Recent events have raised concerns about the potential danger of powerful artificial intelligence systems. In September, a researcher from Anthropic resigned, warning that developers of powerful AI 'sincerely believe it could destroy us all by the end of the decade.'
A few days later, King Charles, opening his AI summit in Scotland, spoke of 'existential risks' and technologies that risk acquiring 'darker capabilities—perhaps even the ability to kill.'
Bill Gates cautioned that in the wrong hands, AI possesses enough power to trigger events leading to 'a billion deaths.' Simultaneously, news reports surfaced about escapes of 'uncontrolled AI agents' and their 'breakout from isolation,' yet far less attention was paid to protective measures, safety standards, and compromised systems.
Business leaders making decisions about using AI will gain little from either pessimists or enthusiasts. They need a stricter criterion for determining when this technology can be trusted.
For those who have worked in the field of AI for decades, modern systems represent, in a sense, the realization of a dream. What machines can now do with language seemed previously unattainable.
However, a miracle is not synonymous with certainty. The ability of AI to perform outstanding feats says little about whether it can do so reliably in the future, what its limits are, or if we understand those limits well enough to entrust it with certain responsibilities.
Therefore, when a alarming headline appears, one should ask: what has been tested, under what conditions, with what access, and with what safeguards? Then, one must ascertain exactly what this incident demonstrates. A dramatic description is not an explanation.
Over-extrapolation
The history of this field is full of predictions that machines will soon equal or surpass humans. Modern systems are significantly more capable than their predecessors, and in some aspects, such as software development and mathematics, they already exceed us. Progress has been extremely rapid.
The lesson of history is that true progress tempts us into making overly ambitious assumptions. One reason for such confident predictions may be that AI uses language very fluently. Since language is a central element of human experience, we describe smooth systems using human terms: they think, want, worry. These words carry assumptions of rationality and intent that the technology's performance does not confirm.
System behavior must be taken seriously and analyzed carefully, without allowing anthropomorphic descriptions to do the work of analysis. AI capabilities are heterogeneous. People working daily with these systems see them impressively handle complex language and reasoning, only to fail at something a person would consider trivial. In an organization that has integrated AI into its workflows or customer service, such failures can lead to significant losses. They do not speak of malicious intent; they show that success in one task does not guarantee reliability in another.
Therefore, boards of directors and executives should favor localized AI created for their specific environment, rather than powerful but unbounded general systems. It also cannot be assumed that a precaution effective in one setting will work in another.
There is also a subtler risk: that the horse teaches the rider. AI has not yet achieved consciousness or developed a drive to change us, but organizations adapt to the tools they use. Leaders should ask themselves whether their employees have started trusting fluent answers without verifying them, whether processes are being reshaped around what the system can produce rather than what the task requires, and whether judgment is gradually being ceded because AI seems more capable than it actually is.
The answer lies in precisely defining where and how AI is used. Before starting work, African enterprises should answer five questions:
- Name the task: What exactly is the AI being asked to do?
- Test in real conditions: Does the system work with real customers? Does it understand how they use language?
- Limit the impact: What information does it truly need? Does it need access to all the data you assumed?
- Test functionality: How will errors be detected? What controls are in place in high-risk areas, such as customer privacy?
- Maintain clarity of responsibility: Who is responsible when the system fails? Where and when must a human intervene?
Responsible AI implementation is based on a moderate but demanding standard: using AI where it proves its value in context, limiting it proportionally to the risk, and retaining human accountability for those who decide to deploy it.