Enterprise applications of LLMs and Generative AI

Enterprise applications of large language models (LLMs), like GPT-3.5, offer significant advantages to enterprise organizations. Their Natural Language Processing (NLP) capabilities empower businesses to efficiently process and comprehend vast amounts of text data, extracting valuable insights from customer feedback, market trends, and internal documents. But where to start, how to govern the process, and how to scale in a sustainable and business-outcome-centred way?

With our clients we are piloting a series of LLM initiatives in HR, Sustainability, Finance, Customer Service and other core business functions. Each is sandboxed, accelerated, and outcome-oriented, with suitable architecture safeguarding in place. To learn more read below, or to discuss a pilot with your organisation and use case, get in touch.

Look no further than the latest investment rounds in LLM and Generative AI businesses to know that there is both significant hype, and significant substance around these new technologies. As well as revolutionising business for our benefit, we all secretly worry whether any of us will have jobs in the future. But to do nothing is not an option. So how do enterprises engage with this technology?

The first step is invariably a proof of value. Running proof of value or POC/POV initiatives with Large Language Models (LLMs) in enterprise organizations presents several challenges. Firstly, data privacy and security concerns arise, as sensitive information may inadvertently be included in the training data, potentially leading to data breaches. Secondly, LLMs’ black-box nature makes it challenging to understand how they arrive at specific conclusions, raising transparency and accountability issues. Additionally, the cost of implementing LLMs and the requirement for significant computational resources may be prohibitive for some enterprises. Lastly, fine-tuning LLMs to specific business domains can be time-consuming and resource-intensive, hindering the rapid adoption of these technologies in proof-of-concept projects.

We are deploying our proven innovation framework, with close change management and strict governance adherence to deliver specific, defined, and secure test initiatives. We are exploring how we leverage LLMs in customer support contexts, for example helping organizations to enhance their engagement with clients, providing personalized responses and resolving queries more effectively. Additionally, we are also looking into how models can be integrated into chatbots and virtual assistants, streamlining customer interactions and improving overall user experiences.

Our use cases go beyond, into financial reporting, marketing analytics, sustainability reporting and tests with content creation, automating report generation, and various HR applications.

By harnessing the power of LLMs, enterprise organizations are taking the first explorative steps to drive data-driven decisions, boost customer satisfaction, and optimize their operational processes.