Can building your own LLM on your own data work to make businesses successful?
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
-
ナレーター:
-
著者:
概要
In this episode of The AI Moment, I sat down with Jonathan Wagstaffe to tackle one of the most pressing questions for modern business leaders: Is it time to build your own company LLM? We move past the hype of "building from scratch" to discuss the practical realities of the Rent, Buy, vs. Build framework.
We explore why context is the new currency in AI. It is no longer enough to simply use a public model; to gain a competitive edge, businesses need to integrate their own operating procedures and product ecosystem into the AI's workflow. However, this isn't without significant risk. We discuss the "dull, boring" but essential issue of data quality, noting that messy or fragmented data will undermine even the most sophisticated AI ambitions.
The conversation highlights Yahoo Scout as a leading example of the "hybrid model"—taking a powerful base like Claude and layering specific data on top to create a specialist tool. For leaders, the takeaway is clear: be mindful of the exploding costs of token usage and the scarcity of AI expertise. Instead of chasing a "naked LLM," focus on building the proprietary guardrails and intelligence layers that turn a generic tool into a powerful business asset. As the enterprise space evolves rapidly toward the summer of 2026, staying agile with a hybrid approach is your best bet to avoid being "cleaned out" by rapid platform shifts