The quest to measure AI vs. human content performance isn’t about finding a winner. It’s about learning how to make the most of the technologies and tools at your disposal.
A 2025 report found half of marketers and other business professionals are using generative AI to create content such as blog posts, slides, emails, and other materials. At its best, AI can help spark ideas and overcome the fear of a blank page, turning basic prompts into full paragraphs of text.
On the flip side, there’s a reason Merriam-Webster chose “slop” as its 2025 word of the year. It defined the term as low-quality digital content “that is usually produced in quantity by artificial intelligence.” This raises questions about whether the productivity gains from AI are worth it.
The only way to be sure is to put hard numbers behind its performance and compare it with work developed entirely by human effort. That means going beyond anecdotal or short-term metrics, like a quick traffic spike.
If you’re trying to develop an enterprise AI content strategy, this post will help you learn how to use integrated content intelligence and analytics to benchmark, evaluate, and optimize AI’s trust impact.
Why measuring AI vs. human content performance is complex
Calculating AI vs. human content performance is tricky in part because the technology spans multiple use cases. Beyond generating content, we’re using it to personalize content developed by humans. Others are turning to AI for SEO, GEO, and AEO recommendations.
Even if you focus on how AI is used in content creation, it can be difficult to isolate AI’s contribution from other variables, like how it was distributed or amplified. This is also still a relatively new area, so within many firms, there is probably a lack of consistent tagging and attribution frameworks to draw upon.
Content performance benchmarking can also be hindered by an overreliance on short-term KPIs. An AI-generated blog post might be deemed a success because it drew significant traffic within the first two weeks of being published, for example. Over time, you could discover that the post did little to drive conversions to product demo pages or encourage visitors to return.
AI content analytics is arguably even tougher within large enterprises, given that content is often being produced by multiple teams for a mixture of global and localized campaigns. Data sources can be fragmented, limiting the ability to see the bigger picture.
Building an AI vs. human content performance measurement framework
There have already been numerous attempts to provide data-backed assessments of AI vs. human content performance. Neil Patel’s company studied close to 800 articles across dozens of websites and concluded human-written content drew 5,444 times more traffic than that produced by AI.
There have been similar studies, but some of them are now two or even three years old. AI and its adoption have changed a lot since then. It’s going to be hard to use third-party research to tell you how much you should lean on AI vs. your own team. You need to develop your own framework. Here’s how:
1. Benchmark before you automate
The FOMO around generative AI has led to some remarkably fast adoption, sometimes without doing some upfront content performance benchmarking of existing assets.
Capturing baseline performance metrics before integrating AI may sound obvious, but it’s easy to overlook in the rush to gain a competitive advantage. In the long term, though, it will be much easier to gauge the marketing ROI for AI if you have this kind of data at hand.
2. Define clear AI-assisted content goals
There can be real differences in human vs. AI content outcomes. When you assign a staff person to write a blog post or social copy, you might want them to offer a unique point of view or tone of voice.
AI might play a larger role in ensuring brand consistency across your content assets or personalizing content aimed at specific customer segments. You have to ask something more specific than “Did this content do well?”
3. Tag AI-generated vs. human-created content
If you’re using a modern CMS, you already have this kind of capability built in. It’s time to use tools like Parse.ly’s Smart Tags to get a more apples-to-apples comparison when using something like Parse.ly analytics.
Realistically, create a tag for “hybrid” or “AI-assisted” content as well. Most marketers are quickly learning there still needs to be a degree of human oversight involved in content creation, no matter how sophisticated the tool.
4. Centralize metrics in a content intelligence platform
Content creation and performance should never be siloed. That’s why WordPress VIP and Parse.ly analytics go hand-in-hand, offering a dashboard where you can filter content by tag, preferred metric, and use comparison mode to assess performance on multiple variables.
Analytics patterns that reveal human vs. AI content outcomes
A data-backed approach to analyzing AI vs. human content performance requires a holistic view across multiple parameters. You’ll want to delve into the following to get the quantifiable information you need to drive a successful enterprise marketing and content strategy:
- Traffic sources: This should be familiar ground, because you’ve probably been doing it with your content all along. Break down the organic traffic you’re driving to both kinds of content based on SEO and also AEO, assuming you’re now deliberately looking to be scraped by large language models (LLMs). Organic reach should be a balance of what you’re seeing in terms of referral traffic — not just from LLMs but from traditional digital channels like social media. You should also carve out what is coming from direct traffic if you’re sharing AI and human-authored content through an email newsletter, for instance.
- Engagement metrics: Most organizations have used metrics like bounce rate and time on site to understand performance. We recommend alternatives such as engaged time and recirculation rate, which offer a more meaningful look at whether human, AI-authored or AI-assisted posts create sustained engagement and encourage sharing the content with peers.
- Conversion pathways: Outside of the marketing department, sales teams will want to know what impact, if any, AI is having on the propensity for prospects to convert. This could mean simply clicking through to download resources like an eBook, form fills, playing with an ROI calculator, direct purchases, or reaching out to contact a rep to see a product demo or formal proposal.
- Long-term retention metrics: Study the cohorts you set up to gauge whether they’re not only attracting an audience but also leading them to stick around. A span of between three to six months or longer may be necessary to see patterns or draw viable conclusions.
Turning insights into an actionable strategy
Instead of thinking in terms of AI vs. human content performance, use your content performance benchmarking to determine where and how AI should assist your team.
AI might be a force multiplier for going through your resource library and updating assets to improve organic search referrals, for example. That gives your team more bandwidth to interview customers and produce rich case studies and buyers’ guides that help close deals. AI also has natural applications to “grunt work” such as developing meta descriptions and headlines.
As you define the ideal moments for AI-assistance, make sure you take a step back to adjust your team’s workflows to integrate AI, whether that’s offering insights on a topic, tailoring the tone for a particular audience segment, or simply producing a first draft they can revise.
From there, weave performance monitoring and analysis into your processes, as product developers do, by conducting thorough user testing and acceptance before shipping anything. Never let your benchmarks gather dust, because AI and online behavior are constantly changing.
The role of an enterprise-grade CMS with built-in content intelligence
AI-assisted content creation and performance benchmarking have quickly moved from CMS nice-to-haves to essential capabilities. For example, WordPress already offers the Jetpack AI Assistant for generating copy and images. Parse.ly’s smart tags help with attribution, and its real-time performance monitoring lets you segment by AI-generated or assisted contributions.
When you invest in the right CMS, you foster greater collaboration between content teams, data analysts, and other stakeholders who can all access a unified platform. An enterprise-grade CMS also lets you scale your AI-assisted efforts when the time is right, all while safeguarding data from cyberattacks and maintaining strong governance.
Though there are still a lot of question marks around AI’s role in content strategy, it’s safe to say that hybrid content will become the norm over time, and the AI vs. human content performance debate will eventually be put to rest.
What won’t change is the need to look closely at how content helps achieve business outcomes — and analysis will beat assumptions every time.
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Shane Schick
Founder, 360 Magazine
Shane Schick is a longtime technology journalist serving business leaders ranging from CIOs and CMOs to CEOs. His work has appeared in Yahoo Finance, the Globe & Mail and many other publications. Shane is currently the founder of a customer experience design publication called 360 Magazine. He lives in Toronto.