Why Companies Should Look to LLMs, Not Chatbots, For Their AI Needs

LLM and AI are buzzwords, but over the last several months we’ve seen examples of LLM-powered chatbots really go off the rails. So, executives are becoming cautious about the future of AI. I argue that chatbots are not a good first avenue for a company that wants to embark on the AI journey. Instead, I suggest focusing on data cleanup tasks. As a supply chain analytics practitioner, I have first-hand knowledge of the daily challenges businesses face, and I describe three specific use cases in which LLMs can make a significant impact to the company's business processes, while being very low-risk.

Even if you went rowing across the Atlantic last year, you could not escape talks about the new technological sensation—Chat GPT. It seemed to conquer the world, promising to make thousands of jobs obsolete and force leaders to reassess business processes. Numerous companies were actively implementing it, mostly as an LLM-enabled chatbot. The idea was clear: they wanted to cut costs by making some human assistants redundant and others more efficient.

Is it too early to adopt LLMs in business, specifically supply chain? I believe that the time is right, but we should not be looking at chatbots first.

However, about 1.5 years after the initial release, there are many examples of this technology going completely rogue, giving dangerous advice and “hallucinating” (providing outright false information). Some wild examples include playing with a car dealership center’s chatbot to get it to act against the interests of the dealership. In other words, LLM is a risk, and now executives are acting with caution, adopting a “wait and see” strategy.

Is it too early to adopt LLMs in business, specifically supply chain? I believe that the time is right, but we should not be looking at chatbots first. What follows are three specific examples of immediate data cleanup use cases where any supply chain professional can start leveraging the advanced capabilities of LLMs with very low risk.

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