NIQ's AI Shopping Survey Puts Influence at 55% and Recent Use at 27%

NIQ’s October 7 release puts 55% and 27% beside the same AI shopping story. Consumer brands can use those figures to scope a discovery test, provided the brief preserves what each percentage measures.

In the Consumer Outlook: Guide to 2027 announcement, 55% of global consumers say AI recommendations at least sometimes influence their household and grocery purchases. Separately, 27% actively used AI-powered tools or assistants to research or decide what to buy in the preceding three months.

One captures reported influence. The other captures reported recent use. Neither establishes how many shoppers authorize software to complete a transaction.

Lauren Fernandes, NIQ’s Vice President of Global Content, describes products competing for attention before purchase:

“Products increasingly compete for visibility, trust, and recommendation”

The company’s survey covers more than 21,000 consumers in 31 countries. The announcement combines survey findings with broader retail purchase measurement, but these two AI percentages are self-reports. Fieldwork dates, full questionnaire wording, and weighting aren’t supplied in the release. The linked report destination wasn’t accessible during our review.

That leaves a useful planning signal with a limited job. A grocery brand has grounds to investigate how customers encounter AI recommendations. A B2B software marketer gets a transferable discipline: keep the behavior, audience, and time window attached to every adoption figure before it enters a budget presentation.

Our recent coverage of AI search and agent trust examined another survey’s definitions. This new report adds a different global, grocery-related measure. The samples and questions don’t form a comparable trend line.

The measurement changes the assignment

An influence question can include a shopper who recognizes a recommendation’s effect without having deliberately opened an assistant to research a purchase. An active-use question asks the respondent to recall an action within a stated period. These descriptions explain why the two measures can differ; the release doesn’t provide respondent-level data showing how the groups overlap.

Subtracting the percentages wouldn’t produce a count of shoppers who abandoned an AI buying journey. Treating either figure as the share of revenue AI caused would require evidence the survey doesn’t offer.

The work follows from the distinction. A discovery team can examine whether generated answers describe the product accurately. An analytics team can track visits and outcomes where the path is observable. A commerce team needs its own evidence that customers want a delegated transaction. Each assignment needs a separate success measure.

Amanda Natividad, Chief Evangelist at SparkToro, offered a useful question in her June 23 essay about AI visibility reports:

“are we being described correctly?”

Her essay concerns a review of SparkToro’s representation in an AI visibility tool, rather than NIQ’s October findings. It discloses HubSpot sponsorship. The question still gives an operator a practical starting point: inspect the description before making the score a target. A mention attached to the wrong category or an inaccurate product claim needs a different response from a missing mention.

Natividad also argued in April that the channel analytics credits with a visit can differ from the places that created demand. That earlier commentary supports checking the limits of a traffic report; it supplies no estimate of AI’s contribution to grocery sales.

A dashboard needs more than an adoption headline

The platform reporting has limits of its own. Google’s documentation says appearances in AI Overviews and AI Mode sit within overall Search Console traffic under the Web search type. It recommends tracking conversions and time on site with other tools, including Google Analytics.

That reporting arrangement gives a marketer several observations with different meanings. A page’s search traffic, a recorded purchase, and a shopper’s stated AI influence can inform the same investigation. They don’t become interchangeable because a dashboard places them side by side.

There is a sensible argument for starting before attribution is complete. Accurate product information helps customers evaluate an offer, and checking what an answer says can reveal errors worth fixing. Google explicitly says existing search practices remain relevant to its AI features. It recommends keeping important content in text, structured information consistent with visible content, and Merchant Center information current. Eligibility still doesn’t guarantee inclusion.

For a smaller company, that supports a bounded review of an important product or comparison page. It doesn’t establish a business case for shifting a fixed percentage of the media budget. Our coverage of product feeds in agent commerce examines the additional work required when the software moves closer to a purchase.

NIQ also sells Cadence, the intelligence platform promoted alongside the findings. That commercial interest belongs with the recommendation for continuous measurement.

Before these numbers reach a planning deck, give them three checks:

1. Keep the question with the figure. Label reported influence and recent active research separately, including the audience and time window.
2. Choose a result your team can observe. For an answer-accuracy test, record the claim and its supporting source. For a website experiment, record visits and completed actions separately.
3. Require a purchase measure for a purchase claim. Put the evidence needed to justify a checkout investment in its own row, and leave that row unfilled until it’s measured.