COLIN HARMAN
THE 5 USE CASES FOR ENTERPRISE LLMS FROM LLM CAPABILITIES TO USE CASE CATEGORIES
COLIN HARMAN is an enterprise AI-focused engineer, leader, and writer. He has been implementing LLM-based 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.
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elcome to the age of Enterprise LLM pilot projects! Right now, enterprises are cautiously but enthusiastically embarking on their first large language model projects, with the goal of demonstrating value and lighting the way for mass LLM adoption, use case proliferation across the organisation, and business impact. In a well-planned and well-executed project, that’s exactly what will happen: the use case will provide new capabilities and efficiencies to the business, function reliably, delight users, and make a measurable impact on the business. Understanding of the technology and its value will spread outside the pilot project, where new use cases will be surfaced and championed. However, if the wrong use case is chosen the project could fall flat, even when executed to perfection. Imagine a use case is chosen that’s a poor fit for the capabilities LLMs provide: You implement an e-commerce analytics assistant, but the LLM-based conversational interface is a clumsier experience than the previous column-based lookup interface, so you remove it. Then you realise all the
analytics requirements are best satisfied by traditional data analytics patterns like statistical analysis, topic modelling, and anomaly detection, so instead you shoehorn a conversational interface into some side feature that nobody will use. You’ve reinvented the wheel, and the LLM provides no benefit to users. Or imagine that a use case is chosen that fails to provide an advantage over existing workflows, even if it is a fit for LLM capabilities: You implement a chat-with-data system on a set of marketing publications. Users try it out, but prefer to use Google because they can’t ask your system about topics not contained in the dataset, while Google has everything and still gets answers and sources. Feedback is negative and your fancy AI system is perceived as less useful than Google search. Or, imagine that the data for your selected use case is not organised or reliable enough to support it: You implement a question-answering system over a massive dump of unstructured data. enterprise search initiatives over this dataset had previously failed spectacularly, due to nonexistent data management, chaotic content, and erratic formatting. Now, the
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