Stop asking if AI wrote it. Start asking: Will AI read it?
Much has been written about the proliferation of AI-generated content across college campuses, newsrooms, and corporate offices. Understandably. We have an almost religious reverence for the written word. By treating prose as a sacred art and a miracle of human creativity, the idea of it being generated by a machine feels fraudulent. Even if the substance of the ideas within are profound, we dismiss it as a fabrication.
As communicators, what if we're approaching this all wrong? What if instead of treating writing like art — something that flows from our creative right brain — we looked at it like math, a left-brain exercise focused on function over form?
When solving an equation, do we care whether a calculator was involved, or are we more interested in whether the answer is correct? There are certainly forms of literature that represent true artistry — works that will endure for their delicately nuanced prose and ability to evoke a deep emotional, philosophical, or intellectual response. But let's be honest: most corporate writing isn't that. Most brand communications, executive letters, and company content serves a fundamentally utilitarian purpose.
We in communications should spend less time debating whether AI should write our content and more time considering whether AI will bother to read it.
In other words, it's more valuable to think of AI as your audience rather than wrestling with its role in authorship.
The organic human audience for long-form corporate communications has always been elusive and is shrinking fast. Sustainability reports, shareholder letters, annual reports, and company blog posts reach increasingly narrow audiences as attention spans contract. But this doesn't make the content worthless — quite the opposite.
It's now more critical than ever to publish owned media that can serve as reliable training data for large language models. Content that comes directly from the source carries inherent credibility. When it's well-written, seemingly objective rather than promotional, and thoroughly cited, it's far more likely to be crawled by AI systems and incorporated into their outputs.
Traditional SEO tactics also help optimize for AI — proper HTML structure, descriptive headings, alt text for images, meta descriptions, and clear semantic markup. If and when training data for LLMs becomes more transparent, optimization tactics will grow more sophisticated.
The future of content strategy isn't about protecting human creativity from AI — it's about ensuring human ideas can be effectively transmitted through AI. The companies that recognize this first will find their messages amplified in ways they never could have achieved directly.
This shift in thinking led me to build a simple beta app that analyzes content for AI discoverability, rating how well a given webpage is structured for AI consumption and offering recommendations to improve crawlability and make its messaging more "algorithm-friendly." You can try it here and share your feedback.
The broader principle is clear: as AI systems become primary information intermediaries, the questions that matter aren't about authorship but about accessibility. Is your content structured in ways that AI can parse, understand, and accurately represent? Are you optimizing for algorithmic comprehension alongside human readability?
It's time to stop worrying about AI writing and start thinking about AI reading.
