Storytelling: The Architecture of Meaning in an Age of Machines

by Sobat Khawaja

A decade or so ago, people told a joke that went, “What does a liberal arts graduate say to an engineering graduate? ‘Do you want fries with that?’” The implication, of course, was that liberal arts degrees were useless in a business context, dooming their earners to low-paying careers in fast food.

No one’s joking about them now, though. Companies everywhere are actively seeking liberal arts professionals to fill lucrative storytelling roles—including OpenAI at a whopping $400,000 a year.

This turnabout is especially surprising when you consider that these same companies, thanks to AI, can instantly generate as much content as they desire. At the end of 2024, a team of SEO experts reported that more than half of the Internet’s written content was now AI-generated. Generative AI models have also been adopted by 90% of Fortune 500 companies, many of them seeking to automate content production tasks in marketing, administration, and customer service.

So why all this demand for human storytellers?

Story as a Human Constant

“Stories have to be told or they die, and when they die, we can't remember who we are or why we're here.” ~ Sue Monk Kidd, author

Long before TV and the Internet, our human ancestors told stories while gathered around the evening fire. These stories—which extended to songs, plays, poems and books—allowed them to translate the complexity of their lived experience into a simple, memorable format for others to follow. In the words of author and journalist Aleks Krotoski,

“Stories are memory aids, instruction manuals and moral compasses.”

Stories are inextricably woven into the fabric of human cognition, teaching us how to think, connect, and behave for our survival and thriving. Even during sleep, the mind tells itself stories (i.e., dreams) as a way to process information.

The Limitations of AI

Then, in 2023, public LLMs arrived. With just a few prompts, anyone could generate pages of written content that looked perfectly polished and sounded authoritative. Companies wasted little time in putting these tools to use, cranking out endless texts, emails, blog posts, video scripts, and white papers.

But stories, the structures people depend upon make sense of all this new information, eluded them.

The way LLMs are built explains why.

“Artificial neural networks and machine learning rely on vast amounts of data to identify patterns within them, but they can’t predict when these patterns will change, a crucial component of creative thinking.” ~ USC Annenberg Relevance Report

LLMs are designed as language replicators. They are fed enormous amounts of content, which the algorithm scans, identifying patterns at the syntax level. Once these patterns are identified, the LLM recreates them for users, using a token system to predict which words would most likely appear in response to a particular prompt or query.2

Simulated Coherence vs Meaning

This approach to language generation is grammatically coherent but not inherently meaningful. As a language replicator, AI lacks the cognitive features humans draw upon to create meaning—such as context, values, reflection, experience, and intent. What appears as emphasis within a text carries no inherent significance within the model. For AI, emphasis is simply a statistical feature of language rather than a tool of intention.

The public—and even AI engineers themselves—may describe AI’s function using words like “understands,” “considers,” “believes” or “thinks,” but, in reality, it does none of these. It can’t, by design. It’s programmed to process language as objects in a pattern rather than a system of symbols humans use to construct meaning. The end result is an output that “looks or sounds right, but…feels off.” 3

“Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that.” ~ Bender and Gebru, et. al.

The Value of Storytelling

Fundamentally, content generation and storytelling describe two completely separate processes. One places words in logical order; the other tells us the lesson we should derive from them. Storytellers control the attention economy, directing people to notice what matters and why. This requires the “soft skills” AI doesn’t and can’t possess by design: contextual awareness, critical thinking, and empathy.4

AI’s creators—and most people, if we’re honest—assumed that all we needed to tell more stories was a machine that could replicate human language. It turns out that stories are far more than a collection of polished sentences. They are blueprints of human experience that allow people to navigate an increasingly complex world with intention and purpose. It’s sad, but also encouraging, that business leaders are finally awakening to this reality and granting liberal arts professionals their rightful place in the org chart.

“Reflecting on all this, storytelling is both art and strategy. It’s not just about telling people what a company does; it’s about helping them feel it, understand it, and remember it. Metrics illustrate performance, but stories show consequence.” ~ Alison Coleman, Forbes

Multifaceted Thinking, by Design

I founded Provectus Consulting in 2016 because I saw firsthand how a dearth of creative thinking impacts companies at every level. Without it, change initiatives lack enthusiasm and purpose, emails and presentations fall flat, internal cultures feel joyless and hostile.

Writers and designers didn’t need to be serving fries at McDonald’s; they had a critical role in creating powerful assets that would allow companies to not just succeed but, also, thrive.

So we set about assembling a team—not of traditional consultants, but of creative thinkers that many corporations had traditionally overlooked. They came from fashion, advertising, programming, education, and crisis management. They are curious and, occasionally, cantankerous, interrogating ideas and uncovering hidden obstacles to a project’s success in hopes of reaching a better solution.

Some executives, particularly those who value alignment, express discomfort with this type of dynamic. However, I’ve found it leads to far better outcomes. When we deliver an asset to a client, we can assure it’s been reviewed, tested, and revised from multiple unique perspectives. In a world where it seems everything is being created with less intention, we’ve built a practice that centers it—to the enthusiastic applause of our clients.

Turns out, we were building for the future.

Sources

1 USC Annenberg Relevance Report. “Storytelling in the age of artificial intelligence.” USC Annenberg, Center for Public Relations. Published and updated Feb 18, 2020. https://annenberg.usc.edu/research/center-public-relations/usc-annenberg-relevance-report/storytelling-age-artificial

2 Emily M. Bender and Timnit Gebru, et. al. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Accessed from ACM Digital Library, ISBN 978-1-4503-8309-7/21/03. Presented March 2021 at the FAccT’21 Virtual Event in Canada. https://doi.org/10.1145/3442188.3445922

3 Paul Jones. “Why Some Are More Suspicious of Artificial Intelligence Than Others.” Greater Good Magazine. Published Nov 13, 2025. https://greatergood.berkeley.edu/article/item/why_some_are_more_suspicious_of_artificial_intelligence_than_others

4 Alison Coleman. “Why Human Storytelling Still Wins In An AI World And How To Harness It.” Forbes. Published and updated Feb 26, 2026. https://www.forbes.com/sites/alisoncoleman/2026/02/26/why-human-storytelling-still-wins-in-an-ai-world-and-how-to-harness-it/