Artificial Intelligence (AI) is now a mainstay in many industries and business sectors. In the business context, the advent of Large Language Models (LLMs), such as OpenAI’s Chat GPT, has opened new frontiers for workflow optimization. These models, with their advanced level of natural language processing, are changing the way companies interact with technology and their own employees.

Challenges and limitations: the importance of critical analysis

The integration of an LLM within an organization is not a process without challenges. One of the first differentiating factors is the knowledge cutoff date, as of today limited to 2021, the quality of training data, and limited access to private or sensitive data are all factors that can affect the effectiveness of the model. This is why continuous evaluation and updating of the system is essential.

To turn it into an effective support tool within companies, the idea would then be to make an intelligent agent that can respond in a “human” way to users’ questions, leveraging both the general knowledge of ChatGPT and the context-specific knowledge that is provided through files of various formats.

In this way, the system is able to access not only the vast knowledge base of Chat GPT, but also the context-specific information provided. This is possible by providing an additional, specific, private knowledge pool to the general model.

Contextual implementation: embeddings and vector stores

The implementation phase is one of the most critical aspects of ensuring successful LLM integration. Using machine learning techniques such as the  embeddings and vector stores, large amounts of textual data can be organized, categorized, and indexed. By doing so, the model can extract information more efficiently when trying to answer specific queries.

Privacy and Security Aspects: Protection of Corporate Data

Enterprise data management is a critical element in any technology implementation. In the case of LLMs such as Chat GPT, it is critical that sensitive information be managed securely. OpenAI, for example, has put in place several security measures, including the non-use of transmitted data for further model training, thus ensuring a high level of data protection.

Practical applications and potential in workflows

Exploring the practical applications of a Large Language Model such as Chat GPT in the business context is critical to understanding its true utility and the extent of its impact. LLMs are not just nice-to-have, but can truly serve as a central pillar in the ecosystem of a modern organization. Next, we focus on a few key areas that represent as many categories of application within the corporate environment. Each of these presents unique opportunities and specific value-added elements that can lead to significant increases in productivity and overall efficiency.

  • Customer Support: Using GPT Chat to provide immediate and accurately contextualized responses can significantly improve the overall customer experience.
  • Access to Corporate Knowledge: The implementation of LLM enables employees to access crucial information quickly and easily, improving knowledge sharing and facilitating decision making.
  • Training Junior Staff: Artificial intelligence can be an invaluable tool for training new employees, providing detailed answers and assistance in understanding complex business procedures.
  • Automation of Repetitive Tasks: In addition to the benefits already mentioned, Chat GPT can be used for automation of repetitive and tedious tasks, freeing up staff for more strategic tasks.

Toward an ecosystem of innovative work

The integration of Chat GPT and other LLMs is more than just a technological innovation. It is a fundamental change in the way companies operate, an evolutionary leap that directly impacts efficiency, productivity, and competitiveness. Preparing the organization for this change requires careful planning and a deep understanding of the potential and limitations of these technologies.

The use of artificial intelligence in addition to processes

The benefits of LLM systems, however, can be extended to the above modes, and the integration of features and patterns such as image or video recognition makes it possible to explore even more innovative mechanics.

Regesta LAB, always attentive to the most current aspects of innovation, will take the opportunity of the upcoming Futura Expo, in Brescia from October 8-10, 2023, to present its research on the use of artificial intelligence in workflows, thanks also to a new software developed in-house, which will be presented during the event.

We invite you to Regesta’s booth at the Brixia Forum to show you Carbon Footprint Calculator, the immersive experience that will allow you to experience firsthand the possibilities of using AI, in this case combined with a video recognition system.

The installation is designed and created by us at Regesta LAB together with Regesta’s development team specifically for this event and is intended to help us become more aware of the impact of our objects and actions on the environment.