5 best ways of PPC campaigns

By 2026, AI will no longer be something marketers argue about. It shapes almost every aspect of digital marketing and creativity.

Because the human brain processes visuals much faster than text, video ads are becoming more valuable and effective, especially as creative costs continue to drop.

The question is no longer whether PPC teams should use AI in video marketing.

It’s a way to use it to develop better results, produce stronger creativity, and avoid problems like misalignment and management gaps that can undermine performance.

Why AI acquisition alone no longer drives PPC performance

About 90% of advertisers now use generative AI to create or version video ads, according to IAB data.

Discovery, however, is not the same as performance.

The difference between winning and losing campaigns on Google ads, especially YouTube, is no longer defined by manual bidding strategies.

It depends on who gives the algorithm the strongest input.

Ad platforms have shifted from keyword-based logic to intent-driven AI recommendations.

Marketers who still try to manually control every placement are competing with systems that process millions of signals per second.

Here are five best practices for using AI in video PPC campaigns to improve performance and deliver high-quality signals.

1. Get rid of the complete cut of material libraries

Historically, PPC video production followed a TV-style workflow: script, shoot, edit, enhance, and publish one “perfect” 30-second spot.

In the era of Performance Max, that approach has become a liability.

AI-driven campaign types are not designed to work with a single finished video.

They work best when provided with a library of assets that can be dynamically combined based on the user’s device, intent, and behavior.

Instead of uploading a single video, advertisers need to provide building blocks for AI that can assemble itself.

• Hook: Three to five different six-second opening clips, including preview, text-heavy, and UGC-style options.
• Body: Multiple value propositions, such as speed, price, or quality.
• CTA: End cards are varied, ranging from gentle notifications to direct conversion requests.

This works because Google’s AI may determine that one user browsing Briefs late at night converts better to a UGC-style hook with a CTA “Learn more,” while another viewing a tech update on desktop responds better to a polished product demo with a “Buy now” message.

Given only one video, AI’s ability to personalize the experience is very limited.

Google’s move to formats like Direct Offers shows where this trend is headed.

2. Substitute keywords for orchestration

A keyword is no longer a strong trigger for video ads.

On platforms like YouTube, keywords now serve primarily as markers that help AI understand the general body of the audience an advertiser wants to reach.

Google continues to push advertisers into Demand Gen and Video View campaigns, which rely on similar segments and search terms instead of exact match targeting.

When targeting is left completely open, AI systems tend to work towards the path of least resistance.

That often leads to low-quality placements, such as children’s channels or accidental clicks on mobile apps. Marketers need to continuously target the target.

• Bad keywords matter: In an AI-driven environment, telling the system who doesn’t have access is often more powerful than specifying who should.
• Investing in first-party data: Upload a list of high value customers and select them as the main signals. This pushes AI to find users who are like top customers, not just recent site visitors.

Dive Deeper: From Video Action to Demand Gen: What’s New in YouTube Ads and How to Win

3. Train the algorithm with value-based conversion data

The biggest mistake PPC managers make with AI-driven video campaigns is feeding weak conversion signals to the algorithm.

If a video campaign is configured to “Increase conversions” and conversions are burning from normal page views or unqualified leads, the AI ​​will actively seek out more users who click and bounce. It optimizes volume, not value.

To make AI work for video, marketers need to use offline conversions and advanced conversions.

• Step 1: A user clicks on a video ad and submits a lead form.
• Step 2: CRM leads the way, like fit versus junk.
• Step 3: Qualified status is returned to Google as a conversion event.

Preparing qualified leads instead of raw submissions trains AI to ignore low-quality signals and prioritize users with genuine purchase intent.

This method is important for increasing the use of video without increasing the cost of customer acquisition.

4. Accept the lift rate in addition to the last click attribute

AI-driven video formats, especially YouTube Short, are difficult to evaluate using traditional attribution models.

A user might watch a video ad during a commute, remember the brand, and search for it directly on a laptop days later.

Legacy attribution models, such as last click, give all the credit to the product search campaign and are absent from the video ad that created the demand.

When video budgets are cut because the return on ad spend appears to be low, search volume for the product tends to drop soon after.

Marketers should go for media mix modeling (MMM) or, for a simpler approach, monitor consistency of direction.

• Examination: If video usage increases by 20%, does the compounded CPA remain stable while the total revenue increases?
• Metric: Shift the focus away from the change of perspective, which can be up, and to the uplift. Google’s lift measurement tools enable holdout testing that separates audiences into visible and invisible groups to show the true impact of video campaigns.

Dig deep: Why reach is the only metric that proves true marketing impact

5. Understand that most users start with mute

Despite the rise of audio-driven trends, the majority of video consumption, especially in the acquisition phase, occurs with the audio turned off or at low volume.

AI tools can automatically generate captions, but effective video art goes beyond subtitles. The visual stage must convey the message clearly without relying on sound.

Review video ads using an AI analysis tool or by watching them silently.

In the first three seconds, the viewer must be able to answer three questions:

• What’s going on? Product or product visibility.
• Whose? Clear demographic signatures.
• What did I do? A visual call to action.

If the AI ​​cannot clearly see the brand or product within the first 25% of the video frames, the performance of the product promotion will suffer.

Pre-screening intelligence with AI-based visualization tools helps ensure that product assets are prominent enough to be properly planned and delivered.

PPC is about construction

The role of the PPC manager has changed.

Advertisers are no longer pilots making constant bid adjustments. They are architects who design the environment in which AI systems operate.

In 2026, the advantage will be in teams that prioritize creative input and data quality.

Building modular assets and closely managing the signals the algorithm learns from will make AI video marketing one of the most dangerous approaches in the marketing stack.

Managing AI-driven video like a traditional display campaign simply trains the system to spend the budget with a measurable return.

Start by testing your signals to understand what campaigns are optimized for.

Decide whether you are driving towards deep funnel actions, such as a purchase or qualified lead, or just preparing for vanity metrics.

Next, refine the creative by identifying the best performing still image and use the AI ​​video generator to turn it into a six-second bumper that can be tested and scaled across all video placements.

No matter how AI changes, video remains the most popular format.

Planning carefully and maximizing the tools available will be critical to winning with video advertising.