The path to purchase describes how consumers move from initial awareness to buying a product or service. In 2026, that journey looks nothing like the linear funnel marketers once relied on. Generative AI (genAI) tools, social commerce platforms, and compressed decision cycles are creating new routes to conversion that bypass traditional search-and-browse patterns. This FAQ examines what is driving these shifts and how marketers can adapt.

What is the path to purchase and how has it changed?

The path to purchase is the sequence of touchpoints a consumer encounters between first becoming aware of a product and completing a transaction. Historically, marketers modeled this as a linear funnel: awareness, consideration, decision, purchase. That model no longer reflects how consumers buy. Physical stores still account for approximately 80% of all retail sales, according to EMARKETER, but the discovery and research phases now span AI chatbots, social feeds, retailer apps, and in-store experiences simultaneously. Nearly half (45%) of global consumers use AI during their buying journeys, per a January 2026 IBM-NRF study. The result is a nonlinear, multi-channel process where consumers move fluidly between digital and physical touchpoints.

How is generative AI reshaping the path to purchase?

GenAI is emerging as a parallel discovery channel alongside search engines and social platforms. Shopping-related genAI use grew 35% between February and November 2025, per Boston Consulting Group. Consumers use genAI across categories: 6 in 10 global consumers who have used genAI prompted it to guide consumer electronics purchases, while about half used it for travel (51%), apparel (43%), or entertainment (43%), according to BCG. Rather than browsing product pages, shoppers ask AI tools conversational questions, compressing the research phase into fewer, more targeted interactions. This indicates that AI is not replacing the path to purchase but adding a new, high-intent layer to it.

Why is the traditional purchase funnel compressing?

Three forces are collapsing the gap between discovery and conversion:

• Social commerce integration. Platforms now enable purchase without leaving the app. Some 42% of US consumers plan to make purchases directly on social platforms, according to First Insight's 2025 Holiday Shopping Report cited by EMARKETER.
• AI-enabled buying. Among consumers who plan to use AI for shopping, 68% say they are willing to buy directly within AI tools, per First Insight data. AI compresses awareness and research into a single conversation.
• Creator-driven impulse. Influencer content triggers immediate buying intent. A product recommendation in a social feed can lead to a tap-to-purchase in the same session, with discovery and conversion happening simultaneously.

This suggests the traditional awareness-to-decision pipeline is giving way to context-dependent buying moments where stages overlap or disappear entirely.

How are social platforms changing product discovery and purchase?

Social platforms have evolved from inspiration hubs into concrete paths to purchase. Over half (57%) of US consumers plan to use social media for shopping research, up from 53% in 2023, found First Insight's report.

Consumer preferences vary by platform:

• Facebook leads for both discovery and checkout, preferred by 59% of consumers.
• TikTok follows at 54%, driven by algorithm-powered product recommendations.
• YouTube ranks third for discovery at 52%, with long-form reviews guiding consideration.
• Instagram leads for checkout at 47%, with shoppable posts reducing friction.

Shopping is becoming increasingly situational, driven by personalized feeds and influencer recommendations rather than active searching. This makes it harder for brands to depend on traditional search-driven paths but opens opportunities in platform marketing.

Do consumers trust AI shopping recommendations?

Trust remains the central friction point in AI-assisted commerce. Only 46% of consumers fully trust AI shopping recommendations, and 89% still verify AI-provided information before buying, according to IAB and Talk Shoppe research. Among shoppers who used AI during their purchase journey, 95% took additional online steps to confirm their decision.

That verification behavior creates an unexpected benefit for retailers: after interacting with AI, visits to brand and marketplace websites increased 80%, according to the IAB study. AI drives traffic rather than diverting it. The trust gap varies by category. Consumers accept AI guidance for repeat or low-consideration purchases more readily than for high-stakes decisions like electronics or travel, where additional research remains standard.

What is agentic commerce, and where does it fit in the path to purchase?

Agentic commerce refers to AI agents that research, compare, and complete purchases on behalf of consumers. Unlike genAI chatbots that assist with information gathering, AI agents execute transactions without continuous human oversight.

Major retailers are building for this shift. Walmart partnered with OpenAI to sell products on ChatGPT via its shopping assistant, Sparky. Amazon's Shop Direct now includes over 100 million products from more than 400,000 merchants, with its "Buy for Me" feature using agentic AI to complete purchases on merchant websites. Consumer acceptance of fully autonomous purchasing remains limited, however. The technology functions better as a copilot than an autopilot, helping narrow choices rather than replacing the decision entirely.

How are retailers adapting content for AI-driven discovery?

Retailers now serve three distinct audiences: human shoppers, AI-assisted consumers, and autonomous AI agents, according to EMARKETER analyst Sky Canaves. Each audience requires different content.

• Human shoppers respond to visual elements, storytelling, and brand consistency.
• AI-assisted consumers need clear, factual product information that chatbots can contextualize within conversations.
• AI agents require well-structured, machine-readable data with detailed specifications, pricing, and availability.

"They're looking for really in-depth product information that's well-structured. They need a lot more content than what typically meets the eye on a product description page," Canaves noted on “Behind the Numbers.”

This is driving adoption of generative engine optimization (GEO), the practice of structuring product content so AI systems can surface and recommend it accurately. Brands with clean, structured data gain a visibility advantage as AI-mediated shopping grows.

What challenges does a fragmented path to purchase create for marketers?

A nonlinear, multi-channel path to purchase creates three primary challenges:

• Attribution complexity. When AI introduces shoppers to a product, they take an average of 3.8 online steps before purchasing, compared with 1.6 steps without AI involvement, per IAB research. Determining which touchpoint drove the conversion becomes harder as the journey spans more channels.
• Content proliferation. Brands must maintain optimized content across social platforms, AI environments, retailer marketplaces, and owned properties simultaneously. Each channel has distinct format and data requirements.
• Budget allocation uncertainty. 71% of shoppers agree AI is effective at narrowing product choices, per IAB data. But with AI reshaping early-funnel behavior, existing media mix models may undervalue or miss AI-influenced conversions entirely.

These challenges are compounded by the lack of standardized measurement across AI shopping environments.

How should marketers optimize for a nonlinear path to purchase in 2026?

Focus on three priorities:

1. Invest in generative engine optimization (GEO). Structure product content so AI tools return accurate, brand-consistent results. Maintain indexed website descriptions, updated product data, and consistent brand voice across channels.
2. Diversify discovery channels. Allocate spend beyond saturated platforms. Experiment with high-intent environments like Reddit and Pinterest alongside established social commerce channels.
3. Strengthen physical retail as a differentiator. As AI expands digital touchpoints, in-store experiences become a competitive moat. "If you don't bolster your store and turn it into a community hub, you will lose," said EMARKETER analyst Suzy Davidkhanian on “Behind the Numbers.”

Brands that treat AI, social, and physical retail as complementary rather than competing channels will capture consumers wherever they enter the purchase journey.

We prepared this article with the assistance of generative AI tools and stand behind its accuracy, quality, and originality.

EMARKETER forecast data was current at publication and may have changed. EMARKETER clients have access to up-to-date forecast data. To explore EMARKETER solutions, click here.