Top PPC tactics to try for all levels of AI adoption

Sometimes it can feel like the ability to create custom strategies has vanished. With the right workflows, though, you can leverage AI-oriented campaign types and settings while still bringing your human acumen to the table.

When people complain about a lack of control, they’re typically talking about three core PPC areas:

• Bidding: Setting the right bids to get enough leads for your conversion rate to come into effect while maintaining profitability.
• Creative: Balancing human creativity and brand style guides with the adaptability of AI surfaces.
• Reporting: Translating data into consumable reports while mitigating data gaps due to privacy.

Last time I put pen to paper on top tips to try, my advice focused more on technical skills. As PPC evolves into a collaboration between human ingenuity and AI efficiency, there’s an underlying question everyone will answer differently: How much do you trust AI to inform, assist, or own the process?

There’s no right or wrong place to fall on the AI trust spectrum, but there are performance caps that come with abstaining fully from AI-assisted workflows. Pure human ingenuity can still deliver gains, though scale will be harder to achieve. The following tips cover each major PPC area at each level of AI trust.

Here’s how I define each level of AI trust:

• AI-informed: You use insights from AI sources, such as chatbots and in-UI AI, but manually control campaign management and optimization.
• AI-assisted: You use AI as a partner for select campaign tasks, such as bidding and creative generation.
• AI-delegated: You allow AI to complete a task or optimize campaigns end-to-end.

Note: I’m a Microsoft employee, and the advice shared is intended to be platform-agnostic.

Bidding strategy

Bidding is one of the few areas where most advertisers agree it makes sense to delegate to AI, even if you’re only using simple automation through scripts or third-party tools. However, there are still use cases where manual bidding might hold sway, such as campaigns with lower search volume or those in niche industries. 

AI-informed: Harvest search term reports to identify cost-effective variants

Budgets should be set up to succeed, which usually means setting them at least 10X the anticipated CPC. This is because a 10% conversion rate is really good for nonbranded search, and if you can’t get at least 10 engagements on your ad, you’re banking on a better than 10% conversion rate.

While not every platform shares the same amount of search term data, there’s still a plethora of insights you can harvest. The most important insight is the ratio of close variants for a given keyword. This can be analyzed through the “matched-by” filter.

If you see a high rate of variants with stable, cost-effective bidding, that means the keyword or target you’ve chosen reflects a useful entry point into the auction. However, if there’s a lot of fluctuation in the bids and variants, then you might be accidentally getting pulled into expensive auctions. Choosing the right keyword champion to be your envoy in the auction, instead of bidding on every variant, ensures you can build enough conversions to focus budgets and achieve goals.

If you don’t see a lot of fluctuation in the variants, that means most people are used to searching in a given way, and there likely aren’t discounts to be had by changing up the keyword champion.

AI-assisted: Use micro-conversions to hit conversion thresholds earlier

Conversion-based bidding needs tracked conversions to function. Some brands won’t hit the ideal 30 conversions in 30 days to get the most out of conversion-based bidding. Rather than stunting their ability to get performance-oriented volume, they can leverage micro-conversions.

However, using micro-conversions without the right conversion value rules and correct ROAS goals/reporting expectations can cause even more problems. This is why it’s really important to have honest conversations with your teams about what each stage is worth and bring offline conversions into the mix when you can.

Both Google and Microsoft support integrations with companies like HubSpot to pass accurate lifecycle data back into the platform, which means the true value can eventually make its way into your bidding logic.

Here’s an example of how to think about micro-conversions, their respective values, and how this can help with AI ad types like PMax and AI Max settings in search and Shopping (in Google).

A commercial shipping company might get a lot of leads, but not all of them might meet the needed sales qualified lead (SQL) standards. To mitigate the influx of residential or smaller shipping requests, they might put a field in their multi-step form making it clear that anything below a certain threshold won’t be a good fit. When a prospect becomes an SQL, they represent at least $10,000.

The following conversions and conversion values might make sense for them:

• Begin form: $10
• Complete form: $100
• Proposal out: $500
• Deal closed: actual value from offline conversion import

By creating three steps along the path to conversion, this account will have an easier time hitting the needed conversion thresholds to use conversion-based bidding and PMax/AI Max, which both can drive incremental conversions.

AI-delegated: Test automated bidding against your current strategy

You don’t need to hand over the entire account to find out whether automated bidding can improve performance. Use campaign experiments to compare your current bidding strategy with a conversion-based alternative, keeping the audience, creative, and conversion goals as consistent as possible.

Before launching the test, decide which result matters most. That might be conversion volume, cost per acquisition, conversion value, or return on ad spend. Avoid judging the experiment solely on clicks or average CPC, since automated bidding may pay more for an individual visit when it predicts that person is more likely to convert.

Give the test enough room to produce a meaningful comparison, but continue checking whether both sides are optimizing toward the right conversions. AI can manage bids within the experiment while you retain control over the hypothesis, success criteria, and decision to adopt the strategy.

Creative strategy

AI-informed: Use PMax creative generation to audit how platforms understand your site

One of the most useful ways to understand how an ad platform interprets your business is to enter your domain into a Performance Max creative workflow and review what comes back.

You don’t have to use any of the generated assets. The real test is whether you agree with them:

• Do the headlines accurately explain what you sell?
• Do the images represent the right products or services?
• Does the suggested language reflect the queries and customers you want?

If the output consistently misses the mark, the problem may not begin with the AI creative generator. Your landing page may be using unclear phrasing, sending mixed visual signals, or failing to make the value proposition easy for systems and people to understand. Treat the generated creative as a diagnostic, not just a shortcut.

AI-assisted: Adapt proven assets and generate new variations

Every ad platform can suggest headlines, descriptions, images, and other assets. These suggestions often draw from your landing pages or existing ads, though some may include newly generated content.

Use AI as a creative partner to adapt those assets, explore different tones, or draft complete variations. You can expand your pool of promising creative without rebuilding every asset from scratch while still reviewing each suggestion for brand fit and accuracy.

AI delegated: Let platforms adapt creative for dynamic AI surfaces

Most ad platforms include settings that allow them to create or fully adapt content within defined brand guidelines and constraints. This approach can be particularly useful on AI surfaces, where conversations shift quickly and a fixed path from keyword to ad to landing page may feel disconnected from the user’s needs.

Tools such as text customization within AI Max can tailor the message and select a more relevant landing page for the conversation. Similar capabilities in Performance Max can also extend into visual creative. Set clear brand safeguards, monitor the output, and give the platform room to assemble an experience suited to the moment.

Reporting strategy

AI-informed: Use manual analysis to test query and audience alignment

Manual analysis forces you to process the information instead of accepting a summary that may flatten the most important differences.

One useful exercise is to compare the search terms that trigger your ads with the grounding queries and topics associated with how AI experiences discover or reference your brand. You can find grounding queries in free tools like Microsoft Clarity’s Topic Insights. 

Look for shared themes, meaningful gaps, and differences in how people express the same need. It’s important to note that paid search campaigns are increasingly finding themselves in AI experiences like AI Overviews, so be prepared for search terms to reflect shifts in consumer behavior.

Then segment those patterns by dimensions such as device, audience, or campaign. The goal isn’t to prove that one match type maps perfectly to AI discovery. It’s to understand whether your paid-search targeting and the language associated with AI discovery reflect the same customer needs.

AI-assisted: Use visualization tools to make insights easier to process

Visualization can help stakeholders absorb important insights and help you process the information more quickly. Start with the reporting and visualization features in each ad platform, then bring the data into tools such as Looker Studio or Power BI when you need a more flexible view.

Model Context Protocol (MCP) connections can make this workflow faster by letting you query raw data with your own prompts rather than relying only on platform-specific reporting rules. You remain responsible for the analysis while AI helps organize and present the information.

AI-delegated: Ask platform AI assistants for rapid account analysis

Platform AI assistants can be useful when you need a quick first pass on account performance. Ask a defined question, such as what changed most significantly, which campaigns contributed to the change, or where spend increased without a comparable increase in the outcome you value.

This can help you prepare a stakeholder update or identify the area that deserves your attention first. However, a fast answer isn’t automatically a correct or complete one. Check the date range, filters, conversion definitions, and supporting data before sharing the conclusion.

Delegating the first pass frees you to focus on strategy, prioritization, and action. The assistant can help identify what happened. Your expertise is still required to determine why it matters and what the account should do next.

Choose the right level of AI trust for each task

PPC management won’t be a choice between doing everything manually and handing the account to AI. The real skill will be knowing which tasks deserve your direct attention, which ones benefit from machine assistance, and which ones can be delegated within clear safeguards.

You may choose a different trust level for bidding, creative, and reporting. You may also move between levels as the account gains better data, clearer brand standards, or stronger measurement.

AI can process more signals, generate more variations, and summarize information faster than a person can. It can’t decide what your business values unless you provide that context. The practitioners who get the most from AI won’t be the ones who give up the most control. They’ll be the ones who are most deliberate about where that control belongs.