What is a story? Novelists consider a story a tapestry of characters, emotions and events woven together to reflect the human experience. When communications professionals put on their storyteller hats, they think about crafting messages that resonate with audiences, convey clear narratives and evoke a desired response.
But what if we thought of a story as thousands of data points? After all, narratologists see stories as the result of underlying structures and themes providing universal resonance. When we see a story as data points, each carefully selected word or punctuation mark becomes its unique piece of data. In the age of artificial intelligence (AI), generative AI and large-language models (LLMs), this perspective allows the storyteller and writer to master their craft at an entirely new level and untangle the daunting web of technologies.
If you think tools like ChatGPT are strange magic, consider the source of that magic. LLMs that power generative AI tools are fed on data sets of the written word. Programmers and computer scientists may have built these tools and dreamt up the possibilities they provide, but writers power them.
Neuroscience has played a prominent role in the development of deep learning (the AI method that is the foundation of generative AI), with some types of models referred to as “neural networks” after the neural connections in the human brain. When an LLM generates content, it functionally predicts the next word in a sentence based on the patterns it learned from the vast training dataset. This process is akin to how our brains predict what comes next in a conversation or while reading a book, using context clues and previous knowledge to make educated guesses.
As writers, it is our job not to lead the reader to the same conclusion our writing gets to, whether that’s a believable (while shocking) plot twist, the proof of a thesis statement or a call to action.
The power of generative AI lies at the nexus of its simplicity and complexity. On the one hand, it’s a straightforward process of input and output (cause and effect). On the other, it’s a sophisticated potion of algorithms and mathematics, where each ingredient in the cauldron is independent and deeply interconnected just like the intricacies of the writer’s mind: taking our experiences, our knowledge and our intuition and stirring them together into narratives that can move, educate and entertain.
With generative AI, we have a new enchantment in our “writer’s spell book” that will benefit the craft as much as the pencil and eraser once did. These tools aren’t just an extension of one writer’s brain, but the ability to engage in a dialogue with a system that, in some ways, mirrors the complexity of our cognitive processes and is trained on the words strung together by millions worldwide.
That’s why generative AI won’t replace writers and storytellers. We’re one of the best-placed professions to leverage these tools. Generative AI and algorithms have always needed writers and storytellers to write the data that feed them and ensure stories aren’t only predictable patterns (even for those of us who love a good trope)—always having that spark of human unpredictability.
Moreover, a writer’s ability to empathize and connect with the human condition gives them a unique advantage. We can guide AI to avoid insensitivity or tone-deafness, ensuring that the output remains grammatically correct, emotionally intelligent and culturally aware, which is the core craft of the communications professional. This human touch will differentiate future stories: stories that are not only generated, but also curated by human experience and insight.
As we peer into the future of communications and storytelling, remember it’s the writers casting the enchantments of generative AI. Embrace the belief that you already have everything to learn about and master generative AI. In doing so, these tools will find their best purpose: to serve not just as an algorithm but as a catalyst for our timeless and beloved art of storytelling.
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