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Large language models: the missing link in corporate intelligence

How LLMs could transform internal information management

In today’s increasingly data driven world, organisations face ongoing challenges in making sense of the vast amount of data that they collect every day. The sheer volume and variety of data can be overwhelming, leaving organisations struggling to extract actionable insights.

This article explores how Large Language Models (LLMs) could be used as internal data discovery tools, and outlines some of the opportunities and risks inherent to this innovative technology.

What are LLMs?

LLMs, which are at the heart of tools such as OpenAI’s ChatGPT and Google’s Bard, are artificial intelligence (AI) models that have been designed to understand, process and generate humanlike natural language. These models can perform a wide range of language-related tasks, from content generation to text summarisation, translation, question answering, and more. The ease of interaction with LLMs via chatbots has made this technology more accessible to a much wider audience, and we are seeing rapidly growing interest in LLMs across all industries and sectors.

The power of LLMs in business applications

LLMs are showing great promise in supporting employees with their day-to-day work, and in improving wider internal business processes. Some examples of potential LLM applications include:

  • Retrieving information – training LLMs on internal company data can create systems that understand both the context and content of specific businesses, allowing employees to easily find relevant information using natural language queries. Connecting these models to finance systems and other operational datasets could, for example, allow users to quickly generate reports and summaries, further reducing workloads on support functions.
  • Improving decision making – by combining disparate data sources, LLMs can be trained to derive insights and support decision making. These insights can be further queried using natural language prompts to understand underlying factors without the time delays traditionally associated with conducting additional analysis.
  • Automating processes – LLMs are well suited to manual, repetitive tasks such as summarising documents, generating meeting agendas and drafting responses to emails. Automating these mundane tasks will save considerable time, enabling employees to focus on more complex, difficult and valuable tasks.
  • Improving data consistency – a significant challenge for businesses is ensuring complete and consistent data quality across systems. LLMs could be used to suggest entries for data fields to support users in completing forms quickly and in a consistent manner.
  • Learning and development – there has been an increasing trend in the personalisation of training for users over the past few years. LLMs could take this one step further by creating tailored learning for employees based on their needs and specific learning styles, enabling businesses to upskill employees more effectively in a rapidly changing world.

These are just some of the potential applications offered by LLMs. The rate of innovation in this field is extraordinary, and it is critical that organisations begin investigating the numerous ways in which LLMs will impact how they operate.

Risks and challenges

Companies that succeed in using LLMs as productivity tools will need to successfully incorporate these tools alongside their employees, and mitigate the various challenges and risks associated with LLMs.

Some of the key challenges and risks to be aware of include:

  • Data confidentiality – there have been numerous stories in the news around employees using publicly available tools that leak confidential data. This is a significant risk that should be mitigated by carefully selecting tools that adhere to appropriate confidentiality requirements, and ensuring employees are aware of the types of information that can be used.
  • Confabulation and bias – there are concerns with the accuracy of responses that are served to users, with current LLMs often providing incorrect or biased outputs. This is particularly problematic in high-risk use cases and in instances where users are reliant solely on model responses. This is an active area of research, with solutions being explored both in the deeper foundation models and in front-end interfaces.
  • Data governance – ensuring the right people have access to the right information will be critical when implementing LLMs. Whilst there is huge benefit in combining data into one model, this data may become accessible to anyone that interacts with the model. Ensuring proper data governance will be necessary when implementing LLMs.

Whilst these are some of the key challenges in a commercial context, there are various other challenges in training and using LLMs, including the availability and cost of computational resources, security and privacy concerns, and an evolving regulatory and compliance landscape which has yet to catch up with the current pace of innovation.

Despite these challenges, LLMs represent an exciting development in our use of computers. They will have far reaching consequences for the way in which we work, shop and play in the very near future, and organisations that implement valuable business applications whilst navigating the challenges and risks will thrive.

If you are interested in learning more about LLMs and their potential applications within your organisation, contact Mo Aboobakar.