This does not mean that all AI is a waste of time and money. It does point out, though, that hype-fueled adoption without a clear business case other than “speed and greed” is going to yield bad results. That should not be surprising.
It’s foolish to think that a hammer is equivalent to having a full set of tools. It’s simply one tool in the toolbox. Similarly, AI is an umbrella term that represents a range of technologies – a toolbox full of singapore whatsapp number data different tools. Generative AI is just one approach or type of AI. As the name suggests, it is useful for generating novel (but not always accurate) content. If your use case is not a fit for what this technology can do, why use it? Why spend time and money trying to use a hammer like a saw? If what you need is to segment data to understand customer behavior, to have an automated system monitor for anomalies or to predict sales trends for your products – generative AI is not a good choice.
Even if content generation fits your use case, the risks of generative AI might be unacceptable. Hallucinations – the propensity of generative AI to provide inaccurate, offensive, or dangerously wrong information – are a known risk and should not come as a surprise. It’s not guaranteed that techniques like retrieval augmented generation (RAG) or fine tuning will solve all of these problems. There are also privacy and cybersecurity risks to consider. Conducting a full cost benefit and risk assessment is essential.