INC Magazine: 4 Ways to Drive Product Sales in an Unstable AI Landscape

There’s a lot of AI confusion out there, particularly when it comes to e-commerce. Increasingly, AI is responsible for more and more internet referral traffic, yet no one really knows how it’s choosing what it serves to users.

When it comes to the DTC world, it’s a mystery how an AI chatbot separates what’s ‘official’ trustworthy product information from everything else. If you used AI to search for bike components, would the bots serve results skewed towards popular but incorrect audience opinion as opposed to official fact? Right now, studies show it could go either way.

The good news is this problem isn’t new to the internet. With time, issues like these have always stabilized once marketers and brands were given more access and information. Until then, the best anyone can do is trial and error—allowing some “experts” to profit by promising unproven solutions in an uncertain time.

Here’s the truth: if you want to optimize your site for product discovery in the era of AI, today’s tactics will not be the same as tomorrow’s. That said, there are a few key considerations that e-commerce brands should keep in mind to give themselves a fighting chance.

1. Focus on textual information

AI doesn’t care if your site or product looks cool. The internet is traditionally a very visual place, and as such, many websites try to convey product value and other information with a range of photos, video, animations, fun page transitions and other tactics.

But this doesn’t translate to an AI-centric approach because, while AI bots can still read text in images, they are decidedly immune to “vibes”. That’s partially why Amazon speaks AI’s language so well – aggressively prioritizing text-first, structured information like modules, bullets, comparison tables, spec definitions, and FAQs. They’re perfect for LLMs that don’t “feel” design the way humans do.

2. Speak in problem/solution language

AI loves to solve problems. Most people tend to use AI to search for solutions to problems. Because of this, reviews offer the type of content and format that tend to be irresistible to AI bots. When a customer talks about a product that solved a problem for them, it’s exactly the kind of language that lends itself to meaningful options and suggestions coming from AI tools and services. To aid AI, Dyson embodies a similar principle with brand messaging that speaks almost exclusively in problem/solution language with every module as a micro–case study explaining how the product resolved a user pain point.

3. Prioritize product education

AI has birthed a new shopper. In the past, the expectation was that shoppers would use a multi-step online search approach—first, describing their problem and reading about different solutions, then picking a few potential solutions and comparing their features before buying. As a result, most product pages prioritized product features, assuming consumers would need those to make a choice. AI search collapses that traditional funnel, so someone can go immediately from awareness to purchase. As a result, modern website content strategies must evolve to take on an awareness-first approach. Like cookware brand Caraway, which uses educational storytelling, material science, safety framing, and lifestyle positioning to let shoppers skip traditional comparison research.

4. Evolve quickly

AI is evolving fast. Rules around AI optimization are changing daily and it can be hard to keep up. When in doubt, go to major retailers like Amazon and follow what they’re doing. Health technology company Oura is another great example of this philosophy in action. Not only do they mirror the Amazon playbook with segmented content, case-study blocks, high-scannability, structured insights and problem-led comparisons that AIs can easily lift as authoritative answers. They also constantly update their page architecture to match shifts in AI-driven discovery and health-tech search patterns, treating product pages as living infrastructure, not static design.

The current lack of transparency in AI optimization won’t last forever. Eventually, there will be a required level of platform maturity and tools so that brands can take concrete steps to make organic discovery in an AI environment better for their own product goals. But until then, brands need to be patient, keep an eye on AI’s evolution and steer clear of anyone promising to have all the answers.

Read the original at INC Magazine