Governing the AI economy (Part 3)

Part 3 of the series: Designing for the Age of AI

In the first article in this series, Designing for the Age of AI, I explored how technological revolutions reshape institutions and why leadership capability becomes the real lever of transformation.

In the second article, The Real Work of AI Transformation, the focus moved inside organisations, examining how leaders must redesign roles, workflows, and culture so that human capability and artificial intelligence reinforce one another.

This final article widens the lens again.

Artificial intelligence is not only transforming organisations. It is reshaping industries, economies, and the institutional frameworks that guide them. Understanding the future of AI therefore requires examining the broader system within which organisations operate. Governments, industries, large firms, and small businesses all play distinct roles in shaping how the technology evolves.

The AI transition is not simply technological. It is societal.

Technological revolutions reshape entire economies

Throughout history, major technological innovations have reorganised economic systems.

Steam power enabled the industrial revolution. Electricity transformed manufacturing and urban life. Digital technologies reshaped information, media, and global trade.

Economic historian Carlota Perez describes these moments as technological revolutions: periods in which new technologies trigger widespread institutional change across finance, industry, and governance (Perez, 2002). Each revolution follows a similar pattern. Early innovation is followed by experimentation, instability, and institutional adjustment. Over time, new governance systems, market structures, and organisational models emerge that allow the technology to reach its full potential.

Artificial intelligence appears to be entering this stage.

According to the McKinsey Global Institute, generative AI could contribute between 2.6 trillion and 4.4 trillion US dollars annually to the global economy through productivity gains across sectors including customer operations, software engineering, marketing, and research (McKinsey Global Institute, 2023). Yet those gains depend on the broader system adapting to the technology. Infrastructure, regulation, workforce capability, and governance must all develop in parallel.

Technology alone does not determine outcomes. Systems do.

The role of government in technological transformation

Governments have always played a central role in enabling technological revolutions.

Public investment funded the early development of railways, electricity grids, and telecommunications networks. The internet itself emerged from publicly funded research, particularly through defence and research initiatives in the United States (Mazzucato, 2013). Artificial intelligence will require similar institutional support.

Governments influence technological transitions in three important ways.

The first is infrastructure enablement. AI depends on high-performance computing infrastructure, data ecosystems, and research institutions. Many governments are now investing heavily in domestic AI capability to ensure economic competitiveness and technological sovereignty. Deloitte forecasts that global investment in sovereign AI computing infrastructure could approach one hundred billion US dollars annually by the end of the decade (Deloitte, 2026).

The second is regulation and governance. AI raises complex questions about accountability, bias, safety, and transparency. Governments play a critical role in establishing frameworks that ensure AI systems are deployed responsibly whilst still enabling innovation. In New Zealand, regulation is still emerging. The Government launched the Public Service AI Framework in 2025 to encourage greater use of AI in public services, forming part of New Zealand's broader Strategy for Artificial Intelligence. Yet public trust remains low. Research by KPMG found that only 34 per cent of New Zealanders say they trust AI systems, compared to 46 per cent globally, and 81 per cent believe AI should be regulated (IoD, 2025). This signals both an urgent need for governance and a genuine opportunity for organisations that earn trust through transparent, accountable practice.

The third is workforce transition. Technological change inevitably reshapes labour markets. Governments therefore influence how societies adapt through education systems, workforce development programmes, and labour policy. These public systems shape how quickly and safely technological innovation spreads.

Industries as ecosystems

Artificial intelligence is also transforming how industries operate.

Traditional industrial models often assumed that companies competed primarily within clearly defined sectors. Digital technologies have blurred those boundaries. Today many industries operate as interconnected ecosystems of platforms, suppliers, partners, and developers.

Research on global value chains shows that digital technologies are increasingly restructuring how value is created and captured across industries (Gereffi et al., 2022). Data has become a strategic resource that links firms across supply chains and ecosystems. Artificial intelligence accelerates this shift. Many AI applications rely on shared data platforms, software ecosystems, and cross-industry collaboration. As a result, competitive advantage increasingly depends not only on internal capability but also on the ability to operate effectively within complex networks.

Industries, in this sense, are becoming systems rather than isolated markets.

The role of large and small organisations

Large organisations play a distinctive role in technological transitions. Established firms often possess advantages that smaller competitors do not: access to capital, large customer bases, regulatory expertise, and extensive data resources. These assets can allow large organisations to scale new technologies quickly once they commit to transformation.

Yet large organisations also face structural challenges. Legacy systems, established processes, and risk management frameworks can slow innovation. Research on digital disruption shows that incumbent firms often respond to new technologies in stages, with incremental adaptation typically preceding deeper business model transformation (Warner & Wäger, 2019). Artificial intelligence may force large organisations to accelerate this process. Firms that successfully integrate AI into their operations may unlock significant productivity gains. Those that fail to adapt risk being outpaced by more agile competitors.

Small and medium enterprises also play a critical role. Their flexibility allows them to adopt new tools quickly and explore novel business models. In the AI era, many smaller firms will access AI primarily through cloud platforms and software services, which may allow them to use capabilities previously available only to large organisations. At the same time, barriers related to digital skills, data access, and investment capacity remain real. Public policy and industry collaboration play important roles in ensuring smaller firms are not left behind.

Governance as a strategic capability

As AI systems become more capable and more deeply embedded in how organisations operate, governance moves from a compliance function to a strategic imperative.

This distinction matters. Many organisations treat AI governance as a risk management exercise: a checklist of policies and controls designed to avoid harm. That framing is too narrow. Strong governance is what allows organisations to move with confidence, to scale AI initiatives beyond isolated pilots, and to earn the trust of customers, employees, and regulators.

As the Institute of Directors New Zealand has observed, governance is the mechanism that grants permission to leverage AI's benefits. It is how organisations earn the social licence to use AI responsibly. Without it, even well-intentioned initiatives can create hidden risks (IoD, 2025).

The IoD's work on AI governance identifies a particular challenge that boards must confront directly: the gap between AI use and AI oversight. In many organisations, employees are already using AI tools, often without board-level awareness or policy frameworks to guide them. Staff may be sharing sensitive information with external AI platforms, relying on unchecked outputs in board papers, or inadvertently breaching intellectual property obligations. Every organisation already has an AI risk profile, whether or not its board has formally acknowledged one (IoD, 2025).

Responsible governance therefore begins with visibility. Boards need to understand which AI tools are in use across the organisation, what data is flowing into them, and whether those choices align with the organisation's risk appetite and values.

What decisions should humans retain?

One of the most consequential governance questions organisations face is not whether to use AI, but where to draw the boundaries of its authority.

AI systems can influence decisions related to credit, employment, healthcare, customer service, and public services. Some of these decisions are routine and low-risk, where automation adds genuine efficiency. Others carry significant ethical, legal, and reputational weight.

Directors and leaders must be deliberate about which decisions they are comfortable delegating to machines and which require human judgement to remain in the loop. This is not simply a technical question. It is a values question.

Scholars studying responsible AI emphasise the importance of transparency, fairness, accountability, and human oversight in AI systems (Floridi et al., 2018). In practice, this means organisations must be able to explain how automated decisions are made, identify and address sources of bias, and ensure that meaningful human review is available where the stakes are high.

The IoD's guidance for directors highlights that those who rely on AI outputs without proper oversight risk exposing themselves to personal liability (IoD, 2025). This is a practical reminder that delegating a decision to an algorithm does not relieve a director or executive of accountability for its outcome.

Building AI literacy at the governance level

Governance cannot keep pace with AI if the people responsible for it do not understand what they are governing.

A consistent finding across IoD research is that many boards face a digital literacy gap. Directors bring deep expertise in finance, strategy, and risk, but AI introduces a steep new learning curve. Building digital literacy at board level is now essential. Directors do not need to be technologists, but they do need to understand enough to ask the right questions, challenge management assumptions, and ensure responsible adoption (IoD, 2025).

This includes understanding the difference between the types of decisions AI can support well and those where human judgement remains essential. It also means boards being alert to the pace of change: AI capabilities are evolving rapidly, and governance frameworks must be flexible enough to adapt.

The IoD has noted that AI will not remove complexity from governance. Used well, however, it can give directors sharper tools, clearer insights, and more time for the judgement-intensive work that matters most: safeguarding organisations, shaping strategy, and building long-term resilience (IoD, 2025).

Governance as a framework for trust

Trust is ultimately what responsible AI governance is designed to produce. KPMG's global research shows that trust in AI improves significantly when organisations demonstrate transparency, accountability, and human oversight in deployment. In New Zealand, where public scepticism about AI is among the highest in the world, this challenge is particularly acute and the opportunity particularly clear (IoD, 2025).

Organisations that build strong governance frameworks are better positioned to deploy AI at scale precisely because their stakeholders trust the systems being used. The IoD has framed this well: governance is not the brake on AI. It is the steering wheel (IoD, 2025).

This reframing is important. The goal is not to slow AI adoption but to ensure that the speed and ambition of adoption is matched by the clarity and rigour of the frameworks guiding it.

Leadership responsibility in the AI economy

Technological revolutions are often described in technical terms. Yet the most consequential decisions about how technologies are used are made by people.

Leaders shape how organisations adopt new technologies, how risks are managed, and how benefits are distributed. Artificial intelligence increases the importance of thoughtful leadership because the technology introduces new forms of uncertainty, influence, and ethical responsibility.

Leaders must navigate complex questions about automation, decision-making authority, workforce impact, and the limits of algorithmic judgement. These questions require more than technical knowledge. They require clarity of purpose, willingness to ask difficult questions, and a genuine commitment to accountability.

The human system behind technological change

Across this series a consistent theme has emerged. Artificial intelligence is often presented as a technological revolution. In reality, it is also a leadership revolution, an organisational revolution, and an institutional revolution.

Technology may create new possibilities. But systems determine whether those possibilities become reality. Governments shape the environment through infrastructure and regulation. Industries reorganise through new ecosystems and value chains. Organisations redesign how work happens. Leaders guide people through uncertainty and change.

And governance, at every level, shapes whether that change is trustworthy.

Artificial intelligence will undoubtedly transform economies and industries in the years ahead. But the direction of that transformation will depend less on the technology itself than on the systems, institutions, and leadership choices surrounding it.

The future of the AI economy will ultimately be shaped by people.

References

• Deloitte. (2026). State of AI in the enterprise: The untapped edge. Deloitte Insights.
• Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., & Schafer, B. (2018). AI4People: An ethical framework for a good AI society. Minds and Machines, 28, 689–707.
• Gereffi, G., Fernandez-Stark, K., & Psilos, P. (2022). Global value chains in the digital economy. Duke Global Value Chains Center.
• Institute of Directors New Zealand (IoD). (2025). Data and AI governance: Building trust in a digital future. IoD NZ.
• Institute of Directors New Zealand (IoD). (2025). Trust, risk and the quiet rise of AI in your organisation. IoD NZ.
• Institute of Directors New Zealand (IoD). (2025). Why your AI pilot failed: Governance is the missing key. IoD NZ.
• Institute of Directors New Zealand (IoD). (2025). Smart boards don't just use AI: They govern it. IoD NZ.
• Mazzucato, M. (2013). The entrepreneurial state: Debunking public vs. private sector myths. Anthem Press.
• McKinsey Global Institute. (2023). The economic potential of generative AI: The next productivity frontier.
• Perez, C. (2002). Technological revolutions and financial capital: The dynamics of bubbles and golden ages. Edward Elgar Publishing.
• Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation. Long Range Planning, 52(3), 326–349.