AI Product Discovery Now Shapes 55% of Consumer Purchases

More than half of consumers worldwide now let artificial intelligence shape what they buy. According to NielsenIQ's newly released Consumer Outlook: Guide to 2027, 55% of global consumers report that AI recommendations at least sometimes influence their household and grocery purchases, and that number is only accelerating. For Fortune 500 marketing leaders, this single statistic represents the fastest and least visible disruption to the customer journey in a generation.

Consumer trends expert Matt Britton has spent the past two decades tracking exactly this kind of behavioral inflection point, first with Gen Z and millennial culture, now with the rise of agentic commerce. He argues that the real headline isn't AI adoption itself. It's the widening gap between how fast shopper behavior is shifting and how slowly most insights teams are rebuilding the infrastructure needed to track it.

More than a quarter (27%) have actively used AI-powered tools or assistants to research or decide what to buy in the past three months , according to the NIQ findings. Meanwhile, a separate NIQ Agentic Commerce Tracker found that 51% of U.S. consumers report using at least one AI tool for shopping in the past month, marking a tipping point in purchase behavior . These are not early-adopter numbers. They describe a mainstream behavioral shift that most Fortune 500 measurement systems were never built to capture.

At the same time, the media side of the business is moving just as fast. Industry estimates now put algorithmic buying driving 70% to 80% of advertising spend, increasing the need for continuous measurement and optimization . Gartner has gone further, forecasting that over 70% of global ad spend will pass through AI-driven self-service ad platforms by 2028, up from 50% in 2025 . CMOs are now buying media and losing customers to algorithms they neither built nor fully understand.

Matt Britton calls this the defining consumer trends story of 2026: a moment when brand visibility into the purchase decision is eroding in real time, while legacy insights infrastructure remains built for a linear, pre-AI customer journey. This post unpacks what's actually happening inside that gap, why it matters more than the adoption headline, and what business leaders need to do before the next earnings cycle.

What Does "AI Product Discovery" Actually Mean for Consumer Behavior?

AI product discovery describes the growing share of purchase decisions where an algorithm, assistant, or recommendation engine shapes what a consumer sees, compares, or ultimately buys before a traditional brand message ever reaches them. This is not limited to chatbots. It spans retail media recommendation engines, voice assistants, AI-powered comparison tools, and increasingly, autonomous shopping agents acting on a consumer's behalf.

NIQ's research frames this shift clearly. The findings underscore the growing complexity of today's consumer journey. Rather than following a predictable path from awareness to purchase, consumers increasingly move between digital and physical channels and draw on multiple sources to inform their choices . A separate NIQ and World Data Lab report found the trend is even more advanced at the top of the funnel, with nearly three-quarters of shoppers now using AI for product discovery, while retail media has grown into a $184 billion global market .

The implication for brand leaders is stark. A NIQ executive summarized it this way in the October 2026 release: the shift isn't that consumers are adopting AI, it's that "AI is beginning to influence what consumers see before traditional brand influence even has a chance to occur" . In practice, that means share of shelf is being replaced by share of algorithm, and most brand marketing budgets still aren't built to compete there.

Matt Britton frequently points audiences to a related finding that reframes what "value" means in an AI-mediated journey. NIQ found that 34% of consumers say quality and performance are the most important factors in determining whether a purchase is worth it, compared with 31% who prioritize affordability . In other words, AI recommendation engines are not simply steering consumers toward the cheapest option. They are increasingly acting as quality filters, which raises the stakes for brands that can't verify how they're being represented inside those systems.

Why Is Algorithmic Advertising Spend Outpacing CMO Measurement Capacity?

The measurement gap is widening because ad buying automation is moving faster than the governance structures meant to oversee it. Gartner's research puts hard numbers behind this concern, warning that as algorithmic buying platforms absorb more of the budget, the more influence AI has over advertising decisions, the more important independent measurement becomes .

The operational risk is twofold. First, advertisers with brand-building, top-of-funnel goals must now be especially vigilant when spending money on opaque, algorithmically driven channels with respect to assessing confidence that a given ad buy is achieving the desired goals . Second, the technical burden of plugging into these systems is rising. Using AI-based platforms usually requires establishing API connections to these platforms, as well as sharing proprietary signal data, which imposes a technical burden on the advertiser while raising important questions around data governance .

This isn't theoretical for most Fortune 500 marketing organizations. NIQ's own survey of CMOs found that 74% of chief marketing officers cited higher demands for return on investment, while only 9% reported access to real-time insights . That 65-point gap between what leadership demands and what insights teams can actually deliver is, in Matt Britton's view, the single most dangerous number in modern marketing. It means most CMOs are being asked to prove performance inside systems they cannot see into clearly.

Britton's keynote work, detailed on his AI keynote presentations page, walks Fortune 500 leadership teams through exactly how this gap forms and what closing it requires operationally. The problem rarely sits with the marketing team's effort. It sits with insights infrastructure that was designed for quarterly reporting cycles, not the real-time, cross-channel behavior AI has introduced.

How Is Agentic Commerce Marketing Changing the Retail Purchase Journey?

Agentic commerce refers to the emerging category of AI systems that don't just recommend products, they take action on a consumer's behalf: comparing prices, narrowing options, and in some cases completing purchases autonomously. NIQ's Agentic Commerce Tracker data shows this is already reshaping day-to-day shopping behavior rather than remaining a futuristic concept.

The tracker found that rather than replacing the shopping experience, AI is compressing decision-making, with respondents reporting using AI to compare options, evaluate value, and narrow choices during the shopping journey . Within that 51% of U.S. adoption, two use cases are leading the way:

• AI-powered product recommendations, now at 20% adoption among U.S. consumers.
• AI-powered personal shopping assistants, following at 16% adoption, reflecting growing demand for personalized guidance and decision support .

This compression effect matters because it shortens the window brands have to influence a decision. When an assistant narrows ten options to three before a consumer ever opens a search engine, traditional funnel marketing arrives too late to matter. Matt Britton's research through the Suzy platform has tracked similar compression patterns across Gen Z and millennial shopping behavior for several years, well before most enterprise insights teams treated it as a board-level priority.

Retail media is accelerating the same dynamic from the supply side. NIQ's broader market data shows retail media has grown into a $184 billion global market , meaning the channels where AI discovery happens are also where ad dollars are concentrating. Brands that fail to understand how their products surface inside these AI-mediated retail environments are effectively invisible at the exact moment the purchase decision is being made.

What Should CMOs in Finance, Real Estate, and Retail Do Differently Right Now?

The companies managing this shift best share one trait: they've stopped treating AI measurement as a side project and started treating it as core marketing infrastructure. Matt Britton advises Fortune 500 clients across finance, real estate, and consumer retail to rebuild insights capability around three principles.

First, continuous intelligence has to replace quarterly reporting. NIQ's own framing of the problem makes this explicit, noting that these shifts are happening faster than many brands can measure, making it essential to move toward always-on tracking rather than periodic snapshots. A quarterly brand tracker cannot capture a purchase journey that compresses in days or hours.

Second, marketing leaders need direct visibility into how algorithmic platforms are representing their products, not just aggregate spend reports. This means negotiating data access and signal-sharing terms with retail media and AI discovery platforms before budgets scale further, not after a competitor already has.

Third, CMOs need to separate AI-driven efficiency gains from genuinely incremental growth. Industry analysis of large advertiser budgets found that only 25-35% of current AI investment is genuinely incremental, while 65-75% is being redirected from existing marketing budgets . Leaders who can't distinguish real growth from budget reallocation will struggle to justify continued investment to their boards.

Matt Britton explores these operational playbooks in depth on The Speed of Culture podcast and in his book Generation AI, both of which examine how organizations rebuild measurement discipline without slowing down enough to fall behind.

Key Takeaways for Business Leaders

• Audit current measurement infrastructure against the pace of AI-driven purchase behavior, not against last year's benchmarks.
• Negotiate data and signal access with retail media and AI discovery platforms before algorithmic ad spend climbs further.
• Separate incremental AI-driven growth from simple budget reallocation to protect board confidence in marketing ROI.
• Prioritize quality and performance signals in product data, since consumers increasingly weigh these over price inside AI recommendation engines.
• Invest in continuous, real-time consumer intelligence rather than quarterly reporting cycles that can't keep pace with agentic commerce.

Frequently Asked Questions

What percentage of consumers use AI for product discovery in 2026?

More than half (55%) of global consumers say AI recommendations at least sometimes influence their household and grocery purchases , according to NielsenIQ's October 2026 Consumer Outlook report. Separately, more than a quarter (27%) have actively used AI-powered tools or assistants to research or decide what to buy in the past three months .

How much advertising spend is now controlled by AI algorithms?

Current estimates show algorithmic buying drives 70% to 80% of advertising spend today. Gartner projects this will keep climbing, forecasting that over 70% of global ad spend will pass through AI-driven self-service ad platforms by 2028, up from 50% in 2025 .

Why are CMOs struggling to measure AI's impact on marketing performance?

A core NIQ finding shows the scale of the gap: 74% of chief marketing officers cited higher demands for return on investment, while only 9% reported access to real-time insights . Legacy measurement systems built for linear customer journeys simply weren't designed to track algorithmic, real-time decision-making.

What is agentic commerce and how does it affect retail marketing?

Agentic commerce refers to AI systems that actively compare, narrow, and sometimes complete purchases on a consumer's behalf rather than just offering suggestions. NIQ's tracker found this is already mainstream behavior, with AI compressing decision-making and narrowing options before brands get a chance to influence the outcome through traditional marketing channels.

The Window to Act Is Closing

The NielsenIQ data makes one thing unmistakable: AI product discovery is no longer an emerging trend, it is the default behavior for a majority of global consumers. Matt Britton's core message to Fortune 500 leadership teams is that the brands winning in 2027 will be the ones who rebuilt their measurement infrastructure now, not the ones with the biggest ad budgets.

This is precisely the gap Britton addresses on stage, translating complex AI and consumer behavior data into clear, board-ready action plans. His keynotes have helped leadership teams across finance, retail, and real estate reframe AI not as a threat to marketing control, but as the next infrastructure investment they can't afford to delay.

Organizations ready to close their own measurement gap can explore Matt Britton's speaker platform to bring this conversation directly to their leadership team. The consumer journey has already changed. The only open question is how quickly marketing organizations choose to catch up.