The Originality Gap: Why Human-in-the-Loop Is Your Most Important Content Strategy Decision

Something quietly significant is happening to content strategy, and most marketing and digital leaders haven’t fully reckoned with it yet. As AI becomes the layer through which audiences discover and consume information, the brands that stay visible won’t necessarily be the ones producing the most content. They will be the ones producing the most original content.

There is a gap opening up. I call it the originality gap. It exists between brands scaling production with AI and those using AI while keeping human expertise at the center. This gap will matter enormously in the coming years.

The New Gatekeeper

For two decades, content was shaped by one question: what does Google reward? While SEO still matters, a new gatekeeper has arrived: what do large language models (LLMs) reward? As audiences use AI for research and discovery, LLMs act as a layer between brands and their customers. Unlike search engines that focus on links, LLMs evaluate content based on its unique value to their training data.

I’ve seen a term popping up in analyst research and strategy conversations lately that helps explain this: Content Perplexity. In the world of AI, perplexity is a measure of how "surprising" or unpredictable a piece of text is to a model. If your content is generic, it has low perplexity. It is exactly what the AI expects to see. But content that offers rare insights, bold opinions, or proprietary data has high perplexity. For an LLM, high perplexity is a signal of authoritative, human-created value. It is the reason a model chooses to cite your brand rather than simply summarizing the status quo.

The Race to the Bottom

AI makes production cheaper, tempting brands to flood the zone with volume. However, content generated at scale without human shaping tends toward low perplexity. It says what everyone else is saying, which makes it invisible because there is nothing for an LLM to distinguish. The brands that stand out will be those that invested in originality while others invested only in volume.

What Human-in-the-Loop Actually Means

"Human-in-the-loop" is not just about risk governance. It means humans are the source of the "surprise" that makes your content worth finding. Proprietary research and practitioner experience do not come from a prompt. They come from people who have developed unique perspectives.

Take, for example, a piece I wrote for Acquia about the New England Patriots’ recent (and decidedly unsuccessful) trip to the Super Bowl. An AI could summarize the need for a new approach to digital experiences, but would it be excited enough about its team heading to the Super Bowl to write a blog post? Maybe so and maybe not. But I certainly was! It was fun to think about how to connect real-word work challenges with the Patriots’ improbable Super Bowl run. That human connection, the "why" behind the words, is exactly what closes the originality gap.

Effective programs use AI for research and formatting while protecting the human contribution. This means preserving a practitioner’s distinctive voice rather than sanding it down to brand-safe blandness.

Closing the Gap

Closing the originality gap requires a shift from measuring output to valuing inputs. This starts with an "originality audit" that moves beyond standard traffic metrics to identify which parts of your content library actually offer insights that an AI could not replicate. From there, the strategy shifts to investing in the raw materials of authority: proprietary data, expert interviews, and firsthand practitioner perspectives. 

You must treat your subject matter experts as originators rather than just final reviewers, ensuring their unique voices remain a competitive asset. Ultimately, the goal is to build for attribution. In an AI-mediated world, having your brand cited by name because of a specific, unique insight is the new gold standard for reach.

The Opportunity in the Gap

The originality gap is a challenge, but it is also an opportunity. Most brands will respond to the AI moment by producing more. The ones that respond by producing more distinctively will find themselves in a stronger position with both human audiences and the AI systems they rely on. Human-in-the-loop is not a constraint. It is the strategy that makes AI adoption work.