When to trust Google Ads AI and when you shouldn’t

Google Ads mostly runs on AI now – and that’s both a blessing and a curse. 

Some automations can boost performance, while others create blind spots that cost you money. 

The real challenge is knowing the difference. 

This guide breaks down exactly when you can trust Google’s AI and when you need to step in.

How Google Ads became an AI-first platform

Google Ads has changed. What was once a platform of manual bidding and full control is now driven by artificial intelligence, automation, and machine learning. 

(For simplicity, we’ll use these terms interchangeably to describe the systems powering today’s campaigns.)

This shift brings efficiency and scale – but also a problem: you’re being asked to trust a system you can’t fully see or control.

Julie Bacchini, PPC expert and President of Neptune Moon LLC, describes it as a constant balancing act:

• “Balancing is the biggest challenge for PPC pros when it comes to Google Ads AI. We have to balance where it helps, and where it creates data black holes. We have to balance client and stakeholder desire to utilize every perceived AI advantage, with keeping a human brain in the process.”
• “AI alone can’t compete with an experienced PPC brain. It can help when applied smartly, but blindly adopting it can be a disaster. That’s why PPC professionals are still very important to the success of most Google Ads initiatives.”

The lack of transparency creates a dilemma for search marketers: 

• How do you hand over budget and strategy to an AI that might optimize for outcomes you don’t want? 
• Are Google’s “recommendations” really in your best interest – or designed to increase spend?

The answer isn’t blind trust or total rejection. It’s learning the nuances of Google’s AI. 

Below is a framework to evaluate which features you can trust and which demand hands-on control.

High-trust automation: Features you can (mostly) set and forget

These features in Google Ads are generally reliable and beneficial, often improving efficiency and performance in ad accounts.

They’re based on solid, historical account data and have a clear objective that aligns with an advertiser’s goals.

Automated bidding strategies

Google’s Smart Bidding strategies – such as Maximize Conversions, Target CPA, and Target ROAS – are the cornerstones of this category. 

When given sufficient conversion data (at least 30 conversions in the last 30 days), these systems can:

• Optimize bids at the auction level.
• Consider thousands of signals a human simply can’t process in real time.

When to trust

Use these strategies when you have a consistent conversion history and a clear performance goal (e.g., maintain a specific cost-per-acquisition or return on ad spend). 

They excel at maximizing performance within a defined budget.

When not to trust

Avoid them on brand-new campaigns or in accounts with very low conversion volume. 

The AI needs a “learning phase” and sufficient data to work effectively.

When to use caution

You should also approach automated bid strategies with caution in industries where auction dynamics are volatile or conversion cycles are very long.

In the legal industry, for example, a single high-value case can skew conversion data, since the time from click to a closed case worth over $1 million may take months or even years. 

The long lead time and infrequent, high-value conversions make it difficult for AI to learn and optimize effectively.

The same applies to home services – especially emergency work like water damage clean-up after a storm – where a competitor with a massive budget can suddenly spike CPCs, throwing automated bidding into disarray.

In these cases, a manual or hybrid bidding approach offers more control and a better read on the market.

Automated rules and scripts

These are custom automations you create to perform specific tasks. 

While they don’t use AI in the way Smart Bidding does, they are a powerful form of automation that many paid search professionals rely on daily. 

Since you define the parameters, you have full control. 

For example, you can create a rule to pause campaigns on a certain date or a script to send an alert when your budget is about to be exhausted.

When to trust

Always. You are in the driver’s seat. Control the trigger and the action.

This is the most transparent and predictable form of automation.

When to double-check

Your trust is in your own code or setup, but not necessarily in the platform’s flawless execution. 

Always double-check. These automations can go wrong for two key reasons: 

• Human error in the coding or setup.
• A technical failure within the Google Ads platform itself. 

To ensure they’re working as intended, get into the habit of reviewing your run logs and performance data regularly.

Dig deeper: Leveraging generative AI in ad scripts for Google Ads optimization

Moderate-trust automation: Use with caution

This is where things get a bit more nuanced. 

These AI features are designed to “help” but can be overzealous and lead to unintended consequences if not carefully monitored. 

The key here is to understand the trade-off between convenience and control.

Recommendations and auto-apply

The Recommendations tab provides suggestions to improve your account.

The Auto-apply feature is a separate, dangerous function that implements these suggestions without your review. 

While the recommendations themselves can be helpful, they are often designed to increase your spending.

When to trust

Only for specific, data-backed suggestions, like fixing broken landing pages or identifying duplicate keywords (after a manual check).

They can be a good starting point for a manual audit and manual implementation.

When not to trust

Avoid blanket suggestions like “Add broad match keywords.”

And never use auto-apply. It gives Google carte blanche to change your account without review.

Dig deeper: Top Google Ads recommendations you should always ignore, use, or evaluate 

Responsive search ads (RSAs)

RSAs combine the headlines and descriptions you provide, using Google’s AI to serve the most relevant ad for each user.

They’re now a core part of ad creation.

When to trust

RSAs are a massive time-saver for ad testing. 

They are excellent for identifying the highest-performing ad combinations, which you can then use to inform your messaging. 

The automation is contained within the ad itself.

When not to trust

If you have strict brand messaging or legal disclaimers that must appear in a specific order. 

Without pinning, you risk your headlines or descriptions appearing in an undesirable combination.

Automated ad assets

This is a core component of Google’s automation, often on by default. 

Google automatically creates and displays assets like dynamic sitelinks, structured snippets, and callouts based on your landing page content.

When to trust

When you have a simple business model and need to quickly add more robust information to your ads. 

These features are designed to fill in the gaps and save you time.

When not to trust

You must check these regularly. 

If your website content is not tightly controlled, the AI might pull irrelevant or misleading text. 

Always review these assets and disable any that are inappropriate.

Dig deeper: How Google Ads’ AI tools fix creative bottlenecks, streamline asset creation

Nuanced automation: Test and monitor

This is the new middle ground that requires a strategic approach. 

These features offer powerful automation but with enough transparency and control to be valuable tools for finding new opportunities. 

The key is to run them as a test and closely monitor their performance.

Dynamic search ads (DSAs)

DSAs are a powerful keyword discovery tool. 

They automatically generate headlines and select landing pages from your site, but with critical controls. 

You can direct them to specific URLs or set rules. 

For example, target all product pages with a certain phrase in the URL or supply an exact list. 

This control keeps DSAs from “running wild.”

When to trust

Use DSAs to find new, long-tail keywords. 

Isolate them in their own campaigns with a dedicated budget to prevent them from going “off the rails.” 

Use negative keywords to exclude brand terms and other irrelevant queries. This turns a potentially risky feature into a reliable growth tool.

When not to trust 

If you have a small, static website or strict brand messaging, the automation can create irrelevant headlines, leading to wasted spending and brand issues if left unmanaged.

AI Max for Search

AI Max for Search is an opt-in feature within standard Search campaigns. 

It bundles several AI tools, including a “keywordless” option that uses your site content to find new queries, and it expands existing keywords with broad match logic.

When to trust

Test it on a new campaign first. AI Max is a great way to discover new search queries you may have missed. 

The new reporting features, which show a distinct “AI Max” match type, provide a level of transparency that makes this a worthwhile experiment for finding incremental volume. 

You can also opt out at the ad group level if you see a negative impact.

When not to trust

Do not apply this to your most profitable, proven campaigns immediately. 

It fundamentally changes how your keywords work, which could disrupt a well-oiled machine. 

It’s also not suitable if your website content is not highly relevant to what you’re selling.

AI image and video creation

Google’s suite of AI-powered creative tools allows us to generate visual assets from a simple text prompt. 

While this technology is evolving rapidly, it requires extreme caution and a highly selective approach.

When to trust 

Consider this an option only for non-branded or commoditized goods where a visual is more important than brand consistency. 

Think of products like:

• A simple salad.
• Generic coffee beans.
• Household cleaning supplies. 

These tools are valuable for smaller businesses that lack the budget for a professional photoshoot and can benefit from a quickly generated, unique visual. 

Even so, the human filter remains essential.

When not to trust

Do not use this tool for any brand-centric products, logos, or services where visual integrity is paramount. 

The AI’s output can be unpredictable and may lack the specific nuance required to build and protect a strong brand. 

It also poses potential legal risks, as the generated content could inadvertently infringe on copyrights.

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Ecommerce automation: The feed is king

For ecommerce advertisers, the Merchant Center is where you hand over a significant amount of control to Google’s AI. 

The trust you can place in the system is directly tied to the quality of your product data feed.

Product feeds (high trust)

The product data you upload to the Google Merchant Center is the foundation of Shopping campaigns. 

The quality of your feed directly impacts the performance of your automated campaigns.

When to trust

When you have a clean, comprehensive, and up-to-date feed that adheres to Google’s specifications.

When you are the one controlling the data you feed into the system, you can trust what the system does with it.

Automated feed updates (moderate trust)

Google offers automatic item updates, which use your website’s structured data to update product information – like price and availability – in your feed.

When to trust

When used as a safety net, it’s a good way to catch minor discrepancies in real time.

This prevents your products from being disapproved if the price or stock status changes on your website.

When not to trust

Don’t rely on it as your main feed update method. 

It’s no substitute for a robust, scheduled feed – and using it alone can cause major errors and disapprovals.

Low trust automation: Proceed with extreme caution

These are the black-box features where you give up most of your control and transparency in exchange for Google’s promised performance.

Performance Max campaigns

Performance Max is Google’s most automated campaign type. 

A single campaign runs ads across all of Google’s channels (Search, Display, YouTube, Gmail, etc.) with very little insight into what’s working. 

While Google has recently made strides in providing more transparency by including channel reporting, you are still lacking the granular control to act on that data. 

You can now see where your budget is going, but you cannot directly adjust bids or opt out of specific channels that may be underperforming.

When to trust

PMax can work well for ecommerce or lead gen accounts with solid conversion data – efficiently finding new audiences and entering auctions across all Google channels. 

It’s best used as a supplement to core search campaigns that still need human oversight.

Expert Insight: Questioning best practices

As PPC expert Menachem Ani recently pointed out in a LinkedIn post, the best insights sometimes come from doing what Google tells you not to do. 

When PMax launched, the conventional wisdom was to “give it everything” – all your creative assets, images, and videos.

However, in testing, Ani discovered that for some clients, performance actually improved when they ran “smart shopping style” campaigns with zero creative assets. 

The algorithm, stripped of visual “noise,” was forced to focus purely on the product data and user intent, behaving more like the Smart Shopping campaigns that previously worked. 

This serves as a powerful reminder that questioning the official playbook is a core part of a paid search professional’s role.

When not to trust

If you need granular control, detailed reporting, or have a limited budget.

For lead gen in particular, you must have conversion tracking in place. 

Without a clear and consistent flow of conversion data, PMax is virtually unusable. 

The algorithm needs to know what a valuable lead looks like. 

Without a reliable tracking system to tell it, PMax will simply optimize for any form submission, leading to a high volume of low-quality and maybe worthless leads.

Smart campaigns

These oversimplified campaigns target small businesses, offering a bare-bones interface with minimal control. 

Essentially a rebrand of Google Ads Express (launched in 2011), they’re worth noting because new users are often funneled into them from the start (even if this article’s readers aren’t the target audience).

The simplified setup bypasses complex options, but at the cost of transparency and control.

When to trust

Never. 

These campaigns are designed for new advertisers with absolutely no PPC experience and a very limited, fixed budget as a kind of trial run.

A paid search professional would never knowingly use or recommend them. 

If a client comes to you with an existing Smart Campaign, it’s a clear signal that they need your expertise immediately.

When not to trust

Always. 

This is where your dreams of reporting on keyword performance and proving ROI go to die. 

The lack of data makes it impossible to show that your success wasn’t just a happy accident, and for a paid search professional, that’s simply not an option.

Inherent limitations of AI

Google’s AI operates within the confines of the data and objectives it is given. 

It lacks the contextual understanding and strategic foresight that an experienced professional provides.

Lack of business context

The AI does not understand your profit margins, inventory levels, or broader business goals unless you provide that data. 

It will optimize for the given metric (e.g., conversions) without considering the strategic value of each conversion.

Dependence on historical data

Machine learning models are trained on past performance. 

In situations with limited or volatile data, such as a new product launch or a sudden market shift, the AI’s predictions can be unreliable.

Potential for misinterpretation 

The AI may optimize for unintended outcomes. 

For example, a Maximize Conversions strategy could drive a high volume of low-value leads if not properly constrained with value-based bidding.

Why AI still needs you

The future of paid search isn’t pure AI – it’s collaboration. 

AI is a powerful tool for efficiency and scale, but it can’t replace human strategy and oversight. 

The key isn’t blind trust or total rejection, but understanding AI’s strengths and limits.

• Smart Bidding excels with consistent data, while volatile markets like legal or home services still demand human judgment. 
• Tools like DSAs and AI Max become discovery engines when guided by a PPC pro, while black-box systems like PMax still require human inputs and interpretation.

Your success will depend on being the human in the loop. 

The best PPC advertisers let AI handle the heavy lifting while they focus on what only people can do: 

• Add business context.
• Set guardrails.
• Anticipate consequences. 

The goal isn’t to push buttons perfectly. It’s to be the strategist who knows when to lean on AI, and when to question it.