We at CarePrecise are as fascinated as anyone about the miraculous capabilities -- and astounding failures -- of the new Large Language Model Artificial Intelligence tools now battling it out in cyberspace. But we've been around too long not to reserve some skepticism about the hype cycle. The other day I was chatting with an LLM about a new medical device. It initially pointed me to the manufacturer's site and some related promo material, but when I told it I'd rather read content from actual users of the equipment it suggested some sites I generally prefer not to use. When I asked instead for Facebook Groups, it gave me a list of suggestions with very specific Group names.
None of which turned out to exist.
So, when pressed for different information than it had been providing, my chatty AI tool employed a very human tactic: MSU.
This suggests to us that perhaps the best way to effectively use AI will be to point it to data you know is good -- specifically, your own data about your customers and prospects.
This approach is already taking root in pharmaceutical marketing. Directing AI tools toward rich, highly accurate reference data will, we think, become a key component in making the new technology produce credible, and actionable, results.
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