The increasing use of artificial intelligence in business strategies has sparked a debate about the level of autonomy machines should have. According to Alan Turnley-Jones, CEO of NTT DATA Middle East and Africa, the real question is where human judgment matters most. He advocates for "human-orchestrated AI," where machines operate at speed and scale, while people set objectives, boundaries, and accountability. This approach is crucial for emerging markets in the Middle East and Africa, where AI can address constraints around skills, service delivery, and financial inclusion.
Turnley-Jones argues that deploying AI without considering governance creates risks. A risk-based approach is recommended, where low-risk decisions support greater automation, and high-risk decisions require stronger human controls. For instance, AI systems influencing credit, employment, or healthcare decisions need more human oversight than those categorizing internal documents. Requiring human approval for every AI output can create bottlenecks and undermine automation. Instead, humans should design the loop, establishing thresholds for AI autonomy and human intervention.
In emerging markets, human orchestration plays another critical role: providing context. AI systems are shaped by data and assumptions, which may not accurately represent local languages, cultures, and socioeconomic conditions. The quality of underlying data is essential, and human oversight cannot compensate for poor information. Organizations must understand the data used by their AI systems, its representativeness, and potential biases. This is particularly important in emerging markets, where data quality and availability vary considerably.
Emerging markets should not adopt oversight models developed elsewhere without modification. Local experts need to recognize when AI systems miss linguistic or cultural nuances or rely on assumptions that do not translate well across markets. By combining global capability with local intelligence, emerging markets can apply global responsible AI principles in ways that reflect their economic, cultural, and regulatory realities.
One principle that should apply everywhere is that accountability cannot be delegated to an algorithm. As AI becomes more autonomous, organizations need clear answers to basic questions about authorization, boundaries, monitoring, and responsibility. Good governance can give organizations greater confidence about innovation, and emerging markets do not have to choose between rapid AI adoption and responsible AI adoption.
The goal is to become more deliberate about where machines should have autonomy, where human judgment remains essential, and how accountability connects the two. By achieving this balance, emerging markets can harness the potential of AI while minimizing risks. This approach can also help build trust in AI systems and ensure that their benefits are equitably distributed.
Ultimately, the adoption of AI in emerging markets requires a nuanced understanding of the technology and its implications. By prioritizing responsible AI adoption and combining global best practices with local intelligence, emerging markets can unlock the full potential of AI and drive sustainable growth and development. This approach can also help address some of the unique challenges facing emerging markets, such as limited access to skills and expertise.
Key points
- Emerging markets need a smarter model of AI oversight that balances machine autonomy with human judgment.
- Human oversight plays a critical role in providing context and ensuring that AI systems are shaped by high-quality, representative data.
- Accountability for AI outcomes cannot be delegated to an algorithm and must be clearly assigned to individuals or organizations.
