COLIN HARMAN
WITH LLMS,
ENTERPRISE DATA IS DIFFERENT By COLIN HARMAN
COLIN HARMAN is an Enterprise AIfocused engineer, leader, and writer. He has been implementing LLMbased software solutions for large enterprises for over two years and serves as the Head of Technology at Nesh. Over this time, he’s come to understand the unique challenges and risks posed by the interaction of Generative AI and the enterprise environment and has come up with recipes to overcome them consistently.
W
elcome to the age of Enterprise LLM Pilot Projects! A year after the launch of ChatGPT, enterprises are cautiously but enthusiastically progressing through their initial Large Language Model (LLM) projects, with the goal of demonstrating value and lighting the way for mass LLM adoption, use case proliferation and business impact. Developing these solutions either within or for mature businesses is fundamentally different from startups developing solutions for consumers. Yet the vast majority of advice on LLM projects comes from a startup-to-consumer perspective, intentionally or not. The enterprise
environment poses unique challenges and risks, and following startup-toconsumer guidance without regard for the differences could delay or even halt your project. But first, what does “enterprise” mean?
Provider
Startup
Enterprise
Consumer
B2C
B2C
Enterprise
B2B
B2B or Internal
User
A simple provider-user matrix highlighting ( ) where enterprise challenges and risks are involved. Note that a product provided to an enterprise user could end up with a consumer end-user, e.g. a chatbot provided to a financial services company for their clients.
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