AI Is Turning Marketplaces Into Decision Engines
The folks at ChannelEngine have just shipped their annual Marketplace Shopping Behavior Report 2026. It is framed as a consumer research paper, but in reality, it is an early warning system. Buried in the back half of the report is a much more important signal: the market is already shifting from human-led browsing toward AI-mediated decision-making.
AI is not just improving discovery or comparison. It is reshaping the unit of competition itself. And that shift favours whoever can make value measurable, legible, and trusted at the moment a decision is made. For CEOs of seller businesses and the agencies that support them, this is no longer a future scenario. It is already visible in margin pressure, rising dependence on sponsored placement, and increasing difficulty explaining why conversion rises or falls.
At first glance, the report confirms familiar behaviours:
53% of shoppers always or often compare the same product across multiple marketplaces
Shoppers browse an average of three marketplaces before buying
95% notice price differences across platforms
Reviews, ratings, delivery speed, and free shipping dominate purchase decisions
But the deeper signal is how shoppers are making those decisions. Shoppers are no longer browsing for inspiration. They are filtering, validating, and eliminating. They rely on proxies. They shortcut evaluation. They delegate judgement because the environment has become too complex to assess manually. That behavioural shift is the prerequisite for AI delegation.
AI has already entered the buying workflow
The report is explicit on this point. Over 58% of shoppers have already used AI tools to research products, with ChatGPT used by one-third of all respondents, ahead of Google’s AI tools (18%) and Gemini (15%). Some shoppers are also using AI directly inside marketplaces, such as Amazon Rufus, or virtual try-on and styling tools.
This matters because AI is no longer peripheral to the journey. It is already:
Replacing early marketplace browsing
Summarising options
Narrowing shortlists
Framing comparisons
The report describes AI as an “emerging co-shopper.” That language is conservative. The behaviour is not.
From co-shopper to delegated decision-maker
While AI-led checkout is still early, the trajectory is clear. Nearly half of shoppers (49%) say they would either buy or consider buying directly through an AI assistant, rather than going to a marketplace or brand site. 17% say they are already comfortable doing so, and a further 32% say “maybe, for some products.”
This is not theoretical acceptance. It is conditional trust. Shoppers are signalling that they are willing to hand over authority once AI:
Feels reliable
Produces consistent outcomes
Reduces effort
Minimises regret
This mirrors every previous automation wave. Humans retain control until delegation consistently outperforms manual decision-making. Then control transfers quickly.
What changes when AI makes the decision
When AI agents move from assisting to deciding, three things become non-negotiable.
1. Value must be machine-legible
AI does not respond to brand storytelling or vague differentiation. It optimises for:
Proven outcomes
Predictability
Risk reduction
Historical performance
Total cost certainty
If your value only exists in human interpretation, it will not survive agent selection. This aligns directly with what we see today: shoppers already default to signals that reduce uncertainty fastest, such as reviews, ratings, delivery reliability, and verified seller identity.
2. Trust signals become decision inputs
The report outline what we already know are key trust signals:
83% of shoppers rely on star ratings
81% rely on verified buyer reviews
60% hesitate to buy if reviews are missing
51% struggle to choose between products that look too similar
These are not “nice to have” signals. They are elimination criteria. For AI agents, they become weighted inputs. Weak, inconsistent, or missing signals do not reduce conversion. They remove you from consideration entirely.
3. Marketplaces become decision infrastructure
As AI agents sit between shoppers and sellers, marketplaces evolve from storefronts into decision systems. Their power shifts toward:
Defining trust standards
Enforcing data consistency
Structuring performance signals
Controlling recommendation logic
The report already shows shoppers find it helpful when marketplaces recommend top sellers. AI amplifies this leverage. Marketplaces that control signal quality and structure will sit directly inside the value exchange.
For sellers, AI introduces a painful asymmetry. Buyers gain clarity. Sellers lose visibility into causality. As AI accelerates comparison and parity:
Price pressure increases
Sponsored placement becomes harder to avoid
Conversion becomes more volatile
Differentiation erodes
Many sellers respond tactically: more spend, better listings, more tools. But this treats symptoms, not the cause. The cause is simple: most sellers cannot clearly identify, measure, and expose the value they actually deliver in a way the system rewards.
Reframing for the value economy
This is where the value economy lens matters. AI does not eliminate competition. It raises the standard for participation, forcing value to be identified, measured, exposed, and justified continuously. Winning sellers will be those who can:
Identify value What do customers actually choose you for in each marketplace?
Measure it Conversion drivers, delivery reliability, review coverage, price competitiveness.
Expose it Make value legible at the decision moment, for humans and machines.
Monetise it Use sponsored visibility to enter the shortlist, not to compensate for weak fundamentals.
The strategic question for sellers is no longer: “Are we using AI?” It is: “Can an AI decision system clearly justify choosing us?”
For decades, good commercial strategy meant one thing: think like the customer Understand their needs. Reduce friction. Influence perception. Win the moment. That logic still matters, but it is no longer sufficient. As AI moves from assisting shopping to actively shaping and then making buying decisions, the frame has to change. Increasingly, you are not selling to a person first. You are selling to a decision system acting on their behalf.
LLMs do not browse, feel, or get persuaded. They evaluate. They filter. They optimise. They must be able to explain their choice. If you want to stay competitive in an AI-mediated marketplace, you need to think like the system that will increasingly decide whether you make the shortlist. Here are five things to consider about how LLMs actually evaluate options, and what that means for sellers.
1. Measurable advantage beats persuasive claims
An LLM can only compare and weight what is explicit. If your differentiation cannot be expressed as two or three hard numbers, it effectively does not exist to a decision system. In practice, this means choosing a small set of metrics that genuinely explain why customers choose you, such as star rating, delivery reliability, return rate, or total landed cost, and ensuring those numbers are visible, consistent, and current across marketplaces. Language like “better quality” or “great service” adds nothing unless it is backed by evidence that can be compared.
2. Clarity at the decision moment matters more than brand depth
LLMs prioritise what is immediately visible and unambiguous. They do not infer hidden value or dig through narrative unless explicitly instructed. For sellers, this means auditing listings so the primary reason to choose you is obvious without scrolling, interpreting, or cross-referencing. If your value requires explanation, context, or brand familiarity, it will be underweighted or missed entirely when decisions are automated.
3. Consistency signals safety, inconsistency signals risk
LLMs are fundamentally risk-minimising systems, and variance increases uncertainty. When pricing, reviews, delivery promises, or policies vary significantly across marketplaces, ranking confidence drops. In practice, this means eliminating unnecessary variance before chasing outperformance: aligning pricing logic, standardising fulfilment promises, and ensuring trust signals do not diverge dramatically by channel. Being reliably good everywhere matters more than being excellent in isolated pockets.
4. Weak signals trigger exclusion, not negotiation
Decision systems filter options before they rank them. Missing reviews, unclear pricing, vague fulfilment terms, or ambiguous seller identity often cause early elimination rather than a lower position. For sellers, this means treating trust thresholds as entry requirements, not optimisation levers. Ads, optimisation, and growth tactics only make sense once minimum levels of proof, clarity, and credibility are consistently met.
5. If the system cannot explain the choice, it will not make it
LLMs are designed to justify their recommendations. If they cannot construct a clean rationale from available signals, they hedge or default to a safer alternative. Practically, this means being able to articulate, in plain language and backed by metrics, why a system should choose you over the next best option, and ensuring that logic is reflected consistently in listings, pricing, delivery promises, and performance data. If your own team cannot explain why you win without slides or caveats, an AI cannot either.
The new mental model
If you want to make this real, don’t overthink it. Take one of your live marketplace listings, copy it exactly as it appears, and paste it into your preferred AI tool. Then ask the AI to evaluate the listing as if it were a shopping assistant deciding what to recommend, not as a marketer reviewing copy.
Specifically, ask it to assess whether your value is measurable, whether the reason to choose you is immediately clear, whether your signals look consistent and low-risk, whether anything would disqualify you early, and whether it can clearly justify choosing you over similar options. Pay attention not just to the verdict, but to where the AI hesitates, hedges, or asks for more information. Those gaps are where human buyers already feel friction, and where AI-driven decisions will increasingly exclude you.
You don’t need perfect answers. You just need to see how legible your value really is when persuasion is removed. If an AI struggles to explain why you should be chosen, a future decision system will struggle too.
In the old world, success came from influencing perception. In the AI-mediated world, success comes from passing evaluation. Marketplaces are becoming decision engines. AI assistants are entering the buying loop. Value is being judged continuously, comparatively, and without sentiment. The leaders who adapt will be those who stop asking: “How do we look to customers?” And start asking: “Would a decision system choose us, and could it defend that choice?” If the answer is no, the decision will still be made. Just not in your favour.
