AI is changing how we do pretty much everything, and that includes selling on Amazon. In the video below, I talk through how AI can genuinely help your listings, and the areas where it can cause problems if you rely on it too heavily. If you want the full breakdown with examples, watch the video, then use the points below to get the key ideas straight.
AI: Friend or Foe?
A lot of sellers think using AI to write a listing is a quick win. You enter a prompt, it produces something that looks like a product description, and you paste it straight into Seller Central. That is usually where things go wrong.
AI often gets details wrong. It can miss sizes or technical features, repeat keywords until the copy sounds robotic, or produce content that looks almost identical to other listings. Because so many sellers are doing the same thing, Amazon ends up full of listings that all feel the same.
A strong listing needs to help customers properly understand the product. If anything feels inaccurate or off, trust disappears immediately. That is why letting AI write the entire listing is rarely the right approach.
How I Actually Use AI on Listings
I use AI as a starting point, not the finished output. One of the best places to begin is customer reviews. Reviews show what people genuinely care about, what they like, what frustrates them, and what they mention repeatedly.
By feeding those themes into an AI tool, you can generate a rough set of bullets or an initial description. From there, the real work begins. I rewrite anything that feels flat, correct any guessed details, and add important points that were missed.
The final version always reflects the brand voice and stays completely accurate. AI speeds up the groundwork, but the part that convinces someone to buy still needs a human touch.
I also mention Rufus in the video. This is Amazon’s own shopping AI that appears partway down the product page. It gives a clear indication of the questions shoppers are asking in your category, which can help you refine and strengthen your content.
Why You Should Test Everything Before Uploading
AI is useful for generating variations, particularly for titles and images. That said, nothing should go live without testing. Tools such as Pikfu allow you to put different versions in front of real people and see which one performs better.
Sometimes a simple change, like swapping an image or tightening a title, can significantly improve click through rate. This is where AI becomes genuinely valuable. It can generate ideas quickly, but the final decisions should come from real people.
Using AI to Understand Your Data Faster
Seller Central already gives you the metrics you need, including sessions, units ordered, and conversion rate. AI can help you interpret this data more efficiently by highlighting trends, flagging underperforming listings, or showing what is improving.
This approach is not limited to Amazon. You can apply the same process to Google Ads, Shopify stores, or any other sales channel where performance data is available.
The Simple Three-Step System I Use
At the end of the video, I break everything down into a straightforward three-step process:
- Draft – use AI to explore ideas and pull out common review themes.
- Test – rely on real customers to decide which version performs best.
- Analyse – review Seller Central data, supported by AI summaries, to keep refining performance.
Used this way, AI becomes a practical tool rather than something that creates problems. Watch the video for the full explanation, and keep an eye out for the case studies and deeper dives coming next.
