I’ve seen a lot of “new eras” of eCommerce get announced. Most of them are just a fresh label on the same funnel. Agentic commerce feels different because it doesn’t wait for perfect UX or long adoption cycles. People are already using AI to shop, and the habit is forming faster than most businesses can redesign for it.
What’s happening first is simple: AI is becoming the pre-purchase brain. Shoppers are using it to compare options, summarize reviews, and reduce decision stress; basically, it’s a “super salesperson” that compresses research into one conversation. And once that becomes the default, everything downstream (visibility, trust, checkout, loyalty) starts to shift as well.
The adoption signals (from Fevad/Odoxa):
- 31% of French shoppers (15+) already use generative AI to make purchases online.
- ~50% of 15–24-year-olds use AI for shopping.
- 13% of seniors use AI for shopping.
- 54% of existing users say they’re using AI shopping tools “more and more”.
How Consumers Are Using AI in the Shopping Journey
Today’s consumers prefer to shop without AI assistance, which functions as a virtual companion that helps users eliminate distractions to reach their decisions more efficiently.
The biggest shift in consumer behaviour occurs before customers complete their shopping process. A recent survey showed that about 58% of folks use AI during the pre-purchase stage to compare products, skim reviews, and feel more confident about what they’re picking. People now use AI systems to find answers instead of opening multiple tabs.
This trend is most visible in the technology market and small appliance market because it has become more common. Shoppers prefer to avoid continuous scrolling when they encounter complicated products because they want to confirm their selection. AI simplifies the process because it provides users with straightforward decision-making options, which they perceive as safer.
Why AI Checkout Still Faces Trust and Adoption Barriers
People are happy to use AI to help them shop. The hesitation starts when AI is about to complete the payment. That’s the line where “nice and convenient” turns into “wait… am I still in control?”
One consumer survey captures that shift clearly. Trust sits at 47% when AI is used for research and comparisons, but drops to 30% when it’s time to validate the transaction. And you can see the same caution in spending behaviour too: shoppers are most comfortable delegating purchases under €50, which is telling when the average online basket in France was about €62 in 2025.
What’s really holding people back at checkout:
- They want to feel they’re making the final decision.
- They’re unsure what data the agent is using or sharing to complete the purchase.
- They don’t fully trust that the recommendation is neutral versus influenced by ads or partnerships.
- And when something goes wrong, they don’t know who’s responsible: the platform, the merchant, or the AI.
The Neutrality Question: Who Benefits From the Recommendation
This is where things get serious. When someone asks an AI, “What should I buy?”, they’re not just getting an answer. They’re accepting a ranked set of options. And once that ranking becomes the place people make decisions, it quietly becomes the new shopfront.
That’s why neutrality matters so much. Shoppers may trust AI to help them compare and simplify, but they also wonder what’s shaping the suggestions. Are results based on what’s best for the customer, what’s easiest to fulfil, or what someone paid to promote? Industry leaders are already calling out this risk because if a few dominant agents control recommendations, the power starts to look a lot like search engines and marketplaces all over again.
Platforms are also leaning into this shift. Google, for example, is building shopping experiences inside AI Mode that blend organic recommendations with sponsored placements and tailored offers. It’s great for convenience, but it raises the stakes for brands. If your product data isn’t structured and readable for AI systems, you don’t just lose ranking. You risk becoming invisible in the interface where the decision actually happens.
How Google AI is Changing Product Discovery and Online Shopping
For years, Google was where shoppers discovered products and then clicked through to buy somewhere else. That model is changing fast. With AI Mode, Google is shaping shopping into an experience where discovery and comparison happen within the interface, and the platform keeps users moving toward purchase without the usual “open 10 tabs” journey.
From my founder lens, this is the real disruption: your website is no longer the only shopfront that matters. The new shopfront is the AI layer that creates the shortlist. If your products aren’t visible and understandable there, you don’t lose a little traffic. You lose the decision moment. And that forces every retailer to think beyond SEO and ads and start thinking like an AI-integrated commerce system.
Key Agentic Commerce Takeaways for Retailers
- Shopping is becoming conversational and comparison-led inside search, not just on retailer sites.
- Visibility will increasingly depend on clean, structured product data and real-time feeds that AI can trust.
- The winners won’t be the brands with the loudest campaigns but the ones who are easiest for AI systems to interpret, validate, and recommend.
Why AI-Ready Product Data is Critical for Agentic Commerce
Most brands I talk to are chasing shiny AI features. A chatbot here, some generated copy there, maybe a “personalized” widget on the homepage. It looks modern, but it doesn’t change the core problem.
The problem is simple: if an AI agent can’t clearly understand your catalogue, pricing, stock, delivery options, and returns rules, it can’t confidently recommend you. And in an agent-led shopping world, “not confidently” usually means “not at all.”
So the real work is boring on purpose. Clean product data. Consistent attributes. Real-time availability. Accurate shipping promises. Systems that can be queried reliably by machines.
And this isn’t theoretical. Shift’s 2026 launch is positioned around stable, standardized APIs so AI systems can interact with delivery, tracking, and returns without brittle integrations, while businesses still keep validation and control.
If you want proof that this is becoming table stakes, look at the adoption pressure. Shopify’s 2025 research says nearly 90% of retailers actively use AI or are considering it, and 87% report a positive revenue impact. That’s the market telling you the baseline is moving fast.
Why APIs are Becoming the Foundation of Agentic Commerce
- When AI starts doing things, the hard part isn’t the AI. It’s whether your commerce stack can actually be “operated” without breaking.
- Agents don’t click around and improvise as people do. They need clean, reliable APIs to understand what’s possible and what isn’t.
- If your shipping options, stock status, pricing rules, or return policies are messy or inconsistent, the agent won’t trust them. And if it doesn’t trust it, it won’t choose it.
- The basics matter more than ever: checking inventory, confirming price, pulling delivery promises, placing an order, and starting a return. These need to work like clockwork.
- You also need guardrails so the business stays in control, like spending limits, approval steps, and validation checks before anything is finalized.
- In the agentic era, the brands that win won’t be the ones with the fanciest AI features. They’ll be the ones with the most dependable infrastructure behind the scenes.
Real-World Use Cases of AI in eCommerce Today
The AI use cases that actually work today are the ones that remove friction without changing how people naturally shop. Think of it as “small wins at scale.” Shoppers get faster clarity through conversational help, smarter product discovery, and better answers to the questions that normally cause drop-offs. Merchants get relief where work is repetitive and expensive, like generating product listings, improving catalog quality, and handling high-volume customer queries.
What I’ve learned implementing these systems is simple: the best results come when AI is tied to real operational outcomes, not just surface-level automation. If it helps customers choose faster, reduces support load, improves listing accuracy, or cuts resolution time when something goes wrong, it’s worth it. If it only “sounds smart,” it won’t survive past the pilot phase.
Risks and Compliance Challenges in Agentic Commerce
Here’s the part leaders can’t skip: AI can help run the journey, but it doesn’t take responsibility for the outcome. You still do,
The risks that show up fast
- If an agent gets product details wrong, the customer won’t blame “AI.” They’ll blame your brand.
- If recommendations feel influenced or pay-to-play, trust drops quickly, and shoppers start second-guessing the whole interface.
- If the agent mishandles returns, delivery promises, or edge cases, support load doesn’t go down. It explodes.
The risks that hurt later
- Data and consent get messy when agents pull information across tools and platforms.
- Accountability becomes unclear when transactions happen inside someone else’s ecosystem, not on your site.
- Compliance becomes harder when decisions are automated but not properly auditable.
My rule is simple: if you can’t explain who’s responsible, what was decided, and why it happened, you’re not ready to let agents execute at scale.
Key Questions Businesses Must Answer Before Adopting Agentic Commerce
- How much control will shoppers actually hand over before they pull back?
- Where does “this is helpful” quietly turn into “this is deciding for me”?
- Why do people trust AI to compare products but hesitate the moment it gets close to payment?
- What makes a recommendation feel genuinely useful versus subtly influenced?
- When something goes wrong, who does the customer blame first, the platform, the AI, or your brand?
- And the big one: what guardrails do you need in place before you let an agent execute at scale?
The future of online shopping will not just be mobile-first, it will be AI-agent-first. Companies that adapt early will dominate tomorrow’s digital marketplace. If you’re exploring how AI can reshape your business model, consider learning more about AI-driven strategy and innovation.
