










Judging AI content quality is really just a matter of exercising the same great taste that manifests itself everywhere else in life:
You know great taste when you see it, hear it, feel it, or taste it. In enterprise marketing, taste is about knowing if content works when you review it.
Generative AI has made it much easier to produce content at scale, but more isn’t necessarily better. Teams can now generate dozens of drafts, variations, and summaries in the time it once took to polish a single headline. The hard part now is deciding what’s worth publishing.
That’s where taste comes in. And it’s something AI cannot replicate. Enterprise marketers not only need to cultivate it, but also know when to bring it into their AI-assisted workflows.
As AI adoption becomes pervasive, taste has become one of the tech sector’s favorite buzzwords. In a now-viral post on X, OpenAI president Greg Brockman said, “Taste is a new core skill.”
Paul Graham, the co-founder of legendary accelerator Y Combinator, shared similar sentiments, noting that “when anyone can make anything, the big differentiator is what you choose to make.”
Having great taste is not only relevant to making code for new software applications. It’s vital for B2B marketing teams trying to generate demand and brand affinity. According to a 2026 research study published by the Content Marketing Institute, 64% of enterprise marketers cite “content relevance and quality” as the top factor in driving greater effectiveness.
In enterprise content operations, AI requires taste — or editorial judgement — that understands your target audience’s needs and aspirations, and how they correlate with your brand’s value proposition, mission, and values. Exercising taste in this context is about articulating ideas so that they are both on-brand and on point for the people who discover them on your web site.
Think of this as a deliberate, structured filter that sits between AI-generated output and publication. This “taste layer” is where human expertise evaluates nuance, emotional resonance, cultural context, and brand fit. These are all dimensions that AI can mimic but not originate.
Taste is the difference between content that feels generic (otherwise known as slop) and content that feels like it came from your organization’s experts. It’s the reason one brand’s AI-assisted blog reads like a thought leadership piece while another’s reads like a summary of search results.
AI excels at pattern recognition, synthesis, and scale. But quality covers a different set of variables:
Involving your top talent, whether they work on the marketing team or in another function, is a way to keep marketing content human, more distinctive, persuasive, and high-converting.
Talking about “taste” could be construed as an extra step that will prolong publishing cycles, which nobody wants.
When you balance your CMS’s AI’s capabilities and human judgement, however, the opposite happens. More quality content gets out the door, and you minimize the work that will come later when it’s time to refresh and repurpose your top assets. Achieve this by:
Use this table to guide which subject-matter experts to lean on depending on what kind of content you’re developing:
Technical accuracy, feasibility, detection of overclaiming, identification of truly novel features
Scheduled technical review(s) during draft stage, checklist of capabilities to verify, short Q&A session for complex features
Confirm feature claims, validate timelines, suggest accurate wording for limitations and caveats
Real-world objections, buyer language, resonance with prospects, common misunderstandings
Bring recent call transcripts or FAQs to reviewers, run a sprint review with reps, incorporate flagged phrasing into edits
Flag claims that sound like marketing, suggest language that answers common buyer concerns, ensure use cases match customer realities
Validation of clarity, usefulness, tone, and perceived value; detects gaps between intent and reception
Beta reader panels, advisory councils, targeted user interviews before publication, quick surveys on draft excerpts
Assess whether message lands, suggest real-world examples, point out confusing or overcomplicated sections
External experts & partners
Credibility checks, industry context, unbiased reality check, media-ready framing
Share drafts with analysts, partner agencies, or journalists under NDA or embargo; request short annotated feedback
Verify market positioning, identify weak claims, suggest data or sources to strengthen credibility
An article on Search Engine Journal suggested that the rise of AI answer engines could be the death knell for evergreen content, or assets that touch broadly on a topic that can be used to drive inbound traffic for an extended period of time. Instead, the author argues that first-person knowledge will become more important, with hands-on lessons from the frontlines defining great marketing content.
Even if evergreen content doesn’t completely go away, the more you can infuse it with the editorial judgement or taste from a living expert, the higher your AI content quality will be.
Taste is a powerful element in AI content strategy because it’s always evolving. As humans we constantly learn, are exposed to new ideas and refine what we decide is worthy of an audience’s attention. AI tools get trained on data, but human judgement brings in a lot of offline and private experiences that can’t be found through a search engine.
Brands can cultivate taste as a skill in part by empowering people inside and well beyond marketing teams to exercise their judgement in assessing AI-assisted content as an everyday process.
You can still invest in better models, platforms, and prompts. Just make sure you treat the expertise around you as another source of data and insight to enhance what AI helps you create.
For more on how people perceive content online and the human element they’re looking for, check out WordPress VIP’s 2026 Future of the Web report.
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。