Artificial Intelligence in New Zealand - Opportunities, Challenges, and Strategic Imperatives

Executive Summary

Artificial Intelligence (AI) is rapidly transforming global economies and holds immense potential for New Zealand's food and fibre sector and healthcare system. However, New Zealand currently faces significant barriers to AI adoption, including conservative attitudes, aging infrastructure, data limitations, and a fragmented approach to research and development. To remain competitive as an exporting nation and to improve societal outcomes, a coordinated national strategy focusing on AI adoption, workforce development, ethical governance, and strategic investment is critical.

Key Themes and Most Important Ideas

1. The Global Acceleration and Economic Imperative of AI

AI is a pervasive and transformative technology with significant economic implications.

Rapid Global Investment & Growth: Global investment in AI is accelerating dramatically, with estimates reaching US$184 billion in 2024 and projected to grow to US$826 billion by 2030, or even US$1.3 trillion by 2032 (Artificial Intelligence: A snapshot of AI in New Zealand). This growth signifies a doubling every 1.7 years.

Economic Impact: Accenture analysis suggests AI could double annual economic growth rates by 2035 in developed economies, with the US potentially seeing an additional US$8.3 trillion Gross Value Added (GVA) by 2035 (Artificial Intelligence: A snapshot of AI in New Zealand).

Productivity Gains: AI software is predicted to quadruple knowledge-worker productivity by 2030, and in agricultural supply chain management and manufacturing, AI could add an estimated US$396.3 million in economic value (Artificial Intelligence: A snapshot of AI in New Zealand).

Competitive Pressure: Countries and businesses that are slow to adopt AI risk being increasingly out-paced by competitors in fast-evolving markets. The consequence of not being an early adopter is becoming subject to whatever economic rents are imposed by whomever owns and controls the AI technology, IP, delivery platforms and applications (Artificial Intelligence: A snapshot of AI in New Zealand,).

2. AI's Transformative Applications Across Sectors

AI applications are already widespread and rapidly expanding in critical sectors.

Food and Fibre System: AI is being implemented across the entire food system value chain, from production to processing, wholesale, retail, and distribution.

Precision Agriculture: AI-driven systems analyse sensor and satellite data for near-perfect accuracy in irrigation, chemical applications, planting, and crop health monitoring. Examples include John Deere's See & Spray Ultimate for targeted herbicide application (reducing use by 77-90%) and autonomous tractors (Artificial Intelligence: A snapshot of AI in New Zealand,).

Animal Agriculture & Aquaculture: AI optimizes feed efficiency, monitors health, predicts disease, and manages breeding programs. Aquabyte uses computer vision for salmon farming to estimate biomass and optimize feeding, with predictions that automatic lice count, and welfare scores will be the industry standard in a few years (Artificial Intelligence: A snapshot of AI in New Zealand,).

Processing & Manufacturing: AI enhances supply chain efficiency, optimizes resource use, predicts equipment failures, and dramatically reduces research time for new product development and regulatory compliance (Artificial Intelligence: A snapshot of AI in New Zealand,).

Supply Chain & Distribution: AI is used for demand forecasting to minimize waste, real-time tracking of perishable products, and optimizing shipping routes (Artificial Intelligence: A snapshot of AI in New Zealand,).

Healthcare: AI offers significant opportunities to address strained health services, support professionals, and improve health outcomes in New Zealand.

Administrative Efficiency: AI can automate low-hanging fruit tasks like scheduling operations, typing notes, and sending patient reminders, freeing up human resources.

Diagnostic Support: Computer vision applications in medical imaging (X-rays, CT, MRI, mammograms) can augment clinical judgment, leading to faster and more accurate results. Volpara Health, a Wellington-based company, is a world-leader in AI image analysis for breast cancer screening, used in over 40% of US breast cancer screenings.

Personalized Care & Monitoring: Wearable devices combined with AI can provide early warnings of health changes (Project Otto, HomeCare) and enable personalized medicine through optimized dosages.

Emergency Services: Machine learning can improve the responsiveness of emergency medical dispatch, with research demonstrating reductions in simulated ambulance response times by up to 75%.

Research & Development: AI, exemplified by AlphaFold's protein folding prediction, can dramatically increase our capabilities in health research by speeding up time-consuming tasks and generating hypotheses.

Generative AI: While currently restricted in patient care, generative AI has potential for clinical education, guiding diagnoses, summarizing patient information, and creating personalized patient communications in various languages.

3. Challenges and Barriers to AI Adoption in New Zealand

Despite the global momentum, New Zealand faces unique and substantial hurdles.

Conservative Attitudes and Limited Familiarity: New Zealand companies are perceived as very conservative in views about AI and slow to adopt compared to other markets. Understanding of AI's broad impact varies widely.

Aging Infrastructure: A common theme from New Zealand based companies was aging plant and machinery serving as a barrier to AI uptake. Many small-to-medium enterprises lack digital systems to generate necessary data for AI.

Data and Cybersecurity Issues: Challenges include the distance to overseas data sources, cybersecurity concerns (often relying on customer internet connections), and the need for data interoperability and open information exchange. In healthcare, concerns exist around data privacy, data sovereignty (especially Māori data), and cybersecurity.

Digital Divide: AI adoption has potential to widen economic performance between tech-driven and traditional, non-tech sectors and between countries. Smallholder farmers and less technologically-enabled countries risk falling further behind.

Workforce Impact: AI can help solve labour shortages on the one hand... but also leads to job displacement on the other. Jobs requiring low skill levels, routine tasks, and few transversal or interpersonal skills are most vulnerable. Administrative, customer service, and secretarial roles face high immediate replacement exposure.

Gender and Age Disparities: Women in administrative and secretarial roles are more vulnerable to displacement. Mid-career (45-49) and early-career (25-29) workers face significant job disruption projections, potentially impacting tens of thousands of New Zealanders.

Ethnicity: Māori, Pacific Peoples, and Asian communities are disproportionately represented in sectors highly exposed to AI (e.g., manufacturing, administrative services, transport), raising concerns about exacerbating inequality.

Social Status and Pay: AI can impact job meaningfulness, with potential for salaries to fall due to increased competition for fewer roles, and a shift in social status for traditionally high-status professions like pilots and surgeons.

Fragmented R&D and Lack of Leadership: New Zealand has limited research in AI, and businesses are not well connected with universities as in other countries. There's a need for sector-wide technology leadership and a national focal point for fostering AI talent and collaboration.

Regulatory Uncertainty: While international AI regulatory trends exist (risk-based approach, policy alignment, collaboration), New Zealand's specific regulatory status is still evolving. The Therapeutic Products Act 2023 will regulate Software as a Medical Device in healthcare from 2026, but criteria for authorization are not yet established.

4. Strategic Imperatives for New Zealand

A proactive and comprehensive approach is essential to harness AI's benefits and mitigate risks.

National AI Strategy: New Zealand needs a clear strategy that deliberately emphasises AI adoption and application rather than foundational AI development, reflecting both economic reality and strategic opportunity.

Workforce Development and Education:

AI-Ready Workforce: Businesses need to proactively develop an AI-ready workforce through trials and training to improve technical capability and prompt engineering skills.

Skills Prioritization: Education should prepare students for STEM fields and subjects that effectively prepare them for working with AI, with a focus on continual skills development rather than one-and-done college degree[s].

AI Literacy: Improve AI literacy among the public, clinicians, and decision-makers to build trust and inform effective adoption.

Retraining and Upskilling: Community and business leaders should prepare upskilling and retraining programmes for displaced workers.

Ethical and Responsible AI Governance:

Te Tiriti o Waitangi and Māori Data Sovereignty: Implementation of AI in healthcare must give effect to te Tiriti, by, among other things, partnering with Māori in its implementation and recognising that Māori data are taonga. Māori data governance models (Te Kāhui Raraunga) emphasize collective rights, which need to be reconciled with individual privacy.

Safety and Effectiveness: AI systems must be safe, not exposing patients to increased levels of risk and effective in achieving health equity. This requires frameworks for assessment, understanding limitations, and robust governance.

Bias Mitigation: Continuous audit and evaluation of potential biases are crucial to ensure AI enhances equity and does not amplify existing inequalities.

Transparency and Trust: Public and clinician trust in AI is paramount. This requires transparent evaluation, clear communication of benefits and risks, and understanding the "black box" nature of some AI systems.

Liability and Control: Clear rules about liability and responsibility are needed, as are guidelines for human supervision, especially as AI becomes more powerful.

Strategic Investment and Infrastructure:

Digital Infrastructure: Prioritize investment in modern digital systems and infrastructure, especially for small-to-medium enterprises, to enable data generation and AI uptake.

Local R&D: Support a Centre of Research Excellence for AI research with a specific focus on healthcare delivery to foster local talent and address national needs.

Data Access and Quality: Maximize quality of national data collection, identify and address data shortages, and establish transparent protocols for health data access for AI development, while safeguarding data sovereignty.

International Collaboration: Engage in global AI governance forums and build strategic relationships to influence responsible AI development and leverage international expertise.

Immigration Policy: Prioritize visas and residency for migrants with AI knowledge and expertise to address talent shortages and foster innovation hubs.

Conclusion

New Zealand is at a critical juncture regarding AI adoption. While the global landscape demonstrates AI's profound economic and societal benefits, New Zealand's current position highlights significant readiness gaps. A concerted, multi-sector effort, guided by a clear national strategy, is essential to overcome these barriers. This includes investing in modern infrastructure, cultivating an AI-literate workforce, establishing robust ethical and regulatory frameworks, and fostering a collaborative environment for research and development. Failure to act decisively risks New Zealand being outpaced by international competitors, impacting its economic competitiveness and its ability to improve the well-being of its citizens.