A Deep Dive on AI and Ad Platforms

Earlier this summer we featured a blog on how AI is shaping advertising. This continues to be a major topic in our industry. In this blog, we dig deeper into how AI is impacting campaign strategy, implementation and performance, along with some nuggets we have taken away from our own experience so far. The big picture here is important: there are major advantages to AI, especially if you have an intimate knowledge of the platform and how AI is impacting your performance. But there are limitations too, and it’s important to be aware of these. AI-powered levers in ad platforms are not a one-size fits all solution. Read on to learn more. 

AI-powered tools, which provide deeper insights, increased efficiency, and unparalleled customization, are no longer a novelty but rather a requirement. Integrating AI into your campaigns as a digital marketing specialist can give you a significant competitive edge.

AI tools enhance marketers’ abilities to automate bid management, ad placement, audience targeting, and even ad creation, helping marketers focus on strategy rather than execution. Here are some ways AI helps.

• AI-Driven Audience Targeting: One of the most powerful uses of AI is to analyze consumer behaviour based on their browsing habits, purchase history, and social media interactions. AI uses sophisticated algorithms, predictive analytics, and enormous volumes of data to reach the target audience at the right time. All media platforms have launched some kind of AI-based campaign in recent years, whether it is Google’s Performance Max or Meta’s Advantage+ campaigns. Recently, Pinterest has also rolled out its version of Performance+ campaigns similar to Meta’s Advantage+. Digital ad platforms also allow the use of first-party data for targeting and use AI to find new potential customers who share similar traits in the form of look-a-like audiences/similar audiences. Machine learning models help segment audiences into specific groups based on demographics, interests, and behaviours, allowing for more personalized marketing strategies.
• AI-Driven Campaign Optimization: Most media platforms offer AI-driven campaigns that enhance optimization with real-time bid strategies. Google’s smart bidding uses a machine learning algorithm that analyzes vast amounts of data, such as device, location, time of day, demographics, and audience behaviour, to adjust bids in real-time and achieve the advertiser’s goals. Google Ads also provides an “Optimization score”, which is an AI powered metric that evaluates the accounts’ performance and suggests actionable recommendations (such as keyword ideas, budget adjustments, and implementing new bidding strategies, etc.). While this helps advertisers to identify trends and make informed decisions quickly, advertisers must carefully consider recommendations depending on their campaign objectives and business needs. On the other hand, Meta offers tools like campaign budget optimization, which uses AI to optimize budget allocation across different ad sets based on real-time performance, ensuring spends are going towards the most effective ads. Similar to Google and Meta, other ad platforms also offer different types of tools that leverage AI to optimize campaigns and enhance the overall campaign performance.
• Use of AI in Creative Development: AI is transforming creative development on media ad platforms by supporting and enhancing the creative process across various aspects of ad development. AI can generate dynamic ad variants tailored to different audience segments. For example, on Google and Meta, dynamic creative ads combine multiple headlines, images, and descriptions to create various versions and find the best-performing combinations. Also, responsive display ads on Google resize the images provided based on the placement available on the website. Similarly, Meta uses standard enhancements to apply changes to the ads automatically with the aim of optimizing ad performance based on individual users behaviors and preferences. In addition to helping with creative itself, these ad platforms also use AI to give copy ideas, allowing advertisers to quickly test multiple versions of headlines, descriptions, and CTAs. These AI-driven tools help advertisers optimize ad creatives more efficiently and effectively across media platforms.

We tested Meta Advantage Shopping campaigns for one of our retailer clients and saw a 25-30% higher ROAS on Advantage Shopping campaign vs. manual targeted campaign.

In addition to these most common uses of AI on media ad platforms, AI has become a versatile tool that’s transforming various facets of digital marketing. Beyond ad targeting and optimization, AI is now integral in everything from customer engagement to data analytics, making it possible for marketers to execute campaigns with greater precision, speed, and scale.

Limitations

Although AI-powered media platform campaigns have many advantages, marketers should be mindful of some possible drawbacks. Here are some of the primary cons:

• Limited Control and Transparency: Most of the AI-driven campaigns, like Performance Max or Meta’s advantage+ campaigns, are “black boxes”, which means advertisers have limited visibility into how and where the ads are getting served and how the bidding decisions are being made in real-time. Advertisers lose control over more granular campaign settings such as bid adjustments, targeting, and placements, which makes it challenging to make manual adjustments or to understand what’s driving results. The story stays the same when performance drops and advertisers don’t have a clear answer on what might not be working.
• Bigger Data Set Requirement for Learning: In order to properly optimize, AI-driven campaigns usually go through a “learning phase” during which they collect data. A large amount of the budget may be spent at this phase, and if the campaign is not in line with the business needs, it can lead to wasted ad spend. Most of the AI features work best with larger budgets and datasets, which can be limiting for small businesses with budget constraints.
• Less Flexibility in Creative Control: Although AI can tailor messages, it can also lead to tone or style inconsistencies among ad variations, which, if not constantly monitored, could dilute the brand’s identity. For example, AI tools like dynamic creative/standard enhancements in Meta can sometimes produce an outcome that doesn’t align with brand aesthetics. Also, Responsive Search ads in Google ads automatically combine assets, and advertisers may find that the final creative combinations aren’t the best. With less control of these permutations and combinations, it’s hard to identify what’s working and what’s not, which in turn limits the ability of advertisers to find the weak link of the ad.
• Misses the Human Insight Needed for Strategic Growth: AI cannot understand the contextual nuances like a human can. E.g., a machine learning algorithm might just focus on the optimization to drive conversion volume, but it is unable to identify if those conversions are resulting in high-quality leads or are merely junk leads. Human intervention is needed to analyze that side of the campaign. Also, the business performance can be highly dependent on external factors such as competition, economy, etc. that AI does not always take into consideration while optimizing the campaigns. Marketers who rely too much on AI may get complacent since they may feel less of a need to actively plan and monitor their efforts. This can lead to missed opportunities for long-term planning and insights, both of which are necessary for steady growth.
• Dependency on the Campaign Inputs: AI-powered campaigns rely heavily on the quality of advertiser inputs. E.g., in Google Performance Max campaigns, advertisers can add audience signals that can guide AI to better optimize for campaign goals. While optional, these signals help align machine learning with strategic objectives. However, if these inputs are inaccurate or poorly defined, the AI will optimize based on flawed data, potentially resulting in low-quality outcomes and missed campaign objectives. This highlights the importance of well-researched, strategic input to fully leverage AI’s potential in digital advertising.

While AI-powered tools have transformed digital marketing by optimizing campaign execution, targeting, and creative development, they also present unique challenges that marketers must navigate carefully. Integrating AI can deliver a significant competitive edge through enhanced efficiency and scalability. However, the limitations—such as reduced control, dependency on large datasets, and potential misalignment with brand aesthetics—highlight the importance of balanced human oversight and strategic input. By combining AI’s strengths with human insight, digital marketers can fully leverage these tools for long-term growth, ensuring campaigns are not only data-driven but also strategically aligned and adaptable.