare, tourism, and agriculture can be uniquely tailored to the nation’s needs, whether it’s AI-driven telemedicine for remote clinics or precision farming tools for small-scale rice producers. By embracing risk-taking and experimentation, the nation can develop intellectual property and expertise that differentiate it on the global stage.
3. Embed Responsible AI Principles
Across workshops and community dialogues, participants expressed concern about the potential risks of AI, from privacy breaches to biased decision-making. Consequently, national policy must require risk assessments for critical AI deployments in sensitive areas like law enforcement, healthcare, or financial services, agencies must conduct risk evaluations that weigh AI’s benefits against potential harms.
Policy must also promote transparency in public sector AI. Citizens have a right to know how government AI systems impact them, whether it’s an algorithm deciding welfare benefits or an AI-based traffic management system. The policy will encourage public disclosure of AI models, data sources, and performance metrics.
4. Clarify Data Ownership and Privacy Protections
Data is essential to AI, but it also raises sensitive questions about privacy and sovereignty. In our interviews, representatives from government, hospitals, and tech startups all underscored the urgency of well-defined data rules. Building on the existing statutes, the government must refine regulations to address AI’s unique challenges, such as automated profiling and large-scale data aggregation. Civil society groups who participated in the interviews emphasized the need for robust enforcement and clear redress mechanisms for citizens.
AI systems often rely on data from multiple sources, including government agencies, private companies, and individuals. The policy framework must define fair licensing agreements, ensuring that data owners are compensated or acknowledged. This was a key concern for agribusiness stakeholders, who worry about how shared data (e.g., farm yields, supply chain info) might be monetized without farmers’ consent.
5. Enable Secure and Responsible Data Sharing
Many stakeholders noted that without efficient data sharing, AI initiatives stall due to fragmented datasets and siloed information. The policy framework will thus facilitate anonymized data exchanges. For example, government agencies can host “data trusts” or secure data platforms that strip identifying information, making large datasets safely available for AI research and development. This approach addresses privacy concerns while spurring innovation, a balance strongly advocated by both industry and privacy advocates.
As AI systems handle sensitive personal or national security data, robust cybersecurity protocols become essential. The roadmap envisions dedicated guidelines for encryption, incident response, and secure cloud infrastructure. Government agencies and private tech companies would collaborate on joint cybersecurity exercises, mirroring recommendations from financial sector stakeholders worried about cyber threats to digital banking.
Non-sensitive datasets, such as traffic flows, weather patterns, or agricultural yields, can be released publicly under open data licenses. Interviewees, especially from the academic and startup communities, highlighted that free access to quality data can catalyze local AI innovation.
6. Examples of Inter-Ministerial Coordination
AI and digital transformation efforts cut across traditional ministry boundaries, spanning manufacturing, education, industry, finance, ICT, and more. Achieving policy coherence requires formal structures for inter-ministerial coordination and integrated execution. Global examples illustrate how high-level bodies and cross-agency strategies ensure all facets of government work in concert on AI policy.
Central Coordinating Committees/Councils: Many countries have created national AI councils or committees chaired by top leaders to align policies across ministries. For example, Japan set up a Strategic Council for AI under its Cabinet Office, comprising ministers of Economy (METI), Communications (MIC), Education (MEXT), etc., to implement its AI strategy holistically (covering R&D, talent, industrialization). South Korea launched a Presidential Committee on the Fourth Industrial Revolution (PCFIR) in 2017, which brought together multiple ministries and private experts to steer AI and Industry 4.0 initiatives. This top-down approach signaled that AI was a national priority and facilitated joint programs such as education reforms for AI talent.
Joint Ministerial Units: The UK’s Office for AI is an example of an inter-ministerial execution team. By reporting jointly to two Secretaries, Business and Digital, it ensures both economic and technological objectives are balanced. Another example is Canada’s AI Advisory Council, which was jointly convened by the Innovation Ministry and Treasury Board to influence both innovation policy and public service adoption.
Dedicated Ministry vs. Mainstreaming: Some nations assign one ministry to lead, but embed responsibilities in others. France’s AI strategy is led by the Ministry of Economy and Finance (which houses digital affairs), but includes specific actions for the Ministry of Higher Education for curricula, Ministry of Labour for workforce training, etc. To coordinate, France appointed an Inter-ministerial Coordinator for AI under the Prime Minister, who regularly gathers representatives from each relevant ministry to check alignment with the national AI plan. Germany took a similar approach. Its national AI Strategy from 2018 was jointly drafted by three ministries, Education & Research, Economic Affairs, and Labor, and is overseen by an inter-ministerial committee that meets semi-annually, ensuring coherence across research funding, industrial support, and labor market policies.
Cross-Agency Funding Pools: Policy coherence is often achieved by implementing joint funding mechanisms. In Singapore’s National AI Programme, initiatives are funded through a central pool called the National Research Foundation and Digital Government funds, but executed by various agencies.
Coherence in Policy Documents: A sign of good coordination is when strategies of different ministries reference and reinforce each other. Estonia provides an example. Its Industrial Digitalisation Roadmap, Education Digital Strategy, and AI Strategy were all aligned under a unified vision. Malaysia’s National Fourth IR Policy (2021) was developed by a cross-ministry taskforce and helped each ministry to create action plans that feed into one another. The Ministry of Science handles AI R&D, the Ministry of Human Resources handles reskilling, and both report progress to the same National 4IR Council.
Inter-Ministerial Execution Teams: On a working level, countries sometimes deploy joint teams to implement specific initiatives. For instance, the Netherlands formed a joint government team for its public-private “AI Innovation Hubs” involving economic affairs officials and regional authorities in one team. An AI program in Finland is the famous “Elements of AI” training for citizens, which was jointly created by the Ministry of Economic Affairs and Ministry of Education, with a shared project team.
Legal/Regulatory Coherence: Coordinating on policy also means ensuring that regulations are aligned. For example, data governance and AI ethics involve justice, digital, and industry ministries. Singapore’s AI governance framework was developed by GovTech and PDPC, its data regulator, with input from economic agencies. This balanced innovation and privacy, while the Smart Nation ministerial committee oversaw it. The European Union’s approach achieves coherence by requiring each country’s strategy to cover all sectors, and by fostering a Coordinated Plan on AI among Member States.
High-Level Champions: Having a champion at the top, such as a Prime Minister, Deputy PM, or Minister with cross-cutting authority is clearly a best practice. In the United Arab Emirates, the appointment of a Minister of State for AI ensured that there is a cabinet-level official whose sole job is to coordinate AI efforts across ministries. Similarly, some nations have appointed a Chief Digital Officer or Chief Data Officer who works across government departments to implement AI tools in public services, ensuring the public sector leads by example and speaks with one voice on AI adoption.
Communication and Joint Stakeholder Engagement: Many countries form Public-Private partnership bodies, such as France’s Hub France IA or Germany’s Platform Industrie 4.0 that include multiple ministries and industry reps to help coordinate actions such as standard-setting, talent exchanges, and pilot projects.
Table 1. Examples: Governance Structures for AI Policy
7. Examples of the Essential Nationwide Digital Infrastructure
It will be essential for future economic success to have a robust digital infrastructure that is accessible to all throughout the nation, including all large cities and all regional areas. This will serve the needs of large business and SMEs alike. Below are examples drawn from around the world that show how various nations have worked to implement nationwide coverage for high speed internet access.
Nationwide Broadband Programs: It is common for nations to implement comprehensive broadband plans. Malaysia’s JENDELA initiative (2020–2025) focused on expanding 4G mobile broadband to greater than 96% of its populated areas, essentially ensuring nearly all industrial hubs and communities are online(1). By end-2022, Malaysia had achieved 96.9% 4G coverage and upgraded mobile speeds above 100 Mbps. For the ~3% of areas still lacking terrestrial coverage, Malaysia deployed satellite broadband to 839 rural communities. Similarly, South Korea and Japan have had longstanding programs, and today more than 70% of broadband subscriptions are fiber-optic, and even rural factories typically have fiber or 5G connectivity(2). The EU has set a goal for gigabit internet for all households and 5G in all populated areas by 2030(3).
Investment Levels & Public-Private Partnership: Providing broad digital infrastructure often requires multi-billion dollar investments. Malaysia allocated roughly RM28 billion over 5 years for JENDELA by combining government and telco funding to upgrade towers, fiberize base stations, and launch satellites(4). Nigeria is targeting 70% internet penetration by 2025, up from 44% in 2024, and is investing heavily in rural fiber and 4G expansion to achieve this(5). Many countries use Universal Service Funds, levies on telecom operators, to finance rural connectivity for SMEs. Sequencing is typically “urban-first.” Operators roll out fiber and 5G in dense urban/industrial areas where immediate demand from factories and tech parks ensures ROI, and then governments incentivize extension to underserved regions via subsidies or coverage obligations. Cluster-focused rollout is also common. Vietnam, for instance, ensured all major industrial parks and export processing zones had fiber broadband early on, recognizing that those clusters house thousands of SMEs.
Urban Hubs and Industrial Parks: Prioritizing connectivity in key industrial zones can rapidly increase the percent of SMEs with access. China and India implemented programs to connect industrial parks with fiber. Eastern Europe leveraged EU structural funds to bring broadband to industrial towns. Latvia achieved greater than 70% of broadband users on high-speed fiber by pushing rollout in smaller cities as well(6). Ensuring 5G in manufacturing zones is also a priority. Germany’s 5G Strategy reserved spectrum for industrial use and saw 5G private networks deployed in major factories, assuring that SMEs in their supply chains can connect to advanced networks.
Rural Innovation Hub Models: Infrastructure alone may not guarantee usage; many countries pair connectivity with physical or virtual hubs that help rural businesses utilize it. Rural innovation hubs are facilities such as co-working spaces or tech centers in smaller towns that offer high-speed internet, shared labs (IoT/AI labs), and training. Ireland launched regional innovation hubs to support rural startups and SMEs, providing office space with gigabit internet and on-site advisors for digital projects(7,8). The Scotland 5G Centre set up a rural testbed hub where local agri-food SMEs can experiment with 5G and AI solutions like precision farming drones with technical support(9). These hubs effectively bring the benefits of infrastructure to SMEs’ doorsteps. In developing countries, a parallel are telecenter networks. For example, Malawi’s rural innovation hub initiative teaches basic digital skills to youth and small business owners in villages, creating a pipeline of digitally aware entrepreneurs who can utilize the new infrastructure. India’s Common Service Centers (CSCs) consists of more than 400,000 kiosks nationwide to provide rural citizens and businesses with internet access and e-services.
Sequencing and Clustering Strategies: Many governments take a phased approach by first focusing on connecting all cities and major industrial districts, then the 20-30% in rural areas with targeted programs. Another strategy is “leapfrogging” in rural areas. Mexico and Indonesia have used satellite broadband in remote manufacturing outposts (mining, plantations) to quickly reach near-universal coverage for businesses.
Typical Coverage Metrics: Countries that are leaders report very high SME connectivity. For instance, South Korea effectively has near 100% of businesses with broadband (given its nearly total 4G coverage and highest fiber penetration globally.(10) EU surveys show that in digitally advanced nations such as Denmark and Sweden, more than 90% of SMEs have broadband and around 75% have at least basic digital tool usage(11,12).
Investment Sequencing: In dense areas, a single fiber backbone or 5G tower can serve dozens of SMEs at relatively low cost, while reaching one remote factory might cost tens of thousands of dollars. Governments often subsidize the marginal costly connections: e.g., Malaysia subsidized satellite kits for remote villages at ~RM40k each, which becomes feasible when the bulk of population is covered by market-driven fiber/4G. Overall, an emerging economy might invest on the order of $1–2 billion per year in telecom infrastructure (public + private) to substantially improve SME connectivity. The returns are seen in GDP growth. Studies show every 10% increase in broadband penetration boosts GDP ~1–2% in developing countries(13). A large portion of that improvement comes due to increased SME productivity.
Public Wi-Fi and Community Networks: As a complementary approach, some governments provide free or low-cost public Wi-Fi in industrial towns and marketplaces. The Philippines and Indonesia have “Free Wi-Fi for All” programs targeting public places in provinces. While not a substitute for dedicated broadband at factories, these community networks ensure even the smallest micro-enterprise can get online.
Table 2. Examples: Digital Infrastructure Coverage & Investment
Conclusion: Achieving a Positive AI-Driven Transformation
AI will almost certainly have a transformative impact across the globe. All nations will be impacted, and most will be impacted in a massive way. The economy is shifting, knowledge is shifting, work is shifting, and social interactions are shifting. But are they shifting for better, or for worse?
Effective adoption of AI as a positive force and as a national priority will depend upon cohesive policies, cross-sector collaboration, and sustained investment in both people and technology. If implemented with transparency and cultural sensitivity, these policies will accelerate innovation, and enable society to attain AI proficiency within a broader vision of equitable, sustainable development.
Throughout the discussions on this project, another consistent theme also emerged: trust is paramount. Citizens, businesses, and global partners will only embrace AI if they believe it serves the public interest, respects privacy, and upholds fairness. A policy framework shaped by multi-stakeholder engagement ensures that AI is developed in alignment with key principles of inclusivity and compassion, while also meeting global standards of innovation and accountability. If executed diligently, these policies will not only secure a place as a regional AI leader but also foster an environment where the benefits of AI reach into every facet of society.