AI search is changing ecommerce discoverability

Search is changing.

For years, ecommerce visibility has largely meant understanding Google, search rankings and SEO. Merchants created pages, optimised keywords, improved metadata and worked to make sure Google could understand what their products and services were about.

That model is not disappearing overnight. But it is becoming only one part of a much larger discovery ecosystem.

Consumers are increasingly asking questions directly from tools such as ChatGPT and Gemini instead of starting every search with a list of links. The difference is fundamental: rather than simply finding pages, AI systems can combine information from multiple sources, interpret context and return a more complete answer.

For ecommerce companies, this changes an important question.

It is no longer only:

How do we rank?

It is increasingly:

How do we make sure AI systems can understand what we sell, who it is for and why it is relevant?

This was the focus of a recent Ecommerce Suomi Podcast conversation with Digizer’s Jaakko Heikkinen.

Who’s in the conversation?

Jaakko Heikkinen

Jaakko Heikkinen is responsible for sales and customer relationships at Digizer and is one of the company’s owners. Digizer is a Finnish digital commerce specialist helping ecommerce companies grow and develop their digital sales, with a focus on areas including marketing, customer service, accessibility and conversion. Heikkinen has worked with ecommerce sales, marketing and digital sales development for roughly nine years.

Jussi-Pekka Moilanen

Jussi-Pekka Moilanen is co-host of Ecommerce Suomi Podcast and an experienced ecommerce industry executive, bringing the perspective of commerce technology, digital business and the Nordic ecommerce ecosystem to the discussion.

Oskari Aaltonen

Oskari Aaltonen is host and founder of the Ecommerce Suomi Podcast, leading conversations with ecommerce operators, technology companies and industry experts around growth, customer experience, technology and the changing digital commerce landscape.

Search is shifting from links to answers

One of the clearest differences between traditional search and AI search is what happens after the user asks a question.

Heikkinen describes testing the same travel-planning question with Google and ChatGPT. Traditional search returned a collection of results that still required interpretation. The AI tool instead processed the information, considered the requirements and produced a more complete recommendation.

The same logic can increasingly be applied to ecommerce.

A shopper looking for basketball shoes, for example, does not necessarily need to search for a specific product name. They can describe their foot shape, playing style, preferred support and other requirements and ask an AI assistant to identify suitable products.

This pushes product discovery further down the buying journey before the customer ever reaches an ecommerce site.

The implication for merchants is significant.

The information required to answer those detailed questions needs to exist somewhere.

SEO is becoming content optimisation

Traditional SEO has often focused on understanding what search engines require and making content fit those requirements.

Heikkinen argues that AI search brings the focus back toward the original purpose of ecommerce content: providing useful information to customers.

Instead of thinking only about search-engine optimisation, ecommerce teams may increasingly need to think about content optimisation.

That means creating information capable of answering a wide range of questions about a product, service or company rather than optimising a page around a narrow set of keywords.

This is an important shift.

The winning question may no longer be:

How many times should this keyword appear?

It may instead be:

How completely can we explain this product and the problem it solves?

More formats create a clearer picture

The traditional product page built around one image, a short description and technical specifications is becoming less sufficient.

AI systems can work across different information formats and connect individual pieces of information into a broader context.

That makes richer ecommerce content increasingly valuable:

• multiple high-quality product images

• video

• detailed product information

• frequently asked questions

• customer and product reviews

• comparisons

• use cases

• guides and educational content

• information explaining who a product is and is not suitable for

The goal is not to create more content simply for the sake of volume.

It is to make the product easier to understand from several different perspectives.

In the podcast discussion, Heikkinen describes this as building information that can answer as many relevant customer questions as possible.

For shoppers, that improves decision-making.

For AI systems, it creates more material from which to understand the product.

AI likes structured information

Rich content does not mean unstructured content.

One particularly practical observation from the conversation is that AI systems benefit from information that is clearly organised.

Heikkinen highlights formats such as tables, clear comparison points, FAQ sections and knowledge bases as useful ways of structuring information.

For ecommerce teams, this creates a relatively practical starting point.

Instead of immediately rebuilding an entire content operation around AI, merchants can ask:

Can our products be compared easily?

Are the most common customer questions answered clearly?

Is important product information hidden inside marketing copy or available in a structured format?

Can both humans and machines understand what differentiates one product from another?

Good structure benefits both.

Your brand is defined outside your own website too

This may be one of the most important changes in AI-driven discovery.

A company can make any claim it wants on its own website.

It can say it is the best, most experienced or most trusted option in the market.

But an AI system can potentially compare those claims with information found elsewhere.

During the discussion, Heikkinen points specifically to customer opinions, reviews and other external sources as increasingly important context. What the company says about itself is only one part of the information available.

Later in the conversation, the idea becomes even more explicit: what matters is both what your content says and what is said about you elsewhere.

This changes the role of earned and third-party visibility.

Customer reviews, industry publications, expert commentary, podcasts, media coverage, community discussions and other independent sources can all contribute to the wider digital context around a company.

The same theme is increasingly visible across the ecommerce industry: brands cannot rely only on repeating their own message through owned channels.

As AI systems synthesise information from broader sets of sources, consistency becomes increasingly important.

A strong brand should ideally tell the same credible story wherever information about it appears.

Authenticity becomes more valuable

AI also makes content production dramatically easier.

That creates opportunity, but it creates another problem at the same time.

If every merchant can rapidly create similar product descriptions, images and marketing content, what differentiates one company from another?

The discussion points toward authenticity.

Merchants will increasingly have access to the same tools and, in many cases, the same manufacturer-provided product information.

Simply copying that information into another ecommerce store does not create much differentiation.

Original product knowledge, genuine customer experiences, specialist expertise, reviews and brand-specific content become more valuable precisely because generic content becomes easier to produce.

AI may make content cheaper.

It does not automatically make it more credible.

Fresh information matters

Content quality is not only about depth.

It is also about relevance.

The conversation highlights the importance of keeping ecommerce information current. Product information changes, customer behaviour changes and some information becomes less useful over time.

Heikkinen argues that regular content updates should already be part of normal ecommerce operations and become increasingly important in an AI-driven discovery environment.

For merchants, this means AI discoverability should not become a one-time optimisation project.

It should become part of ongoing content management.

What should ecommerce teams do now?

There is no complete AI-search playbook yet.

The technology is changing too quickly for that.

But the discussion identifies several practical actions ecommerce teams can already take.

Understand the tools.
Use AI search yourself. Learn how ChatGPT, Gemini and other tools interpret ecommerce questions and how their answers differ from traditional search.

Build a content strategy around customer questions.
Think beyond keywords. Identify the questions customers could ask before making a purchase and make sure your content can answer them.

Create richer product information.
Use text, video, imagery, reviews, FAQs, comparisons and use cases where they genuinely improve understanding.

Structure the information clearly.
Tables, specifications, comparison points and knowledge-base content make complex information easier to process.

Strengthen external credibility.
Pay attention to reviews, expert mentions, media, communities and other places where your company and products are discussed.

Keep information current.
Fresh and maintained content remains an important signal of relevance.

Focus on business value rather than AI hype.
The conversation also contains an important warning: new tools are easy to become distracted by. AI initiatives should still solve actual customer or business problems rather than becoming projects simply because the technology is new.

From AI search towards agentic commerce

The shift may eventually go further than discovery.

If an AI assistant can understand what a customer wants, evaluate products, compare external information and identify a suitable merchant, the next logical step is increasingly obvious.

The agent may also help complete the transaction.

During the conversation, this possibility comes up directly: AI tools could increasingly take a shopper much further into the buying journey rather than simply sending them to a generic product page.

That moves ecommerce from AI-assisted search toward something closer to agentic commerce.

For merchants, the underlying requirement remains remarkably similar.

Your products, company and value proposition need to be understandable to machines as well as humans.

The goal is no longer simply to rank

Traditional search visibility is not disappearing.

But the information environment around ecommerce is becoming broader.

AI systems can increasingly combine owned content, structured product information, customer reviews and external sources into a more complete understanding of a product or brand.

That means ecommerce discoverability is becoming less about manipulating a single ranking system and more about building a coherent information footprint.

The companies best positioned for this change will likely be those that can answer three questions clearly:

What do we offer?

Who is it relevant for?

Can the wider information ecosystem around our brand support what we claim?

The goal is no longer simply to rank.

It is to become a source that AI systems can understand, connect with wider context and confidently use when answering customer questions.

Listen to the full conversation

Listen to the full Ecommerce Suomi Podcast conversation with Jaakko Heikkinen from Digizer on Spotify or watch the episode on YouTube.

Spotify
https://open.spotify.com/episode/0GLSKdhhGmDiZVrDfiLKrt?si=d35c2247a2074eab

YouTube