From Funnel to Tunnel: GenAI is Collapsing the Path to Purchase and Programmatic Retail Media Could Be the Answer

Part 3 of 4 in the “Retail Media’s Programmatic Future” Series

Read the entire series

• Why Programmatic Marketers Should Bet Big on Retail Media
• Survival of the Smartest: Retail Media’s Race Toward Programmatic Profitability
• Align or Fall Behind: Choosing the Right Programmatic Partner for Retail Media

With Artificial Intelligence, consumers are making purchase decisions now happen in minutes that once took weeks. For marketers looking for ways to meet this new reality, programmatic retail media offers the automation, speed, and commerce data needed to compete in AI-driven shopping moments.

This acceleration is reshaping commerce media strategy from end to end. Planning cycles that assumed weeks of research now compete with instant AI-powered recommendations. Creative that once nurtured prospects over multiple touchpoints must resonate in a single interaction. Measurement frameworks built on long attribution windows must deliver real-time insight to keep campaigns aligned with consumer action.

Programmatic retail media is built for this reality. It combines verified commerce signals, automated execution, and cross-channel reach to keep brands present and measurable even when the window to influence a purchase shrinks to seconds. In an environment where consumer decisions move at machine speed, programmatic retail media provides the infrastructure marketers need to keep pace.

Why is AI compressing commerce decisions into a “tunnel” instead of a funnel?

• AI-driven recommendations are collapsing research and consideration into seconds, forcing marketers to influence outcomes earlier and faster.
• Show up where AI decides.
• As discovery shifts to assistants and predictive feeds, brands need programmatic retail media to ensure presence, relevance, and measurable outcomes during fleeting AI-mediated moments without relying on long consideration cycles. According to Microsoft’s 2025 guide, generative AI is making search conversational and shortening paths to purchase.

AI in the purchase path isn’t an evolution, it’s a revolution. Consider how quickly a recommendation turns into a transaction. Someone asks ChatGPT for product suggestions or even how to solve a problem without even a product in mind, is offered a retailer URL, clicks through, and buys on the spot. The traditional awareness, consideration, and conversion model collapses into a single step. For marketers, the challenge is clear: how do you show up in those fleeting moments where intent spikes and disappears just as fast?

Brands are losing control of the discovery moment — what should they do?

Assistants now curate “first-touch” recommendations before consumers reach owned or retailer touchpoints.
Win the pre-click.
Ensure data coverage, retail availability, and creative that maps to common AI prompts; pair Sponsored ads with DSP retargeting so you’re present both in AI-curated lists and where users land after clicking.

Traditional commerce marketing assumed that purchase journeys began on brand websites or retailer platforms.

Marketers could influence the process by intercepting consumers during their research phase across owned and paid touchpoints. AI assistants change this dynamic by becoming the starting point for product discovery. Instead of beginning research on Amazon or Google, consumers ask ChatGPT or Claude for recommendations. These AI systems provide curated product lists with direct purchase links, often bypassing traditional research entirely.

This externalization means that influence happens before consumers reach advertiser-controlled touchpoints. Brands that aren’t represented in AI training data or recommendation algorithms become invisible during the discovery phase. Retailers whose products aren’t easily accessible through AI-generated links lose potential transactions.

Programmatic infrastructure helps brands maintain visibility in these externalized journeys by ensuring presence across the touchpoints where AI systems gather information and where consumers land after receiving recommendations.

Predictive algorithms are rewriting intent-based targeting — how should targeting evolve?

• AI predicts demand before it’s signaled by queries or site visits.
• Shift from reactive to predictive.
• Use contextual, weather, and browsing signals to pre-activate campaigns and scale audiences dynamically; automate pacing and bids so spend follows predictive lift rather than historical intent alone. eMarketer reported in April 2025 that Skai’s Celeste AI aims to simplify commerce media with agent-driven optimization.

Netflix recommends movies before you search for them. Spotify suggests songs before you know you want to hear them. Amazon recommends products before you realize you need them.

These predictive recommendations create purchase opportunities that traditional intent-based targeting can’t capture. Traditional targeting approaches wait for intent signals like search queries or website visits before activating campaigns. AI recommendation systems reverse this dynamic by predicting what consumers will want before they explicitly signal interest.

Programmatic platforms can activate campaigns against these predictive signals rather than waiting for reactive indicators. When weather data suggests increased demand for space heaters, campaigns can launch automatically before search volume increases. When browsing patterns indicate emerging interest in specific product categories, targeting can expand proactively. This shift from reactive to predictive campaign management requires automated infrastructure that can process multiple data signals simultaneously and adjust campaign parameters faster than human operators can recognize opportunities.

Why is programmatic the only infrastructure fast enough for AI commerce?

• AI-speed decisions outpace manual workflows and quarterly measurement cycles.
• Automate or get outrun.
• Programmatic retail media aligns real-time bidding, dynamic creative, and continuous measurement so campaigns adjust as fast as AI-driven demand shifts, compounding performance advantages over time. As Skai coverage and industry commentary show, programmatic + commerce data gives the speed and attribution necessary for AI-era buying.

AI acceleration creates operational requirements that manual campaign management simply can’t meet. The speed, complexity, and continuous optimization demands exceed human capacity for response and decision-making.

AI compression creates new operational requirements — which five matter most now?

Compressed decisions demand machine-paced operations across the plan–create–measure loop.
Build for real-time change.
Prioritize contextual intelligence, predictive activation, dynamic creative, continuous measurement, and automated response—each supported by infrastructure that learns and reallocates in minutes, not months. Microsoft’s 2025 research underscores the shift to conversational, multimodal search and the need for new operating models.

Decision compression eliminates traditional research phases. AI tools eliminate many of the steps that traditional marketing funnels assumed were necessary. Product comparison happens instantly through AI analysis rather than through manual research across multiple websites. Purchase decisions are made based on AI recommendations rather than extended consideration processes.

Context becomes more important than demographics. AI systems consider dozens of contextual variables when making recommendations: time of day, weather conditions, location, device type, recent activity, and current events. This contextual intelligence makes traditional demographic targeting seem primitive by comparison.

Creative adaptation happens faster than human creative cycles. AI tools now modify creative assets in real-time based on performance data and contextual signals. Headlines change automatically based on audience response. Images are selected dynamically based on demographic and behavioral indicators.

Measurement cycles accelerate from quarterly to continuous. Traditional campaign measurement operated on quarterly or monthly cycles because data collection and analysis required significant manual effort. AI acceleration demands real-time measurement that can inform immediate optimization decisions.

Automated response becomes the only viable mechanism. Manual campaign management can’t keep pace with AI-accelerated consumer behavior and market dynamics. The speed of decision-making, the sophistication of targeting requirements, and the frequency of optimization needs exceed human operational capacity.

How should marketers respond to disruption without losing control?

• Treat AI as a market shift, not a novelty—govern, test, and scale.
• Plan, pilot, prove, then expand.
• Stand up guardrails and KPIs, pilot one high-value workflow per channel, and scale wins with human-in-the-loop checks to maintain brand standards while capturing AI-speed demand. Skai’s broader 2025 series highlights the operational payoff when automation and standardized measurement are embedded.

For many brands, the shift to this new “tunnel” model where shoppers skip over much of the traditional funnel, is unsettling. It disrupts how marketers have historically influenced each phase of the journey. What was once a slow and measurable path from awareness to purchase is now compressed into a few seconds and shaped by systems outside a brand’s direct control.

But disruption is nothing new to marketers. The job has always been to assess the current environment and adjust spend, targeting, and messaging to match. AI is a major shift, yes, but it’s just the latest evolution in a long line of market changes. 

The good news is, marketers and retailers have AI too.

Programmatic retail media is already using AI to manage complexity, adapt creative, automate bidding, and predict outcomes. Tools like Skai’s predictive AI modeling or Amazon Marketing Cloud audience analysis give marketers the same data firepower that’s driving consumer platforms. This isn’t about being outpaced by machines—it’s about matching their speed with smarter systems.

When retail marketers lean into programmatic infrastructure, they’re not just reacting to change. They’re equipping themselves with tools to shape outcomes, anticipate demand, and operate at the same velocity as their customers. The tunnel might be short, but programmatic retail media gives marketers a direct line to influence what happens inside it.

Conclusion: Why will automated infrastructure keep widening the performance gap?

• Automation compounds advantages as data density and optimization frequency outstrip manual teams.
• Systems beat sprints.
• End-to-end programmatic stacks continuously test audiences, creatives, and bids, creating a widening gap versus manual setups as AI accelerates both consumer behavior and competitor response.

Programmatic infrastructure provides the automated response mechanisms that AI-accelerated commerce demands.

Real-time bidding responds to opportunity signals as they emerge. Automated optimization adjusts campaigns based on performance data continuously. Dynamic creative optimization adapts messaging and imagery based on contextual factors. This infrastructure advantage compounds over time. Automated systems accumulate data faster, optimize more sophisticatedly, and respond more quickly than manual alternatives. The performance gap between automated and manual operations continues widening as AI acceleration intensifies.

For retail media specifically, programmatic infrastructure enables the speed and sophistication that AI-powered commerce requires while maintaining the measurement precision that justifies continued investment.

When consumer behavior operates at machine speed, only machine-powered infrastructure can respond effectively. Programmatic retail media provides that infrastructure while maintaining the commerce measurement advantages that make retail media strategically valuable.

For unified cross-channel execution, explore our omnichannel marketing platform for end-to-end planning and activation.
To operationalize agent-driven insights and workflows, see Celeste AI.

This article is part of a four-part series on retail media’s programmatic future. Each article can be read independently.

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