Balancing AI and SEO: The Risks of Overuse and How to Stay Authentic (Part 2)

• Shallow, Unoriginal Content – AI tools can piece together words based on patterns and training data – but they don’t bring original thought. When overused, the result is often generic content that lacks depth, fails to answer user intent, and doesn’t stand out on the SERP. Google’s Helpful Content Update specifically targets this kind of “empty-calorie” content.
• Google’s AI Detection & Penalties – Search engines are getting smarter at spotting AI-generated material, especially when it’s lacking in value, originality, or human insight. Google prioritises authentic, well-researched content created for people, not algorithms. Rely too heavily on automation, and your rankings could take a hit.
• Keyword-Heavy but User-Light – AI may know how to incorporate keywords, but it often misses nuance, like context, tone, and natural flow. This can lead to keyword stuffing or content that reads more like a checklist than a conversation.
• Increased Risk of Duplication or Plagiarism – When many brands use the same AI tools fed by the same data sources, content across the web starts to look eerily similar. Google’s algorithms are trained to detect and devalue duplicated or low-originality content.
• The Echo Chamber Effect – AI models generate content based on existing data, often echoing dominant narratives or widely accepted viewpoints. This can limit diversity of thought, reinforce bias, and stifle fresh, independent reasoning. Over time, this risks turning your content strategy into a digital echo chamber rather than a source of original perspective.

• Blind Spots in Analytics – AI can highlight patterns in SEO performance, but it can’t always tell you why a drop happened or what move best aligns with your broader marketing goals. Without human interpretation, even good data can lead to poor decisions.
• Set-and-Forget Mentality – The ease of AI can lead to complacency—publishing content at speed, without regularly reviewing its relevance, quality, or actual performance. SEO is dynamic, and strategies must evolve constantly.

One of the most common issues with over-automated marketing is that everything starts to sound the same. Tools like ChatGPT and Jasper are great for ideation, but without human editing, brands risk producing generic, robotic content that lacks tone, personality, or genuine insight. Your brand voice should feel consistent, distinct, and human—something AI, on its own, still can’t replicate well.

With AI, content can be produced at scale, but volume doesn’t equal value. Overloading users with AI-generated blog posts, emails, or social updates can lead to disengagement. People notice when your content starts to feel formulaic. In the long run, this hurts trust and brand affinity.

AI-driven marketing platforms often use behavioural data to personalise messaging—what ads people see, what emails they get, even what price they’re offered. But how much data is too much? There’s a fine line between “personalised” and “creepy.” Over-targeting, especially without transparent data usage, can erode trust fast.

AI models are only as good as the data they’re trained on. If that data contains biases (and it often does), your marketing content can unintentionally reinforce stereotypes or exclude groups. This isn’t just a PR problem—it’s an ethical one. Brands must ensure diversity and inclusion aren’t just buzzwords in their messaging, but principles baked into their tools and processes.

Chatbots and AI-generated emails can handle high volumes and provide quick answers, but they’re not a replacement for human support. When customers are frustrated, confused, or have complex problems, a generic AI response can make things worse. The result? Damaged relationships and lost business.

AI models, particularly large-scale ones like ChatGPT and image generators, require massive computational power. This power is delivered by vast data centres, which draw huge amounts of electricity—often sourced from non-renewable energy. According to some estimates, training a single AI model can emit as much carbon as five cars over their entire lifetimes.

And it’s not just about electricity. These centres also use enormous volumes of water to keep servers cool—sometimes millions of litres per year. That’s a major sustainability concern, especially in regions already facing water scarcity.

AI infrastructure relies on high-end hardware—GPUs, servers, and networking equipment—all of which contribute to the growing problem of electronic waste. Disposing of this hardware often means dealing with harmful substances like mercury, lead, cadmium, and other heavy metals, which can leach into soil and water if not properly handled.

The rapid evolution of AI tech also shortens the life cycle of devices. As companies chase faster processing power and more efficient chips, old hardware is replaced frequently, adding further strain to global waste management systems.

While these impacts might seem far removed from the day-to-day work of a digital marketer in Perth, they’re part of a larger conversation about sustainable tech use. As AI becomes more embedded in content and campaign workflows, marketers—and the agencies they work with—have a role to play in using it responsibly.

Ask yourself:

• Are you using AI where it truly adds value, or just for convenience?
• Are your vendors or AI tools committed to energy-efficient practices?
• Can you offset your digital carbon footprint by reducing unnecessary output or investing in sustainable tech?