AI-Powered Optimization is Transforming Commerce Media Advertising

Artificial intelligence has touched every industry in the world, including marketing, advertising, and the commerce media landscape. AI programs now drive commerce media forward by automating decisions that once required hours of manual work. 

Today's brands harness machine learning to manage complex bidding strategies and audience targeting across multiple retail networks. They do so at efficiency levels that manual management simply cannot achieve. According to a report by Forbes, platforms using AI-powered discovery methods have produced increased engagement and conversion rates because their results are more accurate and personalized.

Here, we’ll explore how this technology has evolved from an experimental tool to an essential business capability. We’ll also show how AI delivers measurable efficiency gains and cost savings across the industry.

Speed and Scale Make Up the Foundation of AI Automation in Commerce Media Advertising

Modern commerce media advertising operates at a scale and complexity that stretches human capabilities. Brands manage campaigns across hundreds of retail media networks, each with unique targeting options, attribution windows, and reporting formats.

Traditional manual management approaches tend to break down when teams face thousands of keywords, dozens of retailers, and constantly shifting consumer behaviors.

According to one speaker at CMBS 2025, AI automation transformed their operations "within just a few months.”

"The system saved us $50,000 on negative keyword management in just a few months and eliminated thousands of work hours of manual bidding work over a full year,” they said. "AI manages our keywords around the clock, something I wouldn’t have the time or resources to do even if I hired 100 people.”

Across industries, concerns about AI replacing jobs and sidestepping human input are common. However, leaders are discovering that AI-powered capabilities allow them to execute projects that would be impossible for human teams to execute at scale. Furthermore, the technology often frees human employees to work on other, more value-added tasks.

AI Automates Smart Bidding and Budget Allocation at Scale

AI transforms bidding strategy from reactive to predictive. Advanced systems analyze historical performance data, consumer behavior patterns, and market signals to optimize bids automatically.

One speaker at CMBS 2025 claimed brands can achieve 25% more attributable uplift and 40% increased household penetration when machine learning powers their campaigns.

"We can achieve up to 90% higher match rates with machine learning attached to our campaigns,” they said. They also claimed to see performance increases when using proprietary data:

"Compared to third-party data, we have seen that our first-party data can deliver the same business impact with 51% fewer impressions.”

Real-Time Optimization Capabilities

Sophisticated bidding platforms break campaign management into strategic categories, such as the following:

• Defensive brand protection
• Competitor "conquesting"
• Category expansion
• Prospecting for new audiences

AI systems set specific rules for each goal, automatically adjusting bids to hit target metrics while freeing human teams for strategic analysis.

• Defensive campaigns protect branded search terms and maintain market share
• Offensive strategies target competitor keywords during budget gaps or inventory shortages
• Category expansion captures broader relevant search terms and shopping moments
• Prospecting efforts identify and convert new customer segments automatically

Performance data shows these automated systems hit target metrics with 85-90% accuracy. When human oversight focuses on strategy while machines handle tactical execution, brands achieve both efficiency and effectiveness improvements.

Audience Targeting and Personalization

Traditional audience targeting relied on broad demographic categories and basic behavioral signals. AI enables granular personalization by analyzing hundreds of data points simultaneously.

Purchase signals, browsing patterns, seasonal behaviors, and competitive actions combine to create dynamic audience profiles that update continuously.

"Our new purchase signal algorithm is a great example of what AI machine learning can really do, which is more sophisticated than just analyzing data at the brand or at the retailer,” said one speaker at CMBS 2025.

"It provides a combined view, helping us use custom machine learning logic to identify high-value households and optimize toward purchase signals.”

AI systems can consider contextual factors like inventory levels, seasonal demand patterns, and even weather forecasts to optimize ad serving.

According to a major food brand that participated in CMBS 2025, they can even use temperature and precipitation data to automatically decide between promoting different types of products. For example, if the day is cold and rainy, a promotion for a new soup would likely fare better than a promotion for ice cream.

AI-Powered Content Creation and Creative Optimization Drives Sales Increases

Generative AI transforms creative development from time-intensive manual processes to scalable automated systems. Brands can test multiple creative variations simultaneously, with AI scoring each version for conversion potential against specific audience segments.

Automated Creative Testing

Advanced content optimization platforms analyze creative elements systematically. Image composition, color schemes, text placement, and product positioning all factor into conversion scoring algorithms.

At CMBS 2025, brands reported triple-digit sales increases when AI optimized creative content for specific audience segments.

The technology proves particularly valuable for product imagery. AI systems can identify optimal visual elements for different consumer groups, automatically adjusting image focus, text overlays, and promotional messaging. These refinements improve click-through rates and conversion metrics while reducing creative production time.

Campaign Management and Automation

Finally, end-to-end campaign automation represents the frontier of AI applications in commerce media. Advanced platforms handle media planning, campaign creation, optimization, and performance analysis with minimal human intervention.

"At least 80% of retail media operations can be automated,” predicted one commerce media expert at CMBS 2025. This includes strategic decisions like budget allocation across different retailers and business segments, plus tactical execution like automatically building campaigns.

"AI systems can make these decisions by processing market signals and performance data that would be impossible for humans to analyze at scale.”

Brands Can Overcome Implementation Challenges with a Systematic Approach

AI implementation in commerce media faces significant practical hurdles. Data quality issues, integration complexities, and organizational resistance create barriers that slow industry adoption.

Success requires strategic attention to data infrastructure, comprehensive team training, and effective change management processes.

Creating Dedicated Centers of AI Excellence

Leading brands tackle AI systematically by establishing dedicated centers of excellence that bridge business and technical teams. Rather than attempting broad deployment, they focus on specific applications first, such as negative keyword optimization or automated bid management.

This allows teams to build confidence and expertise before scaling operations.

Training Teams to Use AI-Powered Tools

As AI assumes more operational responsibilities, training becomes essential. Marketing professionals must evolve from manual execution to strategic oversight, developing skills in:

• Effective prompt and input creation
• AI performance interpretation and optimization
• Strategic planning and goal setting
• Quality assurance and anomaly detection

The most successful organizations view AI as augmenting human capabilities rather than replacing them entirely. This ensures that strategic thinking and creative problem-solving remain central to commerce media success while leveraging AI's computational advantages for operational efficiency.

Case Study: Hershey’s AI-Powered Media Automation

In 2024, chocolate brand Hershey emerged as a leader in automated media optimization, particularly during its high-stakes Halloween campaigns. The candy giant partnered with Chalice Custom Algorithms to develop sophisticated AI-driven bidding systems that analyze sales data in real-time.

Key Automation Features:

• Dynamic Market Targeting: The system automatically allocates media spend to underperforming markets by analyzing weekly ZIP code data
• Custom Bidding Algorithms: AI adjusts bids dynamically based on regional demand and inventory levels
• Cross-Platform Optimization: Centralized media planning across Meta, YouTube, and The Trade Desk platforms
• Real-Time Campaign Fluidity: Reduces the need for hundreds of manual line items by automating campaign management

The results were significant. Hershey achieved a 1.5% growth in sales that was directly attributable to automated media optimization.

"We took three platforms that don’t speak together… and built the layer above [them] to make sure that we’re allocating all of our budgets appropriately, so that we’re not over investing across each platform in those markets,” said Vinny Rinaldi, Head of U.S. Media for CMG and Salty Snacks at The Hershey Company, in a report by Marketing Dive.

"We were able to bring all these things together to actually prove the value through match-market testing.”

Creating AI-Ready Media Advertising Operations

Leading brands approach AI implementation systematically. They establish centers of excellence, integrate business teams with technical capabilities, and focus on specific use cases before scaling broadly.

Starting with focused applications like negative keyword optimization or bid management allows teams to build confidence before expanding to comprehensive automation.

Training becomes critical as AI handles more operational tasks. Marketing teams must develop skills in AI oversight, strategic planning, and performance interpretation rather than manual execution. The most successful organizations treat AI as augmenting human capabilities rather than replacing them entirely.