LLM Adoption in the Enterprise

Introduction

Tailored copy written by a chatbot, automated code reviews, and a fully AI-driven yet empathetic and helpful customer support. The potential of large language models (LLMs) to increase productivity and drive return on investment (ROI) seems too good to be true. But this disruptive technology also presents a challenge that goes beyond cost and technical know-how. To bring an LLM-based application to production, product leaders need a pragmatic understanding of the technology, the business outcomes it can drive, and the skills required to bring their product idea to life. They must be able to lead diverse teams through the adoption process and respond to new challenges as they arise. By learning to overcome their initial awe of a new and groundbreaking technology, product leads can begin to embrace the potential of LLMs to address their users’ pain points. In this way, LLMs become a powerful addition to the product team’s toolbox that, when used properly, can solve a variety of text-based problems.

The need for such a mindset shift is underscored by a recent Gartner® report,¹ which advises decision makers to learn “how generative AI can drive strategic value.” Otherwise, teams risk building ultimately useless generative AI products whose only purpose is to “demonstrate that it is possible to build something with generative AI, leading to only incremental improvements and ignoring the transformative potential of this technology. This is a mistake.” This report is here ...