Artificial Intelligence, popularized by ChatGPT, is playing a growing role in many fields, and the market is expected to reach $1,300 billion by 2032. AI uses models such as GPT-4 to generate written, artistic, and musical content, as well as computer code.
In 2023, 72% of Internet users used generative AI to search for a product. It has also become a valuable resource for content creation and is revolutionizing how companies interact with their customers, manage operations, and create value. But how can we control the content produced and ensure it is accurate? Or measure its environmental impact? What major challenges can we expect in the coming months?
Generative AI: an essential partner in the modern world of work
Virtual assistants and AI have revolutionized our ways of working and become essential partners in boosting efficiency and productivity. They simplify tasks and produce rich, varied content (images, videos, texts, translations). Companies are adopting conversational agents, or chatbots, on a massive scale to provide quick and accurate answers, reduce wait times, and increase customer satisfaction.
However, it’s important to remember that these algorithms can only create from what already exists. Employee expertise is essential to enrich information databases and create emotions. AI should be able to adapt to changing consumer preferences in real-time. Companies must, therefore, implement systems that learn and evolve with customer needs, which requires greater agility and responsiveness.
The real potential of these new technologies lies in human-machine collaboration. This synergy will be key in future years. However, it will only open the door to new professional opportunities with continuous employee training and constant adaptation.
New 2024 challenges to harness the power of this technological revolution
The rise of AI raises several financial, environmental, regulatory, and security issues. New artificial intelligence systems require a responsible framework to regulate the sector and prevent misuse.
The security of AI systems is a major concern. Chatbots can be vulnerable to adversarial attacks, and model errors can lead to the dissemination of false information. In 2023, 4 out of 10 consumers cited data privacy as the main reason preventing them from using AI for online shopping advice, followed by concerns about the reliability and quality of the answers. The only way to overcome this challenge and guarantee accurate information is with cybersecurity research and robust models.
There are also financial challenges. Large Language Models generate significant costs, requiring the creation of new business models. Investment in research and development and the acquisition of advanced technology is imperative. Companies must consider the cost of data storage and management, customized products and services, maintenance, updates, ethics, and compliance to develop effective, sustainable strategies in this new, constantly evolving economic landscape.
Greenhouse gas emissions (server powering), creation and management of electronic waste, water consumption, hardware replacement… Companies looking to use these systems face many challenges. AI models, in particular deep neural networks, require massive computing capacity. The servers and data centers powering these systems consume a great deal of energy, significantly contributing to a company’s carbon footprint. In an article that should be published later this year, Professor Ren’s team estimates that ChatGPT ‘drinks’ 500 milliliters of water, the equivalent of a small 50cl bottle of water, for every 5 to 50 questions asked.
In 2024, the challenge will be a more in-depth and sustainable integration of these technologies into businesses’ day-to-day operations. This will boost efficiency while limiting misuse. Globally, only 12% of companies are capitalizing on AI to improve their performance, and they are growing up to 50% faster than competitors. Adapting to these new technologies and overcoming the subsequent issues will enable companies to offer tailored experiences, as well as optimize their operations, and guarantee data protection. Awareness, innovation, regulation, and data measurement and analysis will be key to overcoming these challenges.
