While AI offers clear advantages, marketers should be mindful of challenges and limitations:
Loss of Control and Transparency: One common concern is the “black box” nature of AI algorithms. When you use an automated bidding strategy or an AI-driven campaign type like Performance Max, you’re relinquishing direct control over levers such as audiences, devices, locations, and more. And it can be frustrating to know that the bidder is making real-time adjustments, and not knowing exactly what those are: 55% of Americans do not trust AI to make unbiased decisions, partly due to this lack of transparency (Yahoo!/Publicis Media. (2024). Trust Through Transparency: AI and Advertising). Advertisers need to be able to trust in the algorithm and reduce manual interventions – a campaign kept in learning by constant changes can be as negatively impacted as a campaign not using AI at all. It’s important to set proper expectations with stakeholders that you may not get a detailed rationale for every optimization the AI makes.
Data Requirements: One of the first things I tell my clients when they come on board at IgniteIQ is that their most valuable resource is their first party data. This includes not only front-end metrics in ads platforms (clicks, conversions), but also customer data, audience segments, offline conversion tracking, and more. AI systems are only as good as the data fed into them. For instance: if you’re using a tCPA bid strategy, but you’re only driving 1-2 conversions per day, the algorithm is going to have a very hard time learning what kinds of signals drive conversions. This requires accurate, reliable tracking and data density. Google typically recommends at least 15-30 conversions per campaign within your attribution window for smart bidding to have enough data to learn. (Not enough conversion volume? Try optimizing for a ‘micro conversion’ such as an email signup or video view.) Similarly, if conversion tracking is broken or key offline sales aren’t being tracked, the AI will optimize toward incomplete goals. Data quality and volume are critical – marketers often need to invest in analytics and tracking (e.g. importing CRM data, setting up proper conversion events) before turning to AI-backed strategies.
Bias and Undesired Outcomes: Because AI in marketing is geared towards driving results, sometimes algorithms can inadvertently cause biased advertising. In one notable example, a study found that Facebook’s algorithm was delivering job ads differently by gender and race even when targeting was identical. Ads for mechanical or janitorial jobs were served more frequently to men and minorities, while serving ads for secretary or nursing jobs to more women – reinforcing gender stereotypes (MIT Technology Review. (2019). Facebook’s Ad-Serving Algorithm Discriminates by Gender and Race). It makes sense – historically, these were the users who clicked on the ads, therefore reinforcing the stereotype to the algorithm. While there’s not a lot that advertisers can do to prevent this from happening, marketers can be aware of “algorithmic bias” and watch out for any delivery that could go against brand values or violate policies. Regular audits of where and to whom your ads are showing can catch issues early. Platforms like Facebook have had to implement new safeguards (e.g. for housing and employment ads) to prevent discriminatory outcomes. In addition, creative can be a good tool in ensuring more equitable ad delivery – be sure your talent is diverse and speaks to any qualified user, regardless of gender, race, etc.
Brand Safety and Automated Creatives: AI-generated content and creative can be a godsend for advertisers with limited creative resources, but can potentially be out of line with a brand’s voice or goals. The strongest examples are Responsive Search Ads and Performance Max: the dynamic nature of these ad types mean that Google has control over which copy to serve alongside each other, and occasionally these combinations are clunky. However, since the algorithm is always learning, trial and error will eventually teach Google which headlines to skip or when to use a certain headline. Advertisers can also “pin” headlines, which ensures that a headline will always serve in the designated placements. Even when using dynamic content or AI-generated assets, human oversight is important to keep checks and balances on these systems. Marketers should periodically review AI-created ads, search query match-ups, and placements.
Key Takeaways and Next Steps
Key Takeaways: Despite these challenges, most advertisers find the pros outweigh the cons. Smart bidding and machine-learning products such as Performance Max are now the industry standard, and marketers can ensure success by maintaining human oversight to review automated decisions, or starting with small experiments (try switching a few keywords at a time to broad match). With careful implementation, issues like bias or lost transparency can be managed.
Next Steps for Marketers: If you haven’t already, start exploring AI tools in your search platforms. Consider running a pilot test of Google smart bidding on a campaign with solid conversion data. Experiment with responsive search ads or dynamic search ads to let the algorithm improve your creative delivery. As you implement these, regularly review performance and feed the AI with guidance (new negatives, adjusted targets) to steer it. Also, invest in educating your team – make sure everyone understands how these AI features work and what the metrics mean. Finally, maintain a mindset of continuous testing and learning. AI in Paid Search isn’t a one-time project but an ongoing collaboration. Marketers who adapt to this new role – part strategist, part “AI pilot” – will be best positioned to capitalize on the revolution happening in search advertising.
