Executive Overview

This study shows that AI is no longer a niche research aid in shopping. It is already shaping what people discover, compare, choose, and buy—and the brands that win next will be the ones that are easiest for AI to understand, recommend, and trust.

• AI is already influencing real purchase behavior. Consumers are using AI not just to browse, but to change brands, refine choices, and even alter what goes into the cart in real time.
• The strongest value is decision confidence. People turn to AI to reduce effort, compare tradeoffs, and avoid bad purchases more than for novelty alone.
• Growth is likely, but not automatic. Adoption will expand quickly if experiences feel more personal, transparent, and connected to real shopping contexts.

Top Findings

This summary focuses on the implications that matter most for leaders: where AI is already affecting revenue and choice, where growth is heading next, and what brands should do now.

1. AI is already a meaningful part of shopping behavior

Across this sample, AI is already influencing mainstream shopping behavior rather than sitting at the edges of the funnel. The strongest evidence is not just stated intent, but discovery and in-store behavior change.

The takeaway from this chart is clear: AI has crossed from experimentation into everyday decision support. Roughly 4 in 5 participants say AI has at least a moderate influence on how they shop .

• Discovery is already widespread. Nearly 9 in 10 participants say they discovered new products or brands through AI in the past three months .
• AI is affecting physical shopping too. More than 3 in 4 participants say they have changed what they bought while in-store based on an AI suggestion , reinforcing AI-Assisted Research Connects Online and Offline Shopping.
• Participants describe AI as a practical advisor, not entertainment. It helps them move faster, filter options, and commit to a choice with less uncertainty .

What this means: AI is now part of the path to purchase. For many consumers, brands are not only competing on shelf, site, or search ranking—they are competing inside AI-mediated comparison and recommendation flows.

Recommendation: Treat AI visibility as a growth channel. Make product information structured, comparable, and current across brand sites, retail listings, review ecosystems, and local availability feeds so AI tools can surface your offer accurately.

2. AI is strongest where shoppers need help making tradeoffs

Consumers rely on AI most when a decision feels complex, technical, or overloaded with options. Its value comes from reducing effort and helping people choose with confidence.

This pattern matters strategically: AI is most powerful in the parts of the journey where consumers struggle on their own. Comparison, research, discovery, and personalization all rank at the top .

• Comparison and research dominate. More than 9 in 10 participants link AI to comparing options and gathering information before buying .
• The strongest stories come from high-consideration purchases. Participants used AI to narrow running shoes based on biomechanics , find the right Airbnb among hundreds of options , and switch monitor choices after feature-level comparison .
• This extends across categories. The same behavior appears in travel, electronics, apparel, personal care, and food, matching AI-Driven Product Comparisons Influence Purchases.

What this means: AI is increasingly the decision engine for categories with friction—too many options, unclear tradeoffs, or high perceived risk. When brand differences are easy for AI to explain, conversion becomes easier.

Recommendation: Prioritize comparison-ready content. Give AI and shoppers clear specifications, fit guidance, tradeoff explanations, and review-backed proof points that make your product easy to recommend for a specific use case.

3. AI is expanding consideration sets and weakening default brand advantage

AI is not only helping shoppers pick among known options. It is also bringing unfamiliar brands into the decision set and making those alternatives credible faster.

The strategic implication is significant: discovery is no longer controlled only by paid media, shelf placement, or search results. AI is now introducing brands directly into consumer consideration .

• New brand discovery is mainstream. Nearly 9 in 10 participants say AI helped them discover new products or brands recently .
• Participants often bought what AI surfaced. One participant bought a shoe brand they had never heard of after AI recommended it ; another started considering LG for a washer-dryer only after AI highlighted the brand’s features .
• Discovery is tied to perceived objectivity. Several participants explicitly trusted AI more than retailer sites or sales staff when comparing alternatives .

What this means: Brand incumbency is less secure when AI can explain why a lesser-known alternative is a better fit. Challenger brands have more opportunity, while established brands need stronger comparative proof to hold preference.

Recommendation: Optimize for AI-led discovery. Strengthen distinctive claims, verified reviews, use-case language, and structured feature data so your brand is easy to retrieve and justify against better-known competitors.

4. Consumers expect AI to play a bigger role next year

Current behavior is only the base case. Most participants expect AI to take on a larger role in shopping over the next 12 months, with many expecting it to become central.

This is a near-term growth signal, not a distant one: only a very small minority expect no growth. About 9 in 10 participants expect AI to play a bigger role in shopping next year, and 6 in 10 expect it to become central or involved in almost every purchase .

• Consumers want broader integration. They describe AI as something that should work across online and offline shopping, not in isolated moments .
• Growth will come from utility, not novelty. Participants expect more use when AI saves time, narrows options, and improves confidence rather than simply generating ideas .
• In-store use is part of that future. Real-time use cases already exist, such as scanning the shelf and switching products on the spot .

What this means: AI-enabled shopping will likely deepen before it fully mainstreams. The brands that build capability now will be better positioned when AI becomes a standard layer in consumer decision-making.

Recommendation: Build a staged AI commerce roadmap now. Focus first on high-friction categories and high-value decisions, then extend into everyday replenishment, store support, and loyalty use cases.

5. Better personalization and transparency are the main unlocks for adoption

Consumers are not asking for more AI in the abstract. They want AI that understands them better, explains itself clearly, and earns trust over time.

The message from this chart is practical: the biggest unmet need is not entertainment or automation first. It is better decision support—choosing the right item, comparing tradeoffs clearly, saving time, personalizing guidance, and explaining why .

• Consumers want AI to know them. Participants consistently ask for AI to learn preferences over time and recommend what fits them rather than what is merely popular , echoing Consumers Desire Personalized AI Recommendations.
• Trust still has limits. People value AI’s objectivity, but they also worry about hallucinations, bias, and opaque commercial influence .
• Transparency is part of the product. Consumers want to understand why something is recommended, whether it reflects their goals, and whether a brand or platform is shaping the answer , aligning with Generic AI Responses Erode Consumer Trust and Real-Time, Local Integration Needed in AI Shopping Tools.

What this means: The next competitive advantage will come from AI experiences that feel both smarter and safer. Personalization without trust will feel intrusive; transparency without relevance will feel generic.

Recommendation: Invest in three capability areas: persistent preference memory, explainable recommendations with visible sources or tradeoffs, and real-world integration such as stock, location, and price accuracy.

Methodology Note

This study examined how AI platforms influence shopping and buying behavior across categories including food, apparel, electronics, travel, wellness, household products, entertainment, and services. It used in-depth AI-moderated interviews structured around three core objectives: understanding everyday AI entry points, mapping AI’s influence on purchase decisions and cart content, and identifying the jobs, frictions, and unmet needs consumers have for AI in shopping. The study completed 323 interviews out of 350 started, with an average interview length of 30 minutes.