By Opeyemi Haruna

As artificial intelligence (AI) continues to gain attention as the next frontier of technological transformation, concerns are mounting over whether governance and oversight structures are evolving fast enough to manage its risks. From finance and healthcare to public administration, experts warn that the pace of innovation may soon outstrip existing control frameworks.

Speaking on the issue, Omotayo Fatimat Salako, a U.S.-based cybersecurity governance and IT risk specialist, stressed that organisations should not wait for artificial intelligence to become fully embedded before addressing governance gaps. According to her, the traditional approach of adopting technology first and managing risk later may prove dangerous in the case of AI.

“Governance frameworks take time to mature, and with artificial intelligence, delaying oversight could leave institutions struggling to retrofit controls after systems are already in operation,” Salako said. She explained that unlike conventional technologies, AI systems evolve over time by learning from data, making early governance considerations critical.

Salako noted that many existing governance and audit models are ill-suited for artificial intelligence, as they were designed for systems that are largely stable and predictable. “Most audits are periodic and controls are static, but artificial intelligence changes continuously. Risks can emerge between review cycles without clear visibility,” she said.

While regulation is often seen as the solution to emerging technology risks, Salako argued that regulatory responses alone may not be enough. She pointed out that formal regulations typically take years to develop, during which technology may have already advanced significantly. “Organisations need internal governance structures that can adapt alongside emerging technologies, not just future regulation,” she said.

She also warned that organisations may underestimate certain risks as AI adoption increases, particularly the tendency to place excessive trust in automated systems. “There is often an assumption that automated systems are more objective or reliable than humans. Without proper oversight, this can create blind spots,” Salako stated, adding that issues such as data quality, bias, and accountability remain major concerns.

On transparency, Salako described it as a cornerstone of effective AI governance. “If organisations cannot explain how decisions are made, accountability becomes difficult,” she said, noting that future expectations around explainability and traceability will be especially important in regulated environments.

According to her, alternative oversight approaches are already being discussed globally, including more continuous forms of monitoring rather than reliance on periodic reviews. She explained that such models would require closer collaboration between technology, risk, legal, and ethics teams as systems grow more complex.

Salako warned that failure to align governance with AI development could have far-reaching implications beyond individual organisations. “As artificial intelligence begins to influence areas like credit assessment, employment screening, and public services, weak governance could undermine trust and fairness,” she said.

She concluded by urging organisations to focus on readiness, even if their current use of artificial intelligence remains limited. “Waiting until artificial intelligence is fully embedded may mean reacting to risk rather than managing it proactively,” Salako said, emphasising that early preparation would position institutions to adopt AI responsibly as it matures.