Automation has become the operating system of modern advertising.

Google runs on Smart Bidding.
Meta relies on Advantage+.
Amazon optimizes auctions in real time.

Retail media is now undergoing the same transformation, but at an even faster pace.

Retail media networks are evolving into highly competitive ad marketplaces where thousands of advertisers compete for limited placements across search results, product pages, and category shelves. In this environment, manual campaign management simply cannot keep up.

The retail media ecosystem is becoming too dynamic, too competitive, and too data-heavy for humans to manage every lever.

Advertisers know this.
Retailers know this.
And the data proves it.

According to the Scale of AI Adoption Among Retail Media Advertisers study by Osmos, the overwhelming majority of retail media buyers now rely on AI to manage ad bidding, making automated bidding (autobid) the dominant optimization method across retail media networks.

Automation is no longer an optional feature.

Automation is the auction.

Why Automated Bidding Has Become the New Default

Ad bidding is one of the most complex operational decisions in any retail media campaign.

Advertisers constantly face difficult questions:

• What bid level is needed to stay competitive in an auction?
  
  
• How should bids change when competition spikes?
  
  
• How can advertisers balance visibility with cost efficiency?
  
  
• How can they avoid overpaying while still winning impressions?
  
  

Retail media platforms typically offer suggested bid ranges. These ranges are only estimates. Auction dynamics change every second as advertisers enter and exit campaigns, inventory fluctuates, and shopper demand shifts.

For many advertisers, especially marketplace sellers, manually managing bids becomes almost impossible.

Small and mid-sized merchants often lack:

• performance marketing expertise
  
  
• dedicated marketing teams
  
  
• time to monitor campaigns constantly
  
  
• understanding of auction mechanics
  
  

As a result, bidding decisions quickly become guesswork.

This is precisely why the Osmos research found that more than four out of five advertisers now rely on automated bidding systems to run their campaigns.

AI systems take responsibility for:

• adjusting bids at auction speed
  
  
• interpreting competitive pressure
  
  
• improving ad visibility
  
  
• protecting return on ad spend (ROAS)
  
  

In short, advertisers are no longer interested in choosing bids manually.

They want the system to decide for them.

Marketplaces Need Automation Even More Than Retailers

The Osmos study also revealed another important insight.

Automated bidding adoption is even higher on marketplace ad marketplaces than on traditional retailer RMNs.

This trend makes perfect sense when we consider how marketplaces operate.

Unlike traditional retailers that mainly work with large brands, marketplaces serve thousands or sometimes millions of sellers.

These sellers typically:

• lack marketing expertise
  
  
• have limited resources for optimization
  
  
• depend heavily on ads for discoverability
  
  
• compete in extremely crowded product categories
  
  

For them, advertising is not a marketing strategy.

It is a visibility requirement.

Without automation, these sellers cannot manage bidding complexity.

For marketplaces, this means auto bidding is not a premium feature. It is essential infrastructure.

Marketplaces that implement strong automation gain several advantages:

• more sellers entering the ad marketplace
  
  
• higher advertiser spend
  
  
• stronger campaign repeat rates
  
  
• lower operational friction
  
  

Automation becomes a structural competitive advantage.

In many cases, it becomes a platform moat.

Advertisers Trust AI With Bidding More Than Any Other Lever

The Osmos research surfaced a particularly striking insight.

Among all AI-driven features in retail media, ad bidding is the function advertisers trust most.

Compared to other automated functions such as targeting, product selection, or budgeting, automated bidding has the highest levels of:

• adoption
• trust
• willingness to delegate control
  
  

Why?

Because advertisers openly recognize that bidding is not their strength.

Auction environments change rapidly, and predicting outcomes requires analyzing enormous volumes of signals, including:

• competitor bids
• historical performance
• time-of-day dynamics
  
  
• conversion probability
• product demand patterns
  
  

AI systems are far better equipped to process this information.

They excel at:

• predicting auction outcomes
• reacting instantly to competition
• optimizing toward ROAS
• learning from historical patterns
• balancing cost and revenue tradeoffs

However, advertisers do draw one important boundary.

They are comfortable allowing AI to manage bidding decisions, but they prefer to retain full control over total campaign budgets.

This reveals a clear mental model that will shape the future of retail media automation.

AI should manage operational decisions.

Humans should control financial decisions.

Automation Is Expanding Beyond Bidding

Automated bidding may be the most mature application of AI in retail media today. Automation is rapidly expanding across other campaign functions as well.

AI-Led Product Selection

Advertisers often begin campaigns by manually choosing products to promote.

Once campaigns are running, AI systems help identify opportunities such as:

• missing SKUs that should be promoted
• listings that are underperforming
• additional products that improve category coverage
• complementary items that increase campaign reach

This hybrid model combines human intent with machine optimization and is quickly becoming standard across retail media platforms.

AI-Led Targeting and Keyword Expansion

The Osmos research also shows growing adoption of AI-driven targeting capabilities, including:

• AI-curated keyword recommendations
• automated keyword expansion
• contextual targeting based on category signals
• dynamically generated audience segments

Instead of advertisers manually defining every targeting parameter, AI systems continuously analyze shopper behaviour and adjust targeting in real time.

In many cases, AI determines where and when a product should appear, not the advertiser.

AI-Led Budget Pacing and Optimization

Budget control remains one of the last areas where advertisers prefer to maintain authority.

However, automation is expanding here as well.

Retail media platforms increasingly provide AI-driven assistance for:

• campaign pacing optimization
• early warnings about budget exhaustion
• hourly spend distribution adjustments
• recommendations for more efficient allocations
  
  

These capabilities represent the early stages of agentic optimization.

‍

The Emergence of Agentic Retail Media

Automated bidding is only the first step in retail media automation.

AI-assisted product selection, targeting, and pacing represent the next phase.

But the transformation goes further.

The future of retail media will be agentic.

In this model, advertisers do not configure campaigns manually. Instead, they define objectives.

For example:

• “Increase sales of this SKU by 10%.”
• “Maximize impressions within my $200 daily budget.”
• “Drive new-to-brand purchases in this category.”

Once the objective is set, the system handles the rest.

AI will automatically:

• create campaigns
• select products to promote
• choose placements across the ad marketplace
• apply automated bidding strategies
• activate contextual and behavioral targeting
• optimize performance throughout the day
• pause underperforming products
• scale successful ones
• generate insights and recommendations
  
  

Retail media campaigns will shift from lever-driven management to goal-driven optimization.

Advertisers will manage outcomes.

AI will manage execution.

Why Automation Is Now Mandatory for Retailers

Automation is no longer just an operational convenience.

It has become the backbone of a scalable retail media ad marketplace.

Retailers and marketplaces cannot compete without:

• automated bidding systems
  
  
• real-time auction optimization
  
  
• AI-driven targeting
  
  
• product selection intelligence
  
  
• budget pacing algorithms
  
  
• continuous campaign learning loops
  
  

The reasons are structural.

Advertisers expect automation because platforms like Google, Meta, and Amazon already provide it.

Marketplace sellers cannot operate effectively without automated support.

Retailers cannot scale advertising revenue if campaign management requires manual intervention.

Advertisers also spend more confidently when outcomes become predictable.

A modern retail media network must minimize operational friction for advertisers, especially during onboarding and campaign setup.

Retailers that build sophisticated automation infrastructure will attract more advertisers, more campaigns, and more spend.

Retailers that do not will eventually compete primarily on price or lose advertisers to platforms with stronger automation capabilities.

Conclusion: Automation Is the Engine of Retail Media Auctions

Retail media is entering an era where:

• ad bidding is automated
  
  
• auctions optimize in real time
  
  
• AI drives targeting decisions
  
  
• product selection adapts dynamically
  
  
• campaigns continuously self-optimize
  
  

Automated bidding, often referred to as autobid, is only the first milestone in this evolution.

The next phase will be agentic retail media, where advertisers stop managing campaign mechanics and instead focus on outcomes.

The findings from the Scale of AI Adoption Among Retail Media Advertisers by Osmos make this direction clear.

Retailers that embrace automation will scale their ad marketplaces faster, attract more advertisers, and unlock higher revenue.

Retailers that resist automation will struggle to meet advertiser expectations.

Automation is not a feature.

It is the scientific engine behind scalable retail media auctions.