A practical roadmap for retailers to prepare for the future AI-driven buying
Executive Summary
AI is reshaping eCommerce and how customers discover, evaluate, and buy products online. While AI’s dominance feels inevitable, most eCommerce teams have yet to implement AI strategies as of late 2025.
At a time when many shoppers are experimenting with AI, many eCommerce teams are slow to embrace the shift. The AI evolution is only just beginning, and while there is no certainty about how it will evolve, there is risk for eCommerce sites that are slow to react to the changes in play.
It feels risky to jump on board while a new technology is still evolving, but AI has been embraced like few previous technologies, and it’s already clear that the underlying technology will require eCommerce sites to rethink their strategies.
This article presents a pragmatic AI-for-eCommerce playbook that will encourage eCommerce teams to recognize the coming impact AI will have on their sites and begin to make some foundational changes in their approach.
AI Is Reshaping the eCommerce Buyer’s Journey
Despite the noise, most shoppers are still experimenting with AI. While six in ten may have tried AI, only about half as many use it at least once a week. Traditional search engines still dominate 94% of all searches as of July 2025, with only 6% going to AI chatbots.
Many shoppers may be worried about “AI hallucinations” and haven’t given AI all of their trust just yet. At the same time, AI’s potential for eCommerce can’t be denied. AI will certainly change the way people shop online. To argue that AI’s certainty is unclear feels a little like arguing back in the early 60s that color TVs would never overtake the black and white TVs.
AI adoption will continue to grow, and it seems obvious that it will dramatically change the face of eCommerce. Today, we find ourselves on a strategic precipice. While much of AI evolution is not fully known, the impact on eCommerce sites already seems clear.
- Customers using AI search are further along their purchase journey than those using traditional search. AI is likely to delay a buyer’s jump from search to eCommerce and spend more of their purchase journey with AI than they do today.
- AI’s interactive nature and personalized results will dramatically evolve shoppers’ expectations for eCommerce site search engines. Today’s search engines are simply not good enough when compared to AI.
- AI referrals are fed by product data–lots of it. Long-tail models will seek out long-tail solutions, and that means the sites with the most complete product data are likely to be most visible and ultimately win the AI game.
Sites will need to build out the foundational framework that will feed AI search engines. The process of expanding product data to make it more relevant, trusted, and preferred by AI search engines should start now.
Critical Trends We Can’t Ignore
Let’s start with what we already see happening that should be seen as triggers to rethink how AI will impact eCommerce, and while we should embrace some urgency in rethinking some long-standing strategies.
Product discovery is changing
Traditional search takes a “one and done” approach. AI search is interactive in a way that both humans and computers can refine the quality of the interaction and deliver better results. This approach creates a deeply personalized experience, but it also reduces traditional click-throughs and the associated search traffic that goes with it. Instead of chasing organic traffic volume, eCommerce sites should shift strategies to expand site relevance to a broader range of possible searches.
Today’s site search is cumbersome. Try searching for a G4, 3.2-watt, 2200K LED light bulb on a site as strong as Amazon. The results do not all meet the requirements, and it takes multiple clicks through the facets and filters to try to narrow the selection. Is it too much to ask for only products that meet the requirement with one search? AI promises to deliver what you need with fewer keystrokes. The challenge today is preparing your product information for this coming reality.
Organic traffic is changing
Organic traffic is declining for most sites. AI delivers fewer but more qualified clicks, while traditional search delivers more zero-click searches. Instead of thousands of possible links, AI search carefully curates a handful of quality links.
Traditional SEO won’t disappear, but optimizing for AI search will be just as critical and require new techniques.
Customer acquisition costs are rising
What does the cost of a paid search click have to do with AI? The average cost per click (CPC) has risen 40% in the last few years, well ahead of the inflation rate. As traditional organic traffic declines, the competition for paid search potentially accelerates. Rising CPCs means rising customer acquisition costs (CAC). Unless we expect fewer competitors or higher margins, the only way to maintain profitability is to drive up the conversion rate. We suspect that this will require eCommerce teams to increasingly embrace more aggressive on-page strategies that have historically been avoided. Many of these conversion rate optimization strategies are also the types of strategies that AI search engines will also reward.
Loyalty is fragile
Many have argued that eCommerce sites can lose money on a customer’s first order and still be profitable by calculating the lifetime value of the customer. This presumes that customers are loyal to your site and don’t need to be reacquired. This logic, however, is increasingly flawed. There is plenty of evidence that loyalty has been waning for years, but customers who search once are likely to repeat that behavior for subsequent purchases. AI will own more of the purchase journey and likely further weaken brand loyalty. Thus, customer acquisition has to be profitable on every order.
Decision-making is moving off-site
AI is drawing more product research away from the eCommerce site. This has several implications: less product browsing, and thus less ability for eCommerce sites to influence which products the customer considers. This likely shifts the focus to closing the sale and converting visitors into customers.
eCommerce customers may never see the homepage or even a category page, as AI search takes them right to the product page. This will have a profound impact on how eCommerce professionals see the product detail page (PDP). In some ways, it makes sense to think of the PDP as the new site homepage, and PDP pages need to be redesigned to close more business.
The PDP is the New Homepage
When the PDP is a customer’s entry point, and as organic traffic declines, this page needs to work harder and increase the conversion. Providing a shopper or a search engine with all the information they will need to make an informed purchase decision should be the page’s primary objective.
From Promotion to Balance
The days of deploying the same one-sided, manufacturer-supplied content that everyone else publishes need to be retired. Customers want a well-rounded product presentation that recognizes that not every product is perfect for every person. Customers increasingly expect an unvarnished and balanced PDP that gives a trusted perspective.
This balance is frequently found within user-generated content (UGC), which is both great for AI search engines and customers. The challenge, however, is that UGC is difficult to collect, and most products are thin in this regard. Sites need to get creative about how they present their products in a balanced light.
Product pages should not be reluctant to compare themselves to competitive products. This should be less about cross-selling and more about helping confirm to the buyer that they’ve selected the best option without the need to wander off to other products or potentially other sites. Keeping the buyer on the page is a key element of conversion rate optimization.
Think in terms of less hype and urgency and employ strategies that reduce the buyer’s purchase risk.
From Simple to Superset
Many sites have historically opted for simplicity in their PDPs. The idea is that they don’t want to overwhelm the user with a bunch of words. The reality of AI, however, is that the more content you have, the more likely it will fit within AI’s long-tail world. Product pages need to expand their relevance and provide as many answers as possible to potential customer questions to remove objections on the path to clicking the buy button.
PDPs should include all specifications or product attributes. The PDP should include detailed FAQs, configuration, and compatibility information. The page should also detail applications and use cases, and set expectations in a balanced and trusted fashion.
From static to fresh
AI systems and humans both respond to freshness as a trust signal. Pages that never change look stale and less reliable in a world where products, standards, and expectations move quickly. This is a new concept for eCommerce marketers who are reluctant to spend money to create original content (historically a strong SEO signal), let alone update that content periodically.
Seven AI-Enabled Capabilities You Can Use Today
It’s time to start building your AI playbook, and here’s a list of seven AI-enabled strategies all available today that can support the AI transformation.
AI for Attribute Expansion
AI can build an attribute superset by visiting manufacturer and competitor sites, collecting product detail that might be missing from today’s product presentation. The content should be both customer ready (bullets) and AI-ready (structured data). The process of expanding the product page’s relevance need not be exclusively internally focused and this is an excellent way to leverage AI to optimize for AI.
Competitive Content
Taking attribute expansion a step further, competitive content is the idea that search engines have lots of choices, and creating better and more complete content than your competitors is an excellent way to win the visibility game. Imagine a process that regularly reviews competitors’ product pages and leverages AI learning to incorporate their keywords, features, and benefits to create a better presentation. Search optimization is a blood sport–for you to win, someone has to lose.
AI-Driven Visitor Engagement
In a retail store, a customer is likely to be greeted by a sales associate who can answer questions and help the customer narrow their choices. While some sites have live human chat, most don’t. It’s often difficult to get personalized eCommerce help, and yet AI will dramatically drive up expectations for personalized services. AI chatbots can become experts in every detail of every product, answer questions, and narrow choices, giving customers the links they need to the products they are likely to purchase, driving up the conversion rate.
Content Refresh
If AI takes control of building the content, there is no reason it can’t also update that content. It can self-optimize and learn what it takes to increase conversions and visibility. It can adjust content seasonally, respond to competitive threats, and highlight the latest UGC. And, as we’ve established, fresh content helps drive AI visibility.
Leveraged User-Generated Content
AI loves user-generated content (UGC). It’s in the voice of the customer, it’s fresh, and it fits well with other users who are searching for solutions. The most common UGC are product reviews, as well as posted questions and answers. While the value of UGC is clear, it’s difficult to amass enough of it. Amazon’s Vine program is essentially a pay-for-review approach where sellers give away free products in exchange for reviews. This confirms both the value and difficulty of getting organic reviews. For virtually all other sites, review rates are falling, and it’s fairly common to stumble across “be the first to review this product.” AI can comb through all reviews from across the internet and create UGC summaries that accomplish the same goals.
Agentic Search & Service
Replacing the one-and-done searches with a conversation designed to pinpoint the requirement and match that to a selection of products is the future of what eCommerce sites will do. Whether through an agentic chat from the search bar or as an on-page option, customer interactions need to increase.
Beyond pure attributes, agentic search can guide customers using the language of the customers. Searching today for a simple sweater, classy pants, or a lightweight mug might not produce traditional results, but agentic chat can do a better job of guiding the customer.
Agentic search or chat is the process of interacting with the shopper to both clarify their requirements and deliver the top options. If you have humans doing this job, you should be congratulated for making this investment, but few humans have the capacity to be an expert in every product, every detail. Few humans have read every owner’s manual or engineering specification. AI, of course, can be trained exclusively in your products. Agents will never forget, are trained to limit choices, and consistently improve the conversion rate.
Reinventing the PDP: From Promotion to Trust
Product-detail pages have traveled a great distance since the early days of eCommerce. In the early days, there were few details and few photos. The product descriptions were presented as a list of abbreviations that required some expertise to decipher. In the early 2000s, some standardization started to emerge. Photos became common, and product details started to emerge. Initially, manufacturers viewed content creation as the role of the seller until they realized that it was in their best interest to help by providing accurate product information. By the 2010s, it became clear that this duplicative content provided no help with search engines. Some sites embarked on strategies to increase their organic search visibility by creating unique content, while other sites viewed this as a luxury that they couldn’t afford.
AI changes all of this. AI’s approach to product recommendations mirrors that of how a customer might search for a product. For many who have grown up seeing thousands of advertisements every day and are quickly able to discount much of what we hear as untrustworthy. Just as a shopper tries to dig into find the real story behind a product, so does AI. While eCommerce PDPs started as promotional pages with an emphasis on “buy now” today’s AI-optimized PDP should be a balanced presentation optimized for trust.
AI Optimized PDP Checklist
- Balanced information that moves beyond superlatives
- Comprehensive details so buyers don’t need to search anywhere else
- Authentic presentations that feel real and lack an over-produced glossy finish.
- Independent content that leverages UGC and may not always be 100% positive.
- Site benefits should appear on every product page.
- Fresh updates that signal someone is minding the store.
In essence, as AI takes hold, the product detail page should provide all the information necessary to convince a customer to buy. This needs to go beyond the one-sided manufacturer’s presentation and should anticipate and address the types of questions and concerns that customers are likely to have. Every product is not perfect for every person, and by highlighting a product’s limitations, you are likely to build trust for your site.
Tomorrow’s ideal product page likely looks very different than the historic best practices. In the past, most sites relied heavily on others to build out their PDP, but this will have to evolve.
A Practical AI Playbook for 2026 Planning
How do we get from here to there? This is not a time to sit on the sidelines to see how AI develops in the months ahead. While much of AI will most certainly evolve (and hopefully improve) in the months ahead, AI’s need for product content is not likely to change. Here’s a simple, uncontroversial game plan.
- Develop an enhanced product content plan.
- Test something (anything) AI specifically designed for eCommerce.
- Pay more attention to your competitors.
- Measure and track conversion rate over time.
As demanding as AI might feel right now as you begin your transition, the very good news is that AI is also here to help. Virtually all of the challenges that AI presents are largely solvable by leveraging AI technology. Even better, the costs today are a fraction of what you might have expected to pay even a year ago. Jump in, get wet, and you’ll be swimming in no time.
This document maps key statements from your AI Playbook draft to supporting references. Each entry includes: (1) the sentence (or recommended phrasing) that merits a footnote, (2) a brief description of the supporting evidence, and (3) a full source URL for easy copy/paste.
Sources:
Industry reports show rapid growth in AI-powered shopping behavior and generative-AI referrals influencing product discovery and purchase decisions. Source
B2B and retail studies indicate strong experimentation with AI/ML, but relatively few organizations report fully integrated, mature AI strategies. Source
Consumer AI usage research quantifies broad trial but much lower habitual, high-frequency usage. Source
Wall St. Journal research. Source
Explainers and studies on AI search describe conversational refinement and narrower sets of surfaced links versus classic SERPs. Source
Zero-click and AI overviews quantify declining outbound click share from search results pages. Source
Analyst work on AI search and zero-click behavior shows users rely on on-page summaries and AI answers before deciding whether to visit a brand site. Source
CPC trend analyses document significant multi-year increases in Shopping and search ad CPCs for commerce-related terms. Source
Consumer trend studies show rising willingness to experiment with new brands and channels, signaling more fragile loyalty. Source
Retail AI and analytics reports describe shoppers using AI assistants for research, comparisons, and deal-finding prior to visiting merchant pages. Source
PDP best-practice guides emphasize that most purchase decisions are made on PDPs rather than homepages, highlighting their role as key entry and conversion points. Source
Studies on ratings, reviews, and rich content show significant lifts in conversion and buyer confidence when robust UGC and detailed information are present. Source
Search and experience guidance from major platforms highlight recency, relevance, and ongoing updates as quality indicators. Source
Search and experience guidance can drive up conversion rate by 26%. Source
Multiple UGC studies show shoppers rely heavily on reviews and Q&A, with measurable conversion and revenue impact. Source
Official search documentation explains that duplicate content provides limited value and emphasizes unique, helpful content for better performance. Source