How AI is upending search optimization

A paradigm shift is underway in optimizing organic product discoverability online – with AI in one corner, and search engines in the other.

For the past 30 years, since the launch of Yahoo! Search and Alta Vista in 1995 to the Google of today, we have learned to use search engines to find inspiration and product information. Like never before, we have become fully empowered to make informed decisions about products before we buy, whether it’s for a private purchase or in a business role. At this point, Google is now the starting place for most commerce, both online and in the real world, as buyers not only research products, but look for retailers, reviews, and more.

For resellers, brands and manufacturers, optimizing discoverability for Google is vital for sales success. However, the rise of AI and the increasing complexity of omnichannel strategies, larger assortments, and more product information have made this task exponentially harder.

The Google challenge

If ten pages present identical information on the same products, Google will only show the top two or three results and will filter out the rest as duplicate information. This represents a serious challenge for smaller resellers or brands who are trying to capture market share. For that reason, to rank higher on Google, resellers and brands have traditionally tweaked product information slightly for each touchpoint to make it unique, which avoids duplicate content being presented online.

This is a challenge for brands and manufactures; to enforce brand compliance standards across the world when every reseller is incentivized to tweak their content to stand out. For resellers, the challenge is to deliver an accurate description of a product or a brand, while making sure the content is unique enough for Google to find and index it.

This has created a state of the world where brand inconsistency is rewarded in the name of discoverability. However, that world is rapidly changing with the advent of easily accessible AI.

The rise of AI

AI tools, particularly Large Language Models (LLMs) like ChatGPT, are rapidly changing the landscape. A recent survey by Storyblok found that nearly 20% of buyers now use AI models as their primary source of product information, with 15% considering them the most trusted source, compared to 37% for Google.

While Google is still the biggest source of search traffic, consumers are quickly changing their behaviors, AI product searches have gone from non-existent to 20% in just two years. This is only the beginning of the paradigm shift.

In contrast to relying on uniqueness and discoverability for Google, ChatGPT leans on consistency and confidence in product data when deciding what recommendations to make. That’s because ChatGPT prioritizes structured and reliable data to compare brands and products, when generating responses and recommendations.

If a brand’s product data is inconsistent, those products will appear in fewer AI-generated recommendations than their competitors. AI favors structured, consistent, and reliable data to base the comparisons on.

Inconsistent product data will lead to lower AI discoverability, fewer impressions and recommendations, and ultimately lower sales and market share and conversions by up to 20%, demonstrating the power of AI-driven visual content.

Recommendations for 2025

Diversify accurate content

• Ensure unique but consistent messaging to maintain brand integrity. While the core facts (specifications, values and key benefits) must remain consistent and accurate, slightly varying the product story across resellers can improve discoverability.
• Suggest that resellers include unique images, videos or other rich content to differentiate beyond core information and facts.

Tailor descriptions for different audiences

• Customize descriptions per reseller audience, for example, a tech reseller could focus on the features, while a lifestyle store emphasizes aesthetics, while they both share the same core information.

AI considerations

• AI-powered search associates brands with specific messaging, so inconsistent product data and attributes will create confusion, and lower the trust in the data for AI’s, resulting in fewer impressions.
• While valuing consistency, many AI models also look to “build consensus” by combining multiple different sources or viewpoints. Providing different content sources with consistent facts and attributes, but slightly different stories, may help improve AI discoverability.

The bottom line

It has never been more important, or challenging, for brands to enforce standards and consistency with their product data. To ensure maximum visibility in every channel, brands need to vary the product content slightly across resellers – while always maintaining factual consistency with key product data and attributes.

The requirement that brands optimize discoverability in two separate ways in parallel, one for Google and for AI, in addition to multi-channel selling, makes the need for a highly capable PIM even more urgent. A PIM designed from the ground up to make complexity more manageable becomes a key enabler. It gives brands the ability to run and close multiple optimization loops, where data enrichment is quickly customized and re-syndicated based on live insights from how content is showing up at endpoints downstream

Inriver: Your Dedicated Partner in E-Commerce

Enhancing your e-commerce conversion rates is a multifaceted endeavor. It involves optimizing product images, crafting compelling calls to action, simplifying navigation, and leveraging digital shelf analytics.

At Inriver, we understand the complexity of managing and optimizing product information across multiple channels is not as simple as most would expect. Our Product Information Management (PIM) solution is designed to centralize and streamline your product data, ensuring consistency and accuracy. This centralized approach benefits the customer and helps drive increased discoverability online.