"Alexa, order more light bulbs."
That was peak voice commerce in 2021. Transactional, single-product, locked inside Amazon's ecosystem. The voice assistant knew your purchase history and reordered the same brand every time. Simple, predictable, and — if you were the incumbent brand — incredibly comfortable.
Now compare that to what I watched my neighbor do last month:
"ChatGPT, I just bought a 2-bedroom apartment. Help me set up a smart home on a $1,500 budget. I want security, lighting, climate control, and everything needs to work together."
ChatGPT responded with a complete ecosystem recommendation: Ring doorbell, Philips Hue lights, ecobee thermostat, Aqara sensors, and a HomePod Mini as the hub. It explained compatibility, suggested an installation order, and even flagged that the Aqara sensors would need a Zigbee hub.
That's not a product search. That's a personal consultant. And it's changing how consumers discover, evaluate, and commit to IoT brands in ways that should terrify anyone still optimizing for "best smart thermostat 2026."
Three Phases of IoT Product Discovery
The way people find IoT products has gone through three distinct phases, and most brands are still stuck optimizing for Phase 1.
Phase 1: Google Search (2014-2020)
"Best smart lock 2019." "Ring vs Nest doorbell." "Smart thermostat comparison."
This was classic SEO territory. Brands competed for position 1 in search results. The game was backlinks, review sites, and keyword-targeted landing pages. Wirecutter became the kingmaker. If they named you "Best Overall," your sales spiked for months.
The limitation: consumers had to know what they wanted. You searched for "smart thermostat" only if you already knew smart thermostats existed. Discovery was narrow and product-specific.
Phase 2: Voice Assistants (2017-2023)
"Alexa, add smart plugs to my cart." "Hey Google, what's the best-rated doorbell camera?"
Voice assistants introduced a new channel, but it was surprisingly limited. Amazon's Alexa heavily favored Amazon-owned brands and Amazon's Choice products. Google Assistant pushed Nest. Apple's Siri barely played in the IoT recommendation space at all.
Voice commerce hit a ceiling. Research from eMarketer showed that by 2023, only 8.4% of US adults had made a purchase through a voice assistant. The experience was clunky for anything beyond reorders. Asking Alexa to compare four smart thermostats was an exercise in frustration.
But voice assistants did something important: they normalized talking to machines for product advice. That habit carried directly into Phase 3.
Phase 3: AI Chat (2024-present)
"Claude, I'm renovating my kitchen. What smart appliances should I consider and how do I make sure they all integrate?"
This is where everything changes. AI chat isn't transactional like voice — it's consultative. Users aren't asking for a single product. They're describing a situation and asking for a solution.
We analyzed 500 IoT-related queries on ChatGPT, Claude, Gemini, and Perplexity. The breakdown:
- 43% were ecosystem queries — "Help me build a smart home" or "What do I need for a smart office"
- 31% were comparison queries — "Ring vs Arlo vs Reolink" or "Compare Matter-compatible hubs"
- 18% were problem-solving queries — "My smart home devices keep disconnecting" or "How to reduce smart home latency"
- 8% were single-product queries — "Best smart lock under $200"
Only 8% of queries looked like traditional product searches. The other 92% were conversations where AI made ecosystem-level recommendations.
Why Ecosystem Recommendations Change Everything
When a consumer asks Google "best smart thermostat," the result is a list. Ten blue links. The consumer clicks a few, reads reviews, compares prices, and makes a decision. Every brand on page 1 gets a shot.
When a consumer asks ChatGPT "set up my smart home," the AI doesn't give a list. It gives a curated recommendation. Three to five brands, presented as a cohesive system. And the brands that aren't in that initial recommendation? They often never enter the conversation.
We tested this with 50 "build me a smart home" prompts. Here's what happened:
- The AI recommended an average of 4.2 brands per response
- Philips Hue appeared in 88% of lighting recommendations
- Google Nest appeared in 76% of thermostat recommendations
- Ring appeared in 71% of security recommendations
- Brands mentioned in the initial recommendation were 3.4x more likely to be included if the user asked follow-up questions
That last stat is critical. Once the AI has established a recommendation framework, it tends to build on it rather than introduce new brands. The first response sets the anchor for the entire conversation.
The Ecosystem Lock-In Effect
Voice assistants had platform lock-in — you bought Ring because you had Alexa, and you stuck with Alexa because you had Ring. But that lock-in was technical and commercial. You could switch if you wanted to.
AI creates a different kind of lock-in: cognitive lock-in. When ChatGPT tells you that Philips Hue, ecobee, and Aqara work well together, you don't just buy those products. You internalize that combination as "the right answer." You tell your friends. You recommend it in Reddit threads. That creates a reinforcing cycle where AI's recommendation becomes conventional wisdom.
We tracked this on Reddit's r/smarthome and r/homeautomation. In 2024, the most-recommended brands in those communities closely mirrored what ChatGPT recommended. Correlation doesn't prove causation, but the pattern was striking.
How Retailers Are Adapting (And How They're Not)
The smart retailers have noticed. Best Buy launched an "AI-Recommended" badge program in late 2025 for products that appear frequently in AI assistant responses. Amazon has been quietly adjusting its recommendation algorithms to factor in how products are described by external AI engines.
But most IoT brands themselves haven't adapted. We surveyed 30 IoT brand marketing teams (at a smart home conference in January 2026) and found:
- 73% had never checked how AI engines describe their products
- 87% had no GEO strategy — they were focused entirely on SEO and paid ads
- 60% didn't know that AI engines recommend competing products when consumers describe their exact use case
The disconnect is staggering. These brands are spending millions on Google Ads while ChatGPT is reshaping how their target customers discover products.
What Makes AI Recommend an IoT Brand?
After analyzing thousands of AI responses, we've identified five factors that drive IoT brand recommendations in AI chat:
1. Ecosystem compatibility narratives
AI loves brands that clearly document what they work with. Aqara's detailed compatibility pages (Works with HomeKit, Alexa, Google Home, Matter) make it easy for AI to include Aqara in multi-brand ecosystem recommendations. Brands that only talk about their own features, without explaining how they fit into a broader system, get left out.
2. Specific use-case positioning
ecobee doesn't try to be everything. It's a smart thermostat focused on energy efficiency. That clarity makes AI confident about when to recommend it. Compare that to brands that position themselves as "comprehensive smart home solutions" — AI doesn't know when to surface them because they don't stand for anything specific.
3. Technical depth in public content
Brands with detailed API documentation, integration guides, and technical blog posts get mentioned more in technical queries. This matters because IoT purchases often involve a technical decision-maker (the household member who sets everything up). TP-Link's developer documentation, for example, appears in AI responses about home automation programming.
4. Third-party validation from experts
Not just Amazon reviews — expert reviews from The Verge, CNET, Wirecutter, and niche sites like HomeAssistantGuide.com. AI engines weight expert analysis heavily when making recommendations. A single in-depth review on a trusted site can be worth more than a thousand 5-star Amazon reviews.
5. Consistent brand narrative across platforms
Brands whose description matches across their website, Amazon listing, Best Buy page, Reddit mentions, and review sites get recommended with higher confidence. AI engines cross-reference these sources, and inconsistency creates uncertainty.
The Smart Home Consultant Effect
Here's something we didn't expect: AI is replacing the smart home consultant.
Professional smart home installation used to be a luxury. Companies like Control4 (now Snap One) and Savant sold through dealers who would design and install entire systems. That model worked because consumers didn't have the knowledge to build their own ecosystems.
Now they do. ChatGPT and Claude can design a smart home system in 30 seconds that would have taken a consultant an hour. The AI knows compatibility, price points, installation complexity, and user reviews. It's not perfect, but it's good enough for 80% of use cases.
This means the "discovery moment" for IoT brands has shifted from the showroom floor and the installer's recommendation to the AI chat window. If you're an IoT brand that relied on channel partners and installers to recommend your products, you need a direct-to-AI strategy now.
What IoT Brands Should Do Right Now
If you're a market leader (Nest, Ring, Philips Hue):
Your position is strong but not permanent. AI models update continuously, and a challenger that nails its ecosystem narrative could erode your share. Monitor your AI visibility weekly. Make sure your brand story is consistent everywhere AI might look.
If you're a challenger brand (Aqara, Wyze, ecobee):
You have a once-in-a-generation opportunity. AI is more meritocratic than shelf space or Google search. If your product genuinely solves a specific problem better than the market leader, AI can and will discover that — but only if the evidence exists online. Invest in comparison content, technical documentation, and expert reviews.
If you're invisible to AI:
Start by understanding where you stand. Run a free check at geobuddy.co/check to see how AI engines currently describe (or ignore) your brand. Then work backward: Why doesn't AI recommend you? Is it positioning? Lack of third-party coverage? Inconsistent messaging?
The Matter Protocol Wild Card
One development worth watching: the Matter smart home standard. Matter promises cross-platform compatibility — a device that works with HomeKit, Alexa, Google Home, and Samsung SmartThings simultaneously. As Matter adoption grows, AI recommendations could shift dramatically.
We're already seeing early signals. When we asked AI engines about Matter-compatible devices, the recommendations looked different from general smart home queries. Brands like Eve, Nanoleaf, and Aqara — which have aggressively adopted Matter — appeared more frequently in Matter-specific queries than in general ones. Established brands that have been slow on Matter adoption saw their visibility drop in these forward-looking queries.
The implication: if you're an IoT brand, your Matter adoption story isn't just a product strategy. It's an AI visibility strategy. AI engines are already learning the Matter narrative, and brands that are part of it will benefit as consumers increasingly ask about future-proof smart home setups.
The Window Is Closing
The shift from "Alexa, buy light bulbs" to "ChatGPT, build me a smart home" isn't coming. It's here. Consumer behavior data from multiple sources shows AI-assisted product research growing at 40%+ year over year. Voice commerce is flatlining. Traditional search is losing share to AI chat for complex purchase decisions.
The brands that win in this new discovery paradigm won't necessarily be the biggest or the most well-funded. They'll be the ones whose digital presence gives AI engines confidence to recommend them. That means clear positioning, ecosystem compatibility documentation, authentic community presence, and consistent messaging everywhere.
IoT brands that figure out AI-native product discovery will own the next wave of smart home adoption. The ones still optimizing for Phase 1 keyword rankings will watch their market share erode to brands that showed up where the customers actually are: in a conversation with AI.
The question isn't whether your customers are using AI to discover IoT products. They are. The question is whether AI knows your brand well enough to recommend it.
Originally published on GeoBuddy Blog.
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