AI doesn’t know the truth, it just sounds like it does – IT News Africa
A growing portion of the digital content we consume on a daily basis is AI-generated. From articles and code, to customer interactions and even images and video, it’s getting harder to identify what is created by humans and what isn’t. Much of the content is useful and high quality, but some of it is highly inaccurate, because AI does not fundamentally understand truth.
AI was built to learn from human-generated knowledge at scale, but AI is no longer only learning from the collective output of human thinking. Now that it is actively populating the data ecosystem, AI is becoming part of a feedback loop where systems are increasingly training on AI-produced content. The result is a growing risk of data contamination where factual accuracy is replaced by outputs that may be convincing but not necessarily true.
Hallucinations, where AI generates confident but incorrect information because it predicts the most probable next word rather than verifying factual truth, have become a well-known challenge, as have deepfakes and synthetic media, where generated visuals or audio blur the line between authentic and artificial evidence. This is because AI does not “know” facts in the human sense. It predicts patterns.
Accuracy is not optional
In most business processes, this situation is manageable because humans remain in the loop to validate outcomes. However, in highly sensitive domains, the consequences of even a single error become profound.
For example, death claims processing cannot afford any errors, whether they are caused by AI or humans. These processes involve identity verification, cause-of-death documentation, beneficiary validation, policy interpretation, and regulatory compliance. There is no margin for “plausible but incorrect” in this environment.
A hallucination in a chatbot response is one thing, but a hallucination in a death claim process is something else entirely. Not only can it introduce incorrect information, but the emotional impact of that inaccuracy will have significant knock-on effects. Unlike many other use cases, errors here are not easily dismissed or corrected. They are experienced personally, often at a time of heightened vulnerability, creating confusion and triggering unnecessary distress for families already dealing with loss.
This is why organisations modernising claims environments must treat factual integrity as a design principle, not a validation step.
AI is not enough
At MIP, AI is not treated as an autonomous decision-maker. It is constrained, guided, and integrated within controlled systems where data sources are trusted and verifiable, outputs are validated against structured rules, human oversight remains embedded in critical steps, and accuracy is prioritised. In other words, the system is designed to prevent AI from “making things up” in the first place.
AI can play a meaningful role in these types of processes, but the focus must shift from automation first to accuracy first. AI models should operate within curated, authoritative datasets, not open-ended internet-trained knowledge pools. AI should assist interpretation, not make final determinations where emotional or legal consequences exist. Most importantly, factual validation must remain a human process.
AI’s power lies in its ability to interpret patterns at scale, but the next phase of enterprise AI maturity will not be defined by speed or volume. It will be defined by truth control. As AI increasingly contributes to the data ecosystem it once only consumed, organisations must recognise that all data is not equal, and all outputs are not factual. As AI-generated content continues to grow, the challenge will not just be building smarter models, but maintaining a clear line between what is true and what is merely believable. In emotionally charged processes like death claims processing, that line cannot blur. When people are at their most vulnerable, accuracy is not a feature, it is a responsibility.
By Richard Firth, CEO, MIP Holdings
