If you have worked in digital marketing for more than a year, you have probably heard the advice hundreds of times: "create quality content." It is almost a reflex at this point. And yet, every time you ask what "quality" actually means, you get different answers.
In the age of AI Search, the question gets even more complicated. ChatGPT, Gemini, Perplexity, and AI Overviews cite sources based on criteria that do not perfectly align with what we, as marketers, call "good content." A recent study published on Search Engine Journal brings concrete data on this topic. The results are worth paying attention to.
As someone who does SEO every day and constantly tests what works and what does not, I have seen both sides: excellent content stuck on page 4 and mediocre pages sitting in the top 3. It does not always make sense, but the data helps us understand why.
When you say "quality," what do you actually mean?
Ask 10 marketers to define quality content and you will get 15 answers. Some talk about expertise: the author must be a subject matter specialist. Others focus on execution: design, structure, readability. And others emphasize depth: long articles, well-researched, with references and data.
None of these definitions is wrong. But none is complete either. Search engines, whether traditional or AI-powered, do not apply a single definition of quality. They use signals. And signals are not synonymous with quality as perceived by a human reader.
This is the first important lesson: quality content is not a magic button. You can invest weeks in a flawless article with data, graphics, and original interviews, only to discover that Google or ChatGPT prefers a simpler page that is better positioned contextually.
In practice, we see this frequently with clients who invest in AI-focused content: the articles with the most hours of work are not necessarily the ones with the best results. What matters is how well they answer a specific question and how unique their perspective is.
What the research shows: originality matters, but not everywhere
The study analyzed top-ranking URLs from Google, Gemini, ChatGPT, and Perplexity for B2B SaaS queries. Each page received an originality score on a 0-15 scale, evaluated across five dimensions: primary contribution, structural novelty, interpretive depth, source dependence, and contextual insight.
The result? Original content appeared more frequently in AI responses, but the correlation was weak. Strong performance in one area did not reliably predict performance in another. In other words, there is no simple "original equals visible" formula.
The critical difference emerged based on query type:
- Interpretive queries ("benefits of marketing automation," "email marketing best practices"): original content performed clearly better. When the answer requires judgment, AI prefers sources with their own perspective.
- Factual queries ("what is marketing automation"): originality barely mattered at all. When the answer is a verifiable fact, accuracy and clear structure are sufficient.
This distinction is fundamental for any content strategy. Not all your content needs to be "original" in the same way. A technical guide on setting up Google Analytics 4 needs to be precise and clear. An article on how consumer behavior is evolving in the Romanian market needs to bring a perspective nobody else has.
The 200-pound-per-click lesson: timing beats perfection
A case from the research illustrates this perfectly. An API startup created a simple 1,500-word page targeting the term "API design," a keyword with nearly zero search volume at the time of publication. The page was not spectacular. It was functional.
12 months later, search volume exploded exactly as predicted. The modest page continued to outrank major competitors who later arrived with comprehensive content hubs. Within two years, that keyword generated an estimated value of 200 pounds per click.
It was not the quality of the page that made the difference. It was the timing. They identified an empty space, arrived first, and capitalized on first-mover authority.
This is a lesson we apply at difrnt. with our clients: sometimes, "good enough" content published at the right time beats perfect content published six months late. We have seen this play out in the context of search evolving from answers to actions as well: those who adapted their content first for new formats gained disproportionately.
What this means practically for your strategy
I am not saying you should publish weak content. But the obsession with perfection is a real opportunity cost. Here is what we do differently based on this data:
We separate queries by type. For factual questions, we invest in accuracy and clear structure: FAQ schema, direct answers, verifiable data. For interpretive questions, we invest in original perspective and practical experience. The content strategy cannot be the same for both types.
We prioritize timing. We monitor emerging trends in Google Trends, Search Console, and industry conversations. When we identify a rising term with no serious competition, we publish fast. We refine later. It is better to be first with a good article than tenth with a perfect one.
We invest in authority signals. LLMs cannot evaluate content quality directly. They rely on indirect signals: who links to you, how frequently you are cited, whether you are the original source of an idea. We detailed how these signals work in our FSA framework for AI visibility. E-E-A-T is not just a Google concept; it is how AI selects the sources it cites.
We publish perspective, not information. LLMs can generate information. They cannot generate experience from real projects, opinions formed through failures and wins, or context specific to the Romanian market. That is our competitive advantage as an agency, and it should be yours too.
In the Romanian market, where content competition in Romanian is still smaller than in English-speaking markets, the timing advantage is even more pronounced. Brands that start publishing now on emerging topics (AI agents in retail, voice search in Romanian, AI-powered PPC automation) will have a structural advantage that competitors will find hard to recover.
The takeaway from all this data? Good content matters, but "good" does not mean what you think. It is not about stylistic perfection or impressive word counts. It is about saying something that has not been said, at the right time, with the authority that comes from real experience.
