Bridging facts and stories: AI enterprise content creation

How is AI changing the way brands tell stories to their audiences? Tomas Hruby explored that theme in his presentation at Digital Transformation Week Europe, and adopted his speech into this article.

Tomas Hruby

Updated on Feb 21, 2024

Published on Oct 15, 2023

Great brands are in the storytelling business. They craft stories that tell their audiences how their products and services fit into, and enrich, their lives. Advances in technology have created huge changes in the way brands can tell their stories.

The advent of the internet was a huge leap forward. This helped brands connect with their audiences on a global scale. The invention of the Content Management System (CMS) was another leap forward, giving brands the tools they need to collaboratively create, tag, and archive large volumes of content. With these capabilities, the possibility of personalized storytelling started to seem possible, but it still wasn’t quite within reach.

The rise of AI generated content is the latest pivotal change in the state of storytelling, allowing brands to create large amounts of content in very short amounts of time. But that content doesn’t always resonate with audiences. Once again, personalization seems like the missing piece of the puzzle that would make generated content more valuable. Are we on the verge of another storytelling revolution? And what might it look like?

The content creation conundrum: knowledge, consistency, and speed

Before we look at personalization, let’s start with storytelling. For brands, storytelling means transforming raw facts into compelling narratives. But for large enterprises, these facts can be hard to find–they’re spread across all kinds of departments, tools, geographical locations, teams, and so on. This creates a side effect where consistency and maintaining a unified brand voice can become difficult. If it’s hard to align on the right facts, it’s even harder to stay consistent.

Enterprises try to solve these issues by implementing complex content workflows and approval processes. But this ends up creating a bottleneck, and slows down the content creation process. The teams responsible for storytelling have a hard time keeping up the pace, and that leads to:

  • Difficulty covering all products with on-brand storytelling
  • Content that’s limited in scope to “just the facts"
  • Difficulty communicating effectively across different markets
  • No time to implement good personalization

This situation should be familiar to many enterprise brands trying to create content.

The rising tide of content: the LLM effect

The recent advent of readily available Large Language Models (LLMs) such as GPT-4 has significantly increased the total output of content. It’s easier than ever to create large quantities of content, but this increase in quantity has made consumers even more sensitive to content quality. Mediocre content is now quickly drowned out in a sea of generated content.

The consistency problem hasn’t gone away, either. Brands still need to rely on approval processes and workflows to ensure the content they’re producing is on brand and speaking to the right audiences.

The challenge of creating memorable storytelling content is as difficult as ever. Creative content isn’t enough anymore; content now needs to be not only captivating, but also laser-focused and personal.

AI in content production: efficiency and limitations

Some vendors, like, are working to solve this challenge by natively integrating AI capabilities into a CMS. Some clients are already seeing benefits. Here are a few quick examples: a client in the travel industry is using AI in to hyper-personalize location-specific content, which has significantly increased the output of a single content writer. Two other clients have started covering all of their new content multi-lingually with the help of AI. The content still requires review, but the time to a first draft is much shorter.

AI capabilities in

These cases show how AI can help brands improve the speed of their content creation, but it doesn’t solve the consistency issue. LLMs like GPT-4 can be “fed” your brand guidelines as part of their prompt, but that often isn’t enough for them to fully grasp your brand essence. The AI still doesn’t have all the facts, since the context size they can handle is still somewhat limited.

The missing puzzle piece: structured, headless content consolidates facts

“Having all the facts in one place” is one of the key motivations for enterprises to adopt a headless CMS. That’s because a headless CMS acts as a storage place for pure content. In doing so, it helps break down content siloes.

When executed well, a headless CMS helps enterprises achieve more coherent information architecture and a more holistic content structure. This creates an environment where content pieces can be found and reused more easily.

At, we often see how switching to headless CMS reduces the costs of content management by consolidating content siloes. Instead of relying on a multitude of different CMSs, all content can be kept in Reusable content models, a hub for shared resources, and a single place for teams to work and communicate enables significant productivity gains.

Connecting the dots: a new age of storytelling

Now we can start to see a solution to the speed vs. consistency issue. With a headless CMS, an AI can have the ability to find and use all your brand’s content. Imagine a marketer tasked with creating copy for an existing product. They can ask the AI for the benefits of that product, and the AI can find information from the total corpus of product information, some of which may be quite difficult for the marketer to find themselves.

Combining headless CMS with AI is like pairing a librarian with a creative writer. The AI can act as a librarian, finding all the relevant information from a brand’s content repository. Then, acting as a creative writer, it can generate new text based on the information it found. This teamwork is a type of AI design pattern known as Retrieval-Augmented Generation (RAG), and it’s a potential solution to the speed vs. consistency challenge.

Retrieval-Augmented Generation improves the output of AI-generated content

It’s not just theoretical, either. These approaches are already prototyped in, and will soon be available to our clients. It’s just a matter of time until these new capabilities revolutionize brands’ storytelling capabilities. In  the not-so-far future, we see:

  • Effortless compliance with brand guidelines and tone of voice
  • Hyper-personalization at scale
  • Google-like search within your CMS
  • Easier content re-use and interlinking

Our vision is one where enterprises achieve the agility of the startup, with the control of an enterprise. Speed and consistency would no longer be at odds, but they would go hand in hand. If this vision for the future excites you, we’d love to talk about what’s possible with–get in touch and find out how to bring your storytelling to the next level.

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