The new rules of relevance in AI-assisted shopping

Consumers are embracing AI-assisted shopping faster than many marketers realize. Horizon Futures research found that 56% of AI-familiar consumers already use AI daily, while 82% use AI for shopping research and comparison.¹ At the same time, shoppers are increasingly outsourcing cognitive load to AI-powered shopping tools, like Alexa for Shopping, for price tracking, product discovery, and deal hunting.

In fact, 70% of respondents were comfortable letting AI handle deal hunting, and 64% trusted AI for product comparison.² As AI increasingly shapes discovery and purchase decisions, the brands that succeed will be the ones who build systems of trust and relevance to help shape consumer preference before AI recommendation moments occur.

This is where the convergence of streaming media, shopping signals, and AI-powered optimization becomes strategically important.

Build measurement frameworks that optimize for trust, not just attribution

One of the clearest findings in Horizon’s research was that consumers care less about how AI tools work than whether they maintain control when something goes wrong. “Veto control,” easy returns, transparent decisioning, and access to human support ranked among the strongest trust drivers in AI-assisted shopping environments.

For advertisers, that changes how measurement should operate. Brands need visibility into how audiences move across multiple touchpoints, evaluating lead-in and halo effects surrounding touchpoints like premium streaming exposure to understand which signals build the trust and familiarity that AI recommendations reward.

Amazon Marketing Cloud (AMC) makes this possible, enabling advertisers to analyze engagement patterns, overlap across audiences, and conversions in privacy-safe ways that prioritize consumer trust while still delivering actionable insights.

In AI-assisted shopping environments, trust itself becomes a performance variable.

Build brand salience before AI recommendation moments occur

Horizon’s research found that AI shoppers are becoming “optimizers, not browsers.” Consumers increasingly expect AI to narrow choices, compare options, and surface the most relevant products quickly.

That means discovery is shifting upstream. For advertisers, this elevates the role of streaming TV and premium interactive media from awareness channels to relevance-building touchpoints.

The Amazon Ads authenticated graph connects these signals, linking streaming engagement to actual shopping behaviors through trusted relationships across households. Amazon DSP and streaming TV ads create opportunities to connect premium content exposure with measurable commerce outcomes across the consumer journey. Brands using streaming TV alongside interactive formats can move audiences from passive viewing into active engagement environments where content, commerce, and participation reinforce each other.

Practically, that means advertisers should sequence media differently. Streaming TV should establish category-level salience and emotional familiarity before high-intent retail moments emerge. Remarketing through Amazon DSP can then build on streaming engagement signals to drive more efficient downstream action.

We are also seeing more brands use interactive video and creator-led environments to generate stronger engagement signals before transactional moments occur. Interactive formats that invite participation rather than passive consumption often produce richer behavioral indicators that can later strengthen recommendation relevance and conversion efficiency.

In AI-assisted commerce, discovery increasingly happens before the search bar.

Use AI optimization to accelerate execution, not outsource strategy

AI-powered campaign optimization tools are becoming essential operational infrastructure for modern marketers. But automation alone does not create competitive advantage.

In fact, one of the emerging risks in AI-mediated advertising is strategic convergence. If every brand relies on the same optimization signals, audience models, and automated recommendations, differentiation erodes quickly.

The advantage will come from combining machine efficiency with human strategic oversight.

Amazon Ads solutions can help advertisers accelerate media optimization, improve responsiveness, and identify emerging performance patterns faster than manual workflows alone. But high-performing brands are establishing clear governance frameworks around how automation is deployed. That includes creating human review checkpoints for creative optimization, separating short-term ROAS metrics from long-term brand growth indicators, and continuously testing incrementality rather than relying solely on automated recommendations.

This balance matters because consumer expectations around AI remain nuanced. Horizon’s research found that shoppers are significantly more comfortable with AI-assisted research than fully autonomous purchasing decisions. The same principle applies to marketing solutions. AI performs best when it enhances human decision-making, not when it replaces strategic judgment entirely.

The future is not autonomous marketing. It is strategically supervised automation.

What brands should operationalize over the next 18–36 months

Over the next several years, the brands best positioned for the rise in AI-assisted shopping will likely share a few characteristics.

First, they will integrate brand and performance strategies more tightly. As the buyer journey has become less linear and AI tools are shaping discovery earlier in the journey, the historical separation between upper-funnel media and commerce activation becomes less useful.

Second, they will invest in connected measurement solutions that connect signals across streaming and shopping. AI-assisted commerce requires continuity across environments, not isolated channel reporting.

Third, they will design creative and shopping experiences that preserve consumer agency. Horizon’s research consistently showed that consumers want assistance more than delegation. Brands that reinforce transparency, reassurance, and reversibility will be better positioned to maintain trust as automation scales.

Finally, marketers should begin testing now. The research suggests that AI assistance will scale faster than full autonomy over the next 18–36 months, particularly in lower-friction, repeat-purchase categories. Brands that use this period to strengthen their measurement frameworks, streaming strategies, and AI readiness will be better prepared as consumer behaviors continue evolving.

The brands that succeed in AI-assisted commerce will not simply automate transactions. They will build systems of trust, relevance, and reassurance that remain influential before, during, and after AI-mediated decisions.

Sources

^1-2 Horizon Futures custom research. Agentic Commerce. Fielded March 12–19, 2026. Data reflects U.S. N=1,001.

About the author

Katie Comerford is President of Horizon Commerce and Client Transformation, where she leads enterprise data strategy, client transformation initiatives, and Horizon Commerce operations. A marketing and media executive with nearly two decades of experience, she focuses on integrating marketing intelligence with Horizon’s Client Architect model—aligning technology, data, and talent to drive measurable business outcomes and accelerate growth. She has been with Horizon for more than 11 years, previously serving as EVP, Chief Strategy & Operations Officer at Horizon Next, where she helped shape integrated solutions across media, data, and technology.