In business, effective communication is the cornerstone of success, as widely acknowledged in various studies. According to the 2023 State of Business Communication report, business leaders cite increased productivity (72%), increased customer satisfaction (63%), and increased employee satisfaction as the top three benefits of effective communication. Increased trust (60%).
However, the study also found that communication efficiency decreased (by 12%), resulting in a 15% decrease in productivity. The impact is significant: poor communication is estimated to cost U.S. businesses $1.2 trillion annually, or an average of $12,506 per employee per year. This is a question that is constantly heard from his IMD executives in Switzerland, and one that they would like to address.
While challenges remain in human-to-human communication, the introduction of powerful machines, such as new forms of artificial intelligence (AI), adds new layers to conversations. How do we communicate with these machines, and are there ways to do so more effectively?
There is no doubt that AI has the potential to revolutionize business communications. The new form not only increases productivity and frees up time for the creative elements of communication, but also generates content and improves accessibility for individuals with disabilities through speech-to-text technology.
However, the responsible use of AI in communications requires ethical considerations, transparency, and a balance between technical efficiency and human-centered approaches. Yet collaboration between humans and AI is already well underway. Early research suggests that productivity increases significantly through written communication for those who embrace the latest wave of breakthrough generative AI tools, such as OpenAI's GPT-4.
The first step is to understand this much-hyped technology. Generative AI (GenAI), a subfield of machine learning, is a wonder that may take us a decade to fully understand. It works by generating new content and data based on human prompts. Prompts can come from humans through text or voice prompts, or from other machines through code.
Large-scale language models (LLMs) enhance the resulting output, whether in the form of text, images, code, video, or music. Because the creation process involves asking and interrogating the AI, this output often feels like your own. However, the quality of the output is highly dependent on the quality of the questions (also known as prompts) we provide.
