Companies are split between adoption and resistance
Over the past year, generative AI has exploded into the cultural zeitgeist. While discussions around AI and its capabilities have been prevalent for some time, OpenAI’s rollout of tools such as ChatGPT and DALL-E has led to a level of mass adoption by the general public that would have been close to unthinkable a few years ago. Now, the race is on to see which companies can leverage generative AI to win—or simply to survive—over the years ahead.
ChatGPT, the most well-known of the current crop of apps, is a generative AI chat app based on OpenAI’s large language model (the GPT in the name). These models are absolute monsters of pattern recognition; when given a simple prompt, they can create original writings that can only be described as eerily articulate. The internet has been flooded with use cases: efficiently summarizing large texts, creating complex essays or even drafting an outline for an article (such as the one you’re reading right now). The flagship example of how advanced ChatGPT has become is when it comfortably passed an MBA exam administered by the renowned Wharton School of Business in Pennsylvania.
That very advancement has led to significant fear, particularly among knowledge workers, many of whom provide value through the gathering, analysis, synthesis and communication of information—all tasks at which generative AI is ruthlessly efficient. Companies now face a choice: adapt to the reality of generative AI, or be left behind. In the words of Charles Darwin, “It is not the strongest of the species that survive, nor the most intelligent, but the ones most responsive to change.”
Many organizations will be tempted to shrug off generative AI; others will avoid it out of fear or become overwhelmed by it. The most important thing to focus on is that these tools are designed to augment and enhance human intellect, not to replace it. In this context, they are not so different from other tools we already use every day; by way of an example, Microsoft Excel is a complement to our human capacity to analyze and action data—but it is not a complete substitute for that work.