Machine Learning or Higher Learning: Striking the Balance in SEO and Content Strategy

Matthew Capala is the Founder of Alphametic, a search marketing agency, keynote speaker, and author of "The Psychology of a Website."

The world is in a fierce debate on whether AI is the best or worst thing to happen in our generation. Divisions in opinion are especially pronounced between content creators and how machine-generated content will be used for Search Engine Optimization (SEO). Traditionally, content creation has been the domain of skilled writers armed with language proficiency and subject matter expertise. When machine-generated content was first introduced in ChatGPT 1, writers poked fun at the content and how clumsy it was compared to human-generated content. But the latest renditions of machine learning (ML) technologies are a brand new contender. 

Many businesses are considering whether and how they should adapt their content generation, SEO strategy, and marketing technology processes to meet the age of AI. Founders ask themselves, to what degree they can rely on human creativity vs machine? As SEO and content marketing experts with additional expertise in AI, we wanted to dissect the arguments and suggest ways to amplify your reach without losing your audience or voice. Particularly concerning keyword research and volume, and outlining, these tips are going to save you time and help you use AI to your SEO advantage. 

The Case for Machine Learning 

There is no question that AI-driven content creation is a game changer for scaling content and efficiency. Machines scrape thousands of pages of the internet and are fed hundreds of thousands more information daily. Their algorithms analyze huge amounts of data and find trends, patterns, and preferences with surprising accuracy. While this enables content creators to more quickly optimize content tailored to keywords and target audiences, they have to be very careful to check sources, recheck who their targets are, and search for holes in the reasoning. 

Machine learning continually refines content to improve engagement and relevance. Creators can also have their work proofread and modified. An iterative process like this can make human-generated content more accurate and aligned with search engine algorithms and audiences. 

Where we think AI really excels is in keyword research. Analyzing keyword trends, competition, and search volume from low to high-value keywords is a perfect application for AI. AI leverages predictive analytics and natural language processing that can uncover hidden keywords. Human writers can use these to create new content that connects authentically to their audiences as well as perform on search engines. AI can take over the mundane tasks of content optimization, A/B testing, and performance tracking. An overriding theme in the case of AI is taking over the administrative portions of content creation optimization, and testing, while human brain power for strategic initiatives. 

The Case for Higher Learning

With so many case studies and potential case studies, where is the room for human-generated content? We saw writers take to the streets in Hollywood, showing both the threat and making the case for human-generated content. Advocates for human-generated content argue that creativity, nuance, and emotional intelligence can’t be replicated. Where we see machines continue to learn, at a pace that is not capped, will they really be able to replicate content as well as humans? 

Human writers are able to synthesize cultural trends, nuance, and linguistics that machine learning cannot. Machine learning software and programs may be fed rules and prompts that create more specifically aligned content and Mar-tech companies are betting that their continued evolution will revolutionize aspects of creative and SEO industries to name a few. The fact is, human-generated content also ranks higher in SEO. Case studies continue to emerge for machine learning in creative writing and SEO. But what are the real limitations in critical thinking required to properly align with diverse audiences? 

Various news outlets and comedians publicized some hilarious examples of AI-generated content. The New Yorker reported on the shortcomings and implications of AI in ways that perfectly personify the shortcomings of machine-generated content. In particular, they point to deficiencies in planning and strategy. When machine learning and machine-generated content were in their first versions, outputs were often blatantly inaccurate and made apparent errors, however, the latest versions have the capacity to learn novice-level strategies. According to a recent report, ChatGPT 4 can play novice-level chess but fails to make simple math computations. Yes, machines can think, but they cannot strategize. 

Making emotional connections, strategic expertise, and the capability for critical thinking cannot be replicated with artificial intelligence. Where critical thinking is required to engage with an audience in an authentic way, keep writing human-to-human. 

Navigating the Intersection of Machine Learning and Higher Learning

Using AI-generated or human-generated content for SEO is not a binary choice but a balancing act. The theme that we continue to see in businesses and in digital marketing is that machines cannot solve sales. Where marketing is the chief tool of sales, humans need to be able to gauge how efforts are connecting with audiences. Machine learning can read the data produced from campaigns, but the insights are shortsighted, especially when your customers or clients are human. The key to SEO success lies in striking a balance between human-generated content that connects with tedious AI processes. 

Winning content strategies in marketing usually involve a hybrid and adaptive approach. We recommend using first-party prompts to infuse content with originality. There are many guides that are created to suffuse machine-generated outputs with content that is aligned with the voice and message of a brand. The use of AI for data analysis, keyword research, and performance tracking, frees up time for strategy and audience analysis. AI can streamline aspects of content creation, but even the latest iterations of machines that can play novice chess are unable to replicate human creativity and empathy and apply critical thinking. Checkmate, machine learning.