Don’t get me wrong- I would prefer we mature AI with tabular data use cases, but LLMs have us focused on NLP and computer vision. Let’s ride this wave to operationalize AI with tabular data use cases.
AI for tabular data is where the value will explode, specially in Pharmaceutical and Biotechnology R&D, but we need to get the foundations right first. There is not a coherent data management strategy across the organization and lacks appropriate metadata or standards, making it very complicated to assemble large data sets. Most everyone jumped on the LLM train as it did not seem too difficult to use existing unstructured data and it helped them to claim they have joined the hype without much delay, although the value of some of those projects might be questionable
AI for tabular data is where the value will explode, specially in Pharmaceutical and Biotechnology R&D, but we need to get the foundations right first. There is not a coherent data management strategy across the organization and lacks appropriate metadata or standards, making it very complicated to assemble large data sets. Most everyone jumped on the LLM train as it did not seem too difficult to use existing unstructured data and it helped them to claim they have joined the hype without much delay, although the value of some of those projects might be questionable