One of the most common applications is AI-generated content creation. By analysing large data sets, AI-based text generation language models and other AI-driven tools can create content in the form of text, images, and videos like product descriptions and advertisements. Additionally, AI can be used to personalise the customer experience, providing tailored product recommendations. Finally, AI-powered technology can help optimise content quality by picking up on patterns a human might not see, thereby predicting which content might appeal most to customers.

AI and machine learning models can support sales representatives in customer interactions by generating personalised responses and product offers. This process could be coupled with other AI algorithms to analyse customer sentiments gleaned from the nuances of language and subtle signals of customer interest or distrust. Furthermore, pre-trained AI models can support sales managers by allowing them to ask a chatbot specific questions about reports or forecasts, offering them better insights to make more informed decisions. Additionally, AI can analyse market data, competitor pricing and customer behaviour to optimise product pricing and find the right balance between price and demand.

Generative AI can support many operational functions and tasks. In manufacturing, for example, shift plans and maintenance instructions can be automatically created and dynamically adjusted based on direct feedback from production workers. In procurement, for instance, the automated generation of tender documents and AI-based answers to suppliers' questions can support supplier interactions. And in supply chain management, for example, news related to supplier risks can be collected, summarised and reported automatically. Consequently, generative AI becomes a fundamental driver of operational effectiveness.

Learn more

Generative AI systems have several business cases in administration. One of the most significant areas for potential implementation is automating repetitive and time-consuming tasks. For example, to create new documents or templates based on existing data and formats to improve efficiency and consistency across the organisation. Additionally, foundation models could support process optimisation by generating process flows and assigning responsibilities for different tasks, ensuring that everyone in the team knows what they need to do. Furthermore, large language models could reduce knowledge management efforts when trained on a specific body of company internal knowledge, making it easier for employees to access knowledge inside the company.

Generative AI work has various applications in human resources, particularly in recruitment, employee management, and training data. It can be used to analyse data and generate job descriptions based on previous job postings, employee data, and other relevant information. AI technology can also create personalised onboarding and training programmes, enhancing the learning experience and improving employee retention. Additionally, chatbots for employee support, such as answering benefits and policy questions, can reduce the workload of human resources staff and enhance the employee experience.

Finance departments can use generative AI models for multiple purposes. For example, to create various financial reports or integrate income and cash flow information. The technology can also provide financial models and simulations to help assess the impact of different economic scenarios. The ability of generative AI to automate repetitive tasks benefits accounts teams, for example. Instead of manually sifting through incoming invoices and manually transferring data into an ERP system, AI can automatically extract all relevant data, match it with order and delivery numbers and feed payment details into the ERP system. This significantly streamlines the data entry, approval and payment process. The employee continues to check the transactions, but manual transfers are no longer necessary. This way, AI significantly reduces effort and keeps errors to a minimum. 

Moreover, generative systems can produce investment recommendations based on market conditions and other factors, providing valuable insights for structuring an investment portfolio. By using generative AI tools, finance departments can save time and resources they would otherwise spend on manual data entry and analysis.

Learn more

Artificial intelligence has diverse applications in various aspects of risk management. For example, it can summarise and highlight changes in external bodies’ regulatory documents and update company documents to comply with legal requirements. AI systems can also generate realistic scenarios that can help to predict and mitigate potential risks. They can enable organisations better prepare for and prevent attacks by creating realistic simulations of potential cyberattacks.

Apart from marketing and sales, Research and Development (R&D) might be the business function that could benefit most from generative AI. It provides valuable content for cutting-edge fields such as computer vision, deep learning, reinforcement learning, sensor fusion and autonomous systems. The potential use cases are numerous and include generating synthetic training data for machine learning models, automating software testing, code writing and documentation, or even co-designing parts for complex products and systems. In pharmaceutical R&D, it can address challenges in acquiring, verifying, and applying knowledge to search for new drugs with specific 3D structures or chemical properties. Generative AI makes R&D processes leaner, more efficient and more effective. It can also support a circular economy through more sustainable practices focusing on minimising the carbon footprint of products, systems and services across the supply chain.