Wits Professor: AI Governance Choices Extend Beyond Technology Itself

Rennie Naidoo, Professor of Information Systems at the Wits School of Business Sciences. Artificial intelligence (AI) is increasingly framed as a technology whose consequences will depend on the choices we make today. Governments, companies and individuals face difficult questions about how AI is developed, regulated and used, from its effects on employment and inequality, to concerns about misinformation, cyber security and potentially dangerous uses of increasingly capable systems. But some of the most consequential choices may not be about AI at all. They are about the systems surrounding it. AI does not arrive in isolation. It enters schools and hospitals , companies and governments, electricity grids and data centres, labour markets and financial systems. These systems determine not only who gets access to AI, but what can actually be achieved with it. Much of the optimism around AI rests on an appealing idea. Capabilities that were once expensive and scarce can now become widely available. A student can ask an AI tutor to explain a difficult concept. A small business can analyse markets or automate administration without employing a large team. A junior employee can access analytical support that once required considerable experience. These are meaningful advances. But access to increasingly capable software is not the same as distributing intelligence. Intelligence in practice is relational. It emerges through people, knowledge, institutions, infrastructure, capital, experience and the ability to act. Access to increasingly capable software is not the same as distributing intelligence. Perhaps much of the current AI debate ascribes too much agency to the technology. Too often, capability is treated as if it resides mainly within the machine, even though outcomes are produced by the wider system in which that machine operates. Consider two businesses using the same AI model. One has skilled employees, reliable infrastructure, clean data, capital and managers capable of redesigning processes around the technology. Another has the same model but fragmented systems, limited capital and little capacity to turn its outputs into action. They have access to the same AI. They do not have the same possibilities. There is a second assumption worth questioning. We often talk about AI as though greater intelligence is all that stands between existing systems and better outcomes. South Africans should be particularly wary of that assumption. A school may have access to an AI tutor, but that does not solve overcrowded classrooms, device shortages, weak connectivity or the wider conditions in which children are expected to learn. A clinic may have access to advanced diagnostic support, but knowledge does not create medicines, specialists, functioning referral systems or additional beds. A small business may have access to sophisticated AI tools, but those tools do not provide working capital, reliable electricity and water, or access to customers. We have seen this gap between knowledge and action before. Climate change is one of the clearest examples. For decades, scientists have improved our understanding of the causes, risks and likely consequences of a warming planet. We have increasingly sophisticated models, more data and greater analytical capability. Yet knowledge has not translated automatically into action. Energy systems, political interests, industrial dependence, public behaviour and economic trade-offs continue to shape what societies are willing and able to do. The lesson for AI is uncomfortable but important. More intelligence does not guarantee better outcomes. Societies can know more and still struggle to act. These examples point to the same underlying reality. AI can reduce certain information and analytical constraints, but it does not remove the physical, economic and institutional constraints within which people and organisations operate. That is one of the ironies of the AI boom. We are becoming better at produc