Introduction
The adoption of generative artificial intelligence (AI) presents a transformative opportunity for economies worldwide. With advancements in machine learning, AI models can enhance productivity, automate routine tasks, and support economic growth. However, achieving AI readiness requires strong institutional, infrastructural, organisational, and ethical foundations.
Government leadership, cultural shifts, and public engagement will play a critical role in shaping AI adoption. This report outlines the key factors that contribute to AI readiness, the potential economic impact of generative AI, and the enablers that will drive successful AI adoption.
Key factors determining AI readiness
Infrastructure
Generative AI models require substantial computational resources, data storage capabilities, and high-speed networks. Establishing a robust digital infrastructure ensures economies can support AI development, deployment, and scalability. High-performance computing, cloud infrastructure, and reliable data transmission networks are critical for processing the vast amounts of data that AI systems require.
Data quality and accessibility
AI models depend on large datasets for training and optimisation. High-quality, diverse, and well-structured data enhances the accuracy and fairness of AI outputs. Efforts to improve data governance, privacy, and security are essential to ensure AI systems operate effectively and ethically. Additionally, organisations must establish frameworks for secure data-sharing mechanisms to maximise AI potential.
Workforce development
The impact of AI on the workforce will vary across industries, with many roles evolving rather than being replaced. To ensure a smooth transition, governments and businesses must invest in reskilling and upskilling programmes. Strengthening digital literacy, technical skills, and AI education will be crucial in preparing workers to collaborate with AI systems and leverage automation for productivity gains.
Unions should take a pro-active engaged approach to the changes being brought by AI. This includes considering radical scenarios that challenge traditional assumptions about their role, representation, and strategies. Unions can play a positive role in managing AI-driven workforce changes by:
Establishing specialised research units to analyse economic shifts and AI’s evolving role.
Recruiting technology experts to enhance understanding of AI’s implications.
Supporting an augmented workforce by identifying roles where human labour remains critical.
Advocating for workers’ health, safety, and privacy in AI-driven workplaces.
Joining AI consortiums to stay informed and influence AI policy developments.
Negotiating fair retirement conditions for workers in roles likely to be replaced by AI.
Facilitating upskilling and reskilling initiatives in collaboration with businesses and governments.
Participating in ethical AI committees to ensure AI adoption aligns with worker rights and fairness principles.
Leadership and strategy
Senior leaders in government and the private sector must play an active role in driving AI adoption. AI strategies should not focus solely on deploying technology but rather on how AI can support broader economic and societal goals. Leadership commitment is critical, as organisations where senior leaders communicate a clear vision for AI adoption are significantly more likely to achieve their desired outcomes.
To successfully implement AI at scale, organisations should:
Ensure AI strategies align with their core mission and objectives.
Encourage collaboration between leadership, IT, and operational teams.
Develop a clear roadmap for AI integration across different functions.
Balance external hiring of AI talent with internal reskilling programmes.
Promote AI literacy across all levels of government and business to facilitate informed decision-making.
Reimagining business processes and career pathways will also be essential. Organisations that adapt workflows and create new AI-driven career opportunities are more likely to realise AI’s full potential. By fostering innovation, reskilling employees, and embedding AI across operations, economies can enhance productivity while ensuring a smooth transition for the workforce.
Ethical considerations
AI brings ethical challenges, including bias, misinformation, and potential misuse. Establishing clear ethical guidelines, regulatory frameworks, and transparency mechanisms will ensure responsible AI development. Organisations must prioritise fairness, accountability, and inclusivity in AI systems to mitigate risks and build public trust in AI technologies.
Integration into business and society
For AI to deliver tangible benefits, it must be effectively integrated into real-world applications. Businesses need to develop AI strategies that align with their objectives and operational processes. Cross-sector collaboration, public-private partnerships, and industry-specific AI adoption frameworks will be key to successful implementation and long-term AI sustainability. Additionally, cultural shifts within organisations and society at large will be necessary to embrace AI-driven transformation and innovation.
Conclusion
AI readiness is a multifaceted challenge that requires coordinated efforts across infrastructure, workforce, ethics, integration, and policy frameworks. While AI presents immense opportunities for productivity and economic growth, its benefits will only be fully realised with strategic planning, responsible implementation, and continuous adaptation to evolving technological landscapes. Strong leadership from governments, cultural adaptation, and stakeholder engagement—including media, unions, and policymakers—will be crucial in driving AI adoption. By addressing the key enablers of AI adoption, economies can position themselves at the forefront of the AI revolution, driving innovation and sustainable development in the years ahead.
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