What does it take for consumers to delegate to AI?
What does it take for consumers to delegate to AI?
The greatest barrier to the next era of AI isn't technological capability; it's psychological friction. Consumers will gladly use AI to search and compare, but the moment real money or data is on the line, they demand explicit safeguards before they will delegate the task.
While nearly half of online consumers now routinely use AI for search and discovery, 42% explicitly state they would not trust an AI agent to execute transactions on their behalf (Oliver Wyman, 2026). Recent global commerce data from Checkout.com shows one in four consumers (24%) say they will never let AI handle a purchase, and 27% won't trust any organisation to run a shopping agent unless clear safeguards are in place.
This hesitation creates an immediate adoption bottleneck: consumers willingly delegate low-risk, routine queries, but pull back when an AI attempts to book travel, submit sensitive personal data, or process payments (Worldpay, 2025). When brands deploy autonomous workflows without designing for this psychological boundary, consumer journeys stall at the point of commitment. That's why Gartner predicts that over 40% of enterprise agentic AI initiatives will be cancelled or scaled back by 2027.
Treating trust as a single metric or a technical software requirement is a mistake. To design successful agentic experiences, brands need to understand the factors that encourage consumers to delegate tasks.
The delegation spectrum
Comfort with AI varies depending on what the technology is being asked to do. Trust exists on a continuum defined by autonomy, consequence and risk:
AI as information source (low concern):
Users turn to AI when they have a rough idea of what they want but lack the time to compare options across multiple sites, letting it turn endless choices into a short list. The main requirement here is basic accuracy and clear sourcing.
AI as decision aid (greater caution):
The stakes rise when AI begins ranking, curating, or narrowing choices. Users become alert to potential bias, worrying whether recommendations are genuinely tailored or quietly driven by platform preferences, hidden commercial deals, or algorithmic bias.
AI as autonomous agent (highest concern):
When AI performs tasks directly, such as pre-filling personal data, managing bookings, or submitting payments, the cost of a mistake escalates. At this end of the spectrum, trust drops sharply if users feel they are relinquishing control over consequential or hard-to-reverse actions.
This spectrum aligns with MTM and Google’s recent consumer insights on AI-assisted shopping. These show that shoppers will readily adopt AI to cut through noise and discover products but that their expectations around transparency and control spike the moment an AI influences an actual purchase decision.
The commercial implication for brands is clear: the higher the autonomy of the task, the greater the safeguards required to encourage adoption.
The AI confidence gap
Trust in AI is a two-way street. It hinges on the task at hand but also on how confident consumers feel using the tool themselves. MTM’s research shows that many consumers worry a vague instruction or a lack of technical know-how will lead the AI into a costly, unrecoverable mistake.
In behavioural terms: capable AI + uncertain user = journey abandonment.
This reshapes the design challenge. Adoption depends less on building a smarter model and more on making users feel competent, informed and in control. An interface that demands technical know-how, or expects people to know exactly what to ask, will only ever reach the most tech-savvy users.
Safeguards for agentic journeys
When AI moves from giving advice to taking action, brand equity relies on specific interaction design choices that reduce user anxiety. We find that the most trusted agentic experiences follow four simple rules:
Separate "working" from "committing" -
Let the AI search, compare and prepare freely, but always ask for a clear yes before it pays, books or submits anything that can't be undone.
Provide visibility without supervision -
Give people a simple, glanceable update on progress, with the full detail available if they want to check it.
Use progressive permissioning -
Start with small, temporary permissions, and let the AI earn broader access only once it's proven itself.
Build in direct recovery -
When something goes wrong, explain what happened in plain language and let people fix that one step, not start the whole task again.
What brands need to do
As AI increasingly shapes how consumers discover products, weigh up options and complete transactions, product quality alone won't be enough. Brands must also think about the trust architecture of the AI experience.
To evaluate whether your brand's AI experience is building or eroding trust, ask these five strategic questions:
Where does this task sit on the delegation spectrum?
Are you asking users to trust an answer, a recommendation, or an irreversible action?
Does the experience make the user feel competent?
Are you guiding their inputs with clear options and guardrails, or expecting them to know how to prompt effectively?
Is there a distinct circuit breaker before commitment?
Does the system clearly separate task research and form-filling from final authorization?
Is commercial logic transparent?
Can users easily tell why a specific option appeared, where the data originated, and whether results are uncompromised by hidden steering?
How well does the journey recover from failure?
When an error occurs, does the interface explain what went wrong and provide an instant path to fix it?
How MTM can help
Building trustworthy AI experiences is not a guessing game, nor is it an issue that can be solved in a silo by engineering teams. Because trust thresholds vary widely across different categories, tasks, and risk levels, brands must map where user confidence flourishes and where it breaks down.
At MTM, we help brands navigate these shifting dynamics through:
Understanding consumer attitudes and behaviour.
AI adoption is not uniform, and neither is consumer trust in it. We conduct qualitative, quantitative, and tracking research into how consumers in your category use AI in their purchase journeys and flag where friction threatens adoption.
Brand, consumer, and user experiences.
We use established frameworks to help brands integrate AI with human experience in ways that preserve control, agency and confidence: the qualities that define brand preference in the AI era.
AI technology and use case mapping.
The AI landscape is moving fast, and its impact is uneven. We map emerging technologies, identify the agentic use cases most likely to affect decision-making in your category, and make strategic recommendations for your competitive context.
Commercial opportunity.
AI changes the economics of your consumer journey. We assess where it creates threat and opportunity, help build the go-to-market case for where to invest, and connect AI design directly to the outcomes that matter most: conversion, retention and brand trust.
If any of this is live in your organisation, we would welcome the conversation. Get in touch today.
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