Over the past few years, Large Language Models (LLMs) and other generative AI technology have become increasingly prevalent in business and enterprise. A LLM is a form of of artificial intelligence that uses deep learning algorithms to process, understand and generate natural language. They make up a part of generative AI, which refers to AI capable of generating content such as text, images, and other realistic and original media. LLMs have revolutionised the process of natural language processing, meaning they have had and will continue to have a significant impact on entrepreneurs and their businesses. In fact, researchers at the IMD (International Institute for Management Development) referred to LLMs as a “technological tsunami” about to “reshape the global economic landscape”. This buzz around generative AI isn’t without cause, as researchers at McKinsey estimate generative AI could add the equivalent of $2.6 to $4.4 trillion of value to the world economy annually.

Primarily, the rise of LLMs has caused entrepreneurs to operate more efficiently due to the increased productivity resulting from automating tasks. LLMs are often used to perform routine tasks, such as constructing reports, assisting with consumer service and engaging with content creation for marketing and advertising. This results in increased productivity, as generative AI is significantly faster and more accurate than humans when it comes to these functions. For example, McKinsey research suggests the use of generative AI can increase productivity in marketing by 10% and in customer support (via intelligent bots) by nearly 40%. In fact, companies such as Ultimate AI assist entrepreneurs set up AI customer support bots, and claim to help entrepreneurs thrive through resolving 60% of customer requests using intelligent chat bots. This use of generative AI, along with other productivity boosting AI initiatives mean entrepreneurs and their teams can operate more efficiently as they can streamline their time and effort towards more complex tasks that are exclusive to humans.

Furthermore, the rise of generative AI means entrepreneurs and companies will have to quickly determine their stance and tactics towards the adoption of AI in the workplace, and then identify which uses of LLMs and generative AI work best regarding their business models. For example, generative AI can be used significantly in software development, as artificially generated code is becoming more accurate and widespread in the technology development industry. AI can also automate the code testing process, enabling entrepreneurs to take advantage of more robust, error free programs in the technical makeup of their companies. Additionally, generative AI can help entrepreneurs by accelerating several types of IT operations. For example, it can assist automating tasks like passwords resets, status requests or basic diagnostics, several tasks that were previously delegated to CIOs and CTOs. This further increases companies’ productivity, and provides entrepreneurs with reliable and fast sources of the basic technology-based tasks needed in business.

This rise of generative AI will also mean that entrepreneurs need to adapt to the changing job requirements in their companies. Many entrepreneurs may need to create a large generative AI platform team, whose primarily focus is developing and maintaining generative models that can be utilised by product and application teams. This will require entrepreneurs to hire cohorts of data scientists, machine learning engineers and other types of data engineers to help their company utilise generative AI to the fullest extent. These teams will also require the company to have fine-tuned its data architecture, meaning entrepreneurs need to ensure company data is categorised, organised and error-free so LLMs can operate using reliable and accurate data sources. 

Once this is achieved, generative AI can perform a wide variety of data analysis tasks needed to improve the analytical performance of the company. For example, entrepreneurs can use AI to perform natural language processing, which is the analysis of speech and text through an AI algorithm. This processing method is used by a range of companies to perform tasks like social media monitoring, text analysis and search autocomplete, which are often used by companies like Meta, Google and Amazon to provide users with improved search results which are tailored to their previous searches and profiles. In fact, natural language processing has the ability to scan and perform sentiment analysis on social media posts, meaning “private and government entities can leverage on a structured use case roadmap to generate insights” into consumers and citizens. These same techniques are only going to become more prevalent in business and entrepreneurship in the future as generative AI and consumer data becomes more and more accessible.

On the whole, generative AI technology and LLMs will have a huge impact on individual companies and entrepreneurs, and therefore a widespread impact on global economies. This type of AI tech will undoubtedly increase the productivity of entrepreneurs, as automated tasks and increased data analysis means companies can operate efficiently and make accurate decisions faster than ever before. This technology will evidently have a large impact on the job market, with it predicted to create 97 million new jobs by 2025. However, these new jobs come alongside hundreds of millions of jobs that are put in jeopardy by the rise of AI, and therefore the total impact on employment opportunities could be negative. The entire effect of this AI on entrepreneurship will take years to fully reveal itself. However, it’s certain that business and entrepreneurship is undergoing a technological revolution.