Over the past couple of years, Ad platforms like Google and Meta have gone all-in on leveraging AI to help manage digital ad campaigns. This has caused ad spend to trickle into placements that you would typically not use if you had full control of campaigns. In today’s digital marketing landscape, AI-driven campaign types like Google’s Performance Max and Meta’s Advantage+ promise better performance through advanced automation and machine learning. There’s no argument that AI can optimize bids in real-time more efficiently than a human could. Still, many marketers suspect that platforms are also using these all-in-one systems to funnel budgets into less-proven or lower-performing placements.

In this post, we’ll dive into why AI-driven campaigns have become so prevalent, how the ad platforms benefit, and how savvy marketers can strike a balance between human insight and machine intelligence.

How We Got Here: The Rise of AI in Paid Media From Smart Bidding to Fully Automated Campaigns

A few years ago, tools like Smart Bidding and automated ad rules were considered cutting-edge. Now, Google’s Performance Max and Demand Gen campaigns allow marketers to run ads across different aspects of Search, Display, YouTube, Discover, Gmail, and Maps from a single campaign. Similarly, Meta’s Advantage+ solution (formerly known as Automated Ads) removes much of the manual setup of ad targeting, bidding, and creative optimization.

Why This Shift to Automation?

• Technology Advancements: Improvements in machine learning have made real-time optimization possible at a massive scale. 
• User Demand: Many advertisers, especially small businesses, want simpler, more hands-off solutions. Low budgets may mean they have a hard time justifying the cost of using an agency or employing a digital specialist to run their campaigns.
• Platform Priorities: Google and Meta benefit when more ad inventory gets filled, which is easier if advertisers rely on a single, AI-driven campaign type rather than cherry-picking placements. An experience specialist will always go for the best-performing inventory, creating a glut of unused placements that aren’t generating revenue for the platforms.

The Upside of AI-Driven Campaigns

Before we talk about the potential pitfalls, it’s important to highlight why AI-driven campaigns have garnered so much attention—and not just from the platforms pushing them:

• Real-Time Signal Reading:  AI continuously monitors user signals (e.g., time of day, device type, user behaviour) to optimize bids in ways a human simply can’t match at scale. Simply put, machine learning can read thousands of signals across campaigns at a rate that no human ever could. Couple that with advanced AI models filled with years of historical campaign data that have been trained by professional marketers, and humans just can’t keep up. 
• Time Savings: Rather than spending hours adjusting bids or dissecting audience segments, you can let the platform’s algorithms do the heavy lifting—freeing you up for creative or strategic tasks.
• Built-In Best Practices: For new or busy advertisers, automated campaigns can serve as a “best-practice factory reset,” preventing major setup mistakes. Automation can eliminate many of the mistakes a less experienced specialist would make in the past when campaigns were set up and run manually.
• Scalability:  Larger advertisers running thousands of ads benefit from letting an algorithm handle granular optimizations. Machine learning can mix and match ad copy, headlines, and descriptions on the fly to optimize performance, where manual labour would need to do this in the past.

The Downside: Less Control and Opaque Placements

While the technology behind AI-driven campaigns is impressive, it’s not without its downsides. This is especially true when it comes to placement control:

• Limited Reporting: In many AI-driven campaign dashboards, you’ll see aggregate performance data (e.g., total conversions, cost per acquisition), but limited insight into exactly which placements or audiences are performing best. For instance, some advertisers note that Performance Max lumps performance metrics together, making it difficult to exclude underperforming sites or apps. After years of feedback from advertisers, Google has recently started to provide channel-level reporting for Performance Max, which allows you to understand what channels are serving and driving conversions. This is a big improvement, but it still has many limitations around allowing a specialist to understand what, where and when ads are actually being served.
• Forced Use of Lower-Performing Placements: Because Ad platforms optimize to placements across Google’s or Meta’s entire ecosystem, your ads may appear in places you would normally exclude if you were running manual campaigns (e.g., certain Display Network sites, less relevant social feeds, partner networks, etc.).
• Inflated Ad Spend on New or Unproven Inventory: AI-driven tools often attempt to expand reach by testing new audience segments and placements. While this can uncover potential wins, it can also lead to higher-than-expected spend in areas that simply don’t convert. This is especially true in smaller markets when datasets are limited due to smaller audiences, reach and budgets. Canada’s huge geographic spread relative to its population often creates this challenge.

Why Platforms Are Motivated to Push AI-Driven Placements

• Inventory Utilization – Platforms like Google and Meta have a vast array of ad inventory. As the premium placements get saturated, there’s a need to drive advertiser budgets elsewhere. By bundling all placements together in a single “smart” campaign, they can fill more inventory without asking advertisers for explicit consent.
• Revenue Growth – More placements filled = more ad impressions = more revenue. Simple math, right? When smart marketers run separate, manually optimized campaigns, they tend to focus on the highest-performing placements and ignore the rest. Automated campaigns remove that choice, to some extent, which allows ad platforms to see incremental revenue where otherwise they may not.
• Data Aggregation (Learning) – The more data you feed AI systems, the “smarter” they become and the more “locked-in” you become. For instance, Performance Max relies on aggregated signals across all Google properties, which encourages you to funnel more budget into the system for it to learn effectively. The hope is that 
• if you feed enough data and money in, you will eventually see a positive result. Once this is achieved, you won’t want to give the channel up for fear of losing momentum.

Striking a Balance: Human & Machine

Despite all of the negatives, AI isn’t going away. Which means instead of resisting it, we must consider how to blend human insight and strategy with machine intelligence:

• Leverage Automation for Bidding and Budget Allocation:  Let the algorithms handle rapid-fire optimizations that are impossible to do manually at scale.
• Stay Vigilant With Placement Exclusions Where Possible: Check the placements or audience segments in your reporting (as much as the platform allows) and proactively exclude the ones that are clearly off-brand or underperforming.
• Run Control vs. Automated Tests: Whenever possible, run split tests (e.g., one campaign using Performance Max vs. another set up manually on search) to see how different strategies compare. Many marketers and agencies share A/B test results on blogs like Search Engine Land and WordStream, which can guide your approach.
• Set Clear KPIs and Monitor Closely – Don’t solely rely on the platform’s top-line numbers, such as CTR or Impressions, as these can be very deceiving when high funnel awareness channels get layered into the mix. Track key metrics like CPA (cost per acquisition), ROAS (return on ad spend), and LTV (lifetime value). More importantly, track your business results. If your real-world sales data doesn’t align with what the AI is optimizing for, step in. Platform metrics only tell one side of the story when it comes to campaign performance measurement. You can be optimizing for an amazing return on ad spend (ROAS), but the product or service you are selling is not at all profitable. Likewise, the platform may try to avoid selling a product or service that has a low ROAS, but is highly profitable or drives volume sales for you. Machines are only as smart as the data they are being given and the other inputs being provided to them. 
• Use a Hybrid Approach – Keep some campaigns on manual or semi-automated strategies as a baseline. This gives you a point of comparison if you suspect the automated placements are underperforming.

Future Implications and How to Prepare

As AI/machine learning continues to advance, expect Google and Meta to place even greater emphasis on fully automated campaigns. The days of hand-picking audiences or keywords, controlling bids in detail, and choosing placements independently are numbered.

What does this mean for marketers?

• We will spend less time on manual optimizations and more on creative direction, data analysis, and strategic planning.
• Success will hinge on your ability to interpret AI-driven results and identify where the system’s algorithms might be wasting spend.
• Ongoing education is more important than ever. Platform changes and new features are coming fast and more frequently. Whether through platform certifications, webinars, or industry blogs, it will be crucial to stay ahead of new features and policies.

AI-driven campaigns undeniably offer incredible benefits, from real-time data processing to freeing up resources for higher-level strategy. However, if left unchecked, they can also inadvertently steer your ad budget toward underperforming or lower-quality placements.

The key is balance: Embrace the best of AI by letting it handle what it does best, which is optimizations at scale, while you keep a careful eye on where your ads appear and how they perform in the real world. By maintaining a hybrid approach and consistently reviewing your data, you can ensure that you’re leveraging automation to truly grow your business, rather than simply funding the ad platforms’ next revenue stream.