A customer opens an AI assistant on a Sunday evening and types two requests. "Reorder the usual laundry detergent." And: "Find me running shoes for road races, under $120. I like Brand A and Brand B, and I don't want anything too heavy." The agent handles the first on its own. For the second, it compares a handful of options, checks reviews and specifications, and comes back with two recommendations for the customer to choose between and pay for.
Those two sentences contain most of what marketers need to understand about agentic commerce in 2026. The first is a purchase the customer has fully handed over. The second is a decision the agent shapes but the customer still makes, within limits they set, and it names two brands before the agent has looked at a single product page. When agents join the buying process, marketing's job moves to either side of the transaction: getting named in the brief the customer gives the agent, and owning the relationship after the sale. The middle, where so much conversion optimisation lives today, matters less than it used to.
This is a trends piece about what agentic commerce means for marketing. The technical side, including payment rails, catalogue readiness and margin, is covered in the rise of AI shopping agents, and the earlier assistant wave in the rise of AI shopping assistants.
Where things actually stand
Headlines about agents "doing the buying" run ahead of what most customers are willing to do. Surveys through 2026 show strong enthusiasm for AI help with research and comparison, and a sharp drop in willingness at the moment of payment. Accenture's survey of more than 25,000 people across 16 countries found that about a third would let an agent decide what to buy within set boundaries, provided they pay themselves, while under one in ten were open to fully autonomous purchases. Deloitte's survey of European shoppers found willingness to delegate comparison and price-finding at roughly a quarter to a third, falling to single digits at checkout. Research from Checkout.com found about a quarter of consumers say they will never delegate purchases to AI.
The platforms have adjusted too. OpenAI's in-chat Instant Checkout has not become the centre of its commerce strategy, while advertising in ChatGPT has expanded to more markets, aimed at the moments when people are researching and comparing. The practical reading for 2026: agents increasingly shape what people choose, while people still largely decide whether and when to pay.
Think of it as a delegation dial
Agentic commerce is not a switch that flips from "human shopping" to "machine shopping". It is a dial that each customer sets differently for each kind of purchase, and each position asks something different of marketing.
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| Dial position | Agent does | Customer keeps | Marketing must win |
|---|---|---|---|
| Research help | Explains, summarises, compares | The whole decision | Accurate, cited information |
| Shortlist | Narrows the options | The final choice | A place on the shortlist |
| Bounded choice | Picks within budget and preferences | Approval and payment | The brief, and the evaluation |
| Autonomous | Chooses and pays | Only the original instruction | Default status and the relationship |
Delegation happens category by category
The same customer will hand over some purchases entirely and keep tight control of others. Research consistently finds delegation most acceptable for routine, low-risk, frequently repurchased items, with groceries and household supplies at the top, and least acceptable where purchases carry personal meaning or high stakes. That makes it possible to estimate how exposed each part of your range is.
Score each product category from 1 to 3 on three questions, then add them up:
→ Repetition: how often is it bought again? (1 = rarely, 3 = routinely)
→ Low risk: how little can go wrong? (1 = costly mistakes, 3 = trivial)
→ Low personal meaning: how little does the choice express who the customer is? (1 = very personal, 3 = purely functional)
Examples:
→ Laundry detergent: 3 + 3 + 3 = 9, highly exposed to full delegation.
→ Running shoes: 2 + 2 + 1 = 5, likely bounded choice, with the customer naming brands.
→ An engagement ring: 1 + 1 + 1 = 3, human-led, with agents helping only with research.
Scores of 7 to 9 signal categories where defaults, price and availability will decide more and more; scores of 3 to 5 signal categories where brand and experience stay central.
High-exposure categories are where the discipline in replenishment reminders and easy reordering becomes a competitive defence, because being the customer's default is worth more than winning any single comparison.
The shift When agents join the buying process, marketing's job moves to either side of the transaction: getting named in the brief the customer gives the agent, and owning the relationship after the sale. The middle, where so much conversion optimisation lives today, matters less than it used to.
Before the transaction: win the brief
The most important marketing moment in a delegated purchase happens before the agent starts working, when the customer tells it what they want. Accenture found that more than half of consumers would tell their agent which brands to consider. A brand named in that instruction starts with an enormous advantage; a brand that isn't named has to be discovered and preferred by the agent on facts alone.
That makes brand familiarity and preference more valuable, not less, in categories with any personal element. It is built the slow way: memorable experiences, genuine recommendations, consistent presence. The agent era does not replace brand building; it raises the stakes of having done it.
During evaluation: be legible and verifiable
When an agent weighs options, it works from information it can read and check. Accurate, structured product data, clear specifications, honest pricing and consistent facts across your site and the wider web all matter, which is the groundwork described in brand entity optimization and keeping AI accurate about your brand. Independent proof counts as well, because agents lean on reviews and third-party sources to judge claims, as covered in earning third-party mentions.
What loses power is persuasion aimed at human impulses. Countdown timers, "only three left" messages, anchoring against an inflated "was" price and other urgency tactics carry little weight with an agent comparing facts against a customer's stated criteria. Some will even count against you if they conflict with verifiable data. The honest levers, genuine value, fit and reliability, are the ones that survive. When a human does click through to check, the page they land on still needs to confirm what they were told, as covered in landing pages for visitors sent by AI.
After the transaction: own the relationship
An agent that chose you once can switch just as easily next time. Accenture found that more than a third of loyal consumers would let an agent move them to a better-fitting alternative, and Braze reported that most marketing leaders feel agents have already weakened their direct connection with customers. Loyalty in the agent era is conditional: earned on fit and value each time, and protected by owning the relationship after the sale.
That means investing in what agents can't intermediate: direct communication, service, community and genuine reasons to stay. First-party data becomes more strategic, as argued in building a first-party data strategy, and loyalty benefits that customers actually value, as covered in loyalty programme design, can become something customers explicitly tell their agent to protect.
Paid media follows the agent
Where attention goes, advertising follows. Paid placements inside AI assistants are expanding, aimed at the research and comparison moments where decisions now start. Expect this to become a significant channel, with its own formats, disclosure rules and measurement questions. It will reward brands whose products stand up to comparison, since a paid mention that an agent then scores poorly against the customer's criteria is money wasted. Your own marketing operations are changing in parallel, as covered in agentic AI in campaign execution, so the same team may soon be marketing to agents while using agents to do it.
What to do now
You don't need to rebuild everything for a future that is arriving unevenly. Score your categories for delegation exposure. Make product information complete, accurate and consistent everywhere. Build genuine brand preference in categories where customers will name brands. Strengthen reordering, replenishment and loyalty where delegation is highest. And invest in direct relationships that don't depend on any intermediary. Brands that want help connecting these pieces across catalogue, retention and paid channels often work with a specialist e-commerce marketing team. For how all of this fits into the wider shift, see our plain-English guide to AI search optimization.
The short version
In 2026, AI agents mostly do the choosing, within limits customers set, rather than the buying. Surveys show strong enthusiasm for AI help with research and comparison but a sharp drop at payment: around a third of people would let an agent decide within boundaries while paying themselves, fewer than one in ten welcome fully autonomous purchases, and about a quarter say they will never delegate. Think of it as a delegation dial set differently for each purchase, with routine, low-risk, functional categories most exposed and personal, high-stakes ones staying human-led. Marketing's job moves to either side of the transaction. Before it, win the brief: many customers name brands for their agent to consider, so brand preference matters more. During evaluation, be legible and verifiable, because urgency tricks lose power against an agent weighing facts. After it, own the relationship through first-party data, service, loyalty and easy reordering, since agents can switch as easily as they choose. Paid placement is following attention into AI assistants.
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Explore E-commerce Marketing →Frequently asked questions
What is agentic commerce?
Agentic commerce describes AI agents acting on a shopper's behalf across part or all of a purchase: researching options, comparing products and prices, building a shortlist, choosing within limits the shopper sets, and in some cases completing the checkout. It differs from earlier AI shopping assistants, which mainly answered questions, because an agent takes actions rather than just giving advice. In practice it is best thought of as a dial rather than a switch. At one end the agent simply helps with research; further along it shortlists and recommends; further still it chooses within a budget and preferences while the customer approves and pays; and at the far end it buys autonomously. In 2026 most real activity sits in the research, shortlisting and bounded-choice positions, while fully autonomous purchasing remains a small minority, concentrated in routine, low-risk categories such as groceries and household supplies.
Are consumers actually letting AI agents buy for them in 2026?
Mostly not yet, at least not all the way to payment. Consumers are enthusiastic about AI help with research and comparison, but willingness drops sharply at checkout. Accenture's survey of more than 25,000 people across 16 countries found that about a third would let an agent decide what to buy within set boundaries provided they make the payment themselves, while under one in ten were open to fully autonomous purchases. Deloitte's survey of European consumers similarly found willingness to delegate comparison and price-finding in the range of roughly a quarter to a third, falling to single digits at checkout. Research from Checkout.com found that about a quarter of consumers say they will never delegate purchases to AI, with delegation most acceptable for groceries and household supplies. The practical picture for 2026 is that agents increasingly shape what people choose, while people still largely decide when and whether to pay.
How should marketing change for AI agent shoppers?
Marketing's job moves to either side of the transaction. Before it, the goal is to be named in the brief a customer gives their agent, because many people tell their agent which brands to consider, so brand familiarity and preference built in advance matter more rather than less. During evaluation, the goal is to be easy for a machine to understand and verify: accurate, structured product information, consistent facts across the web, and credible independent proof such as reviews and third-party coverage. Classic on-page persuasion, such as urgency messages, countdown timers and price anchoring, has little effect on an agent weighing facts against a customer's criteria. After the purchase, the goal is to own the relationship directly through first-party data, service, loyalty and replenishment, since an agent that chose you once can switch just as easily next time. Paid placement is also emerging on AI platforms, so advertising will increasingly reach people at the research moment rather than only on websites.
Will brands matter less if AI agents do the shopping?
Probably the opposite for many categories, although the way brands matter changes. Surveys suggest that a majority of consumers would tell their agent which brands to consider, which makes being on that personal shortlist a powerful advantage that is built long before any agent gets involved. At the same time, loyalty becomes more conditional: a meaningful share of even loyal customers say they would let an agent switch them to a better-fitting alternative, so brand preference alone will not protect a product that is weaker on the things the customer asked the agent to optimise for. The categories most at risk of becoming pure comparison contests are routine, low-risk, frequently repurchased items where the customer cares mainly about price, availability and specification. Categories with high personal meaning, such as fashion, gifts and identity-driven purchases, are likely to stay much more human-led, with agents helping to research rather than decide.
