Generative AI (GenAI), machine learning (ML), and large language models (LLMs) are all becoming increasingly important to modern enterprises, but achieving measurable value from AI is still a challenge. Part of the issue is that a well-trained AI model relies on a large amount of data, and for many companies, organizing and making use of all their data qatar whatsapp number data slows them down every day. To maximize the value from AI, companies need to ensure their data stack is well organized. If a company is able to consolidate data sources, it’s much easier to create valuable use cases for generative AI. Here are a few examples already adding value today.
AI in Software Development and Data Science
As far as LLMs go, GPT-4 is an impressive generalist, with broad ranging knowledge of topics spanning from world history to computer programming to middle eastern cuisine and beyond. That’s not surprising, as it was largely trained on webpages scraped from the internet. But what most companies need are specialized models focused on their vertical market, that are trained on their internal data, not the internet. The a16z post on What Builders Talk About When They Talk About AI explained how enterprises don’t really need more chatbots. Companies need GPTs that can efficiently provide insight with high accuracy and precision. It doesn’t matter if the AI can summarize Shakespeare – it matters whether it can accurately predict what a potential customer’s lifetime value might be.