PPC advice in 2025 leans heavily on AI and shiny new tools.
Much of it felt credible. Most cost advertisers money.
Teams followed field narratives instead of business issues. The budget grew. Efficiency did not.
As 2026 begins, taking those beliefs forward ensures the same.
This article breaks down three PPC myths that looked smart in theory, spread quickly in 2025, and often drove bad decisions in practice.
The goal is simple: reset your priorities before repeating costly mistakes.
Myth 1: Forget manual pointing, AI does it better
We’ve seen this claim everywhere:
AI is surpassing humans in guidance, and manual structures are past.
Combine campaigns as much as possible.
Let the AI run the show.
There is some truth to that – but only under certain circumstances.
AI performance is completely dependent on input. No volume means no reading. No learning means no results.
A more dangerous version of the same problem is low signal quality. No business-level conversion signal means no logical optimization.
For ecommerce brands that feed purchase data back to Google Ads and generate at least 50 conversions per bid strategy each month, trusting AI for targeting can make sense.
In those cases, the volume and quality of the signal is usually sufficient. Simply put, AI likes scale and clear results.
That mentality quickly backfires on low-volume campaigns, especially those that target leads as the primary conversion.
Without enough high-quality conversions, AI cannot learn effectively. The result is not better performance, but automation without optimization.
How to fix this
Before completely handing over targeting decisions to AI, you should be able to answer “yes” to all three of the questions below:
- Are campaigns optimized against business-level KPIs, such as CAC or ROAS thresholds?
- Are enough of those conversions being returned to ad platforms?
- Are those conversions reported quickly, with minimal delay?
If the answer to any of these is no, 2026 should be about a fundamental reexamination of PPC.
Don’t be afraid to go old school when the situation calls for it.
In 2025, I doubled the client limit by using the mirror match structure and setting up broad match keywords.
It went against existing best practices, but it worked.
The decision was based on historical performance data, shown below:
| A type of match | Cost per lead | Customer acquisition costs | Search to share an idea |
| Of course | €35 | €450 | 24% |
| A phrase | €34 | €1,485 | 17% |
| Broad | €33 | €2,116 | 18% |
This is a classic example of Google ads targeting leads and delivering exactly what it was asked to do: drive the lowest cost per lead across all audiences.
The algorithm is real. It does not care about bottom-line results, such as business-level KPIs.
By taking back control, you can direct money to high performing audiences that are not yet saturated. In this case, that meant the exact same keywords.
If you’re not comfortable with older architectures like mirror type matching – or even SKAGs – learning advanced semantic techniques is a viable alternative.
Those methods can provide a more controlled starting point without relying entirely on automation.
This myth is especially frustrating because it sounds so logical and spreads so quickly.
The claim is simple: more creativity means more learning, which leads to better auction performance.
In fact, it more reliably increases production costs than improves results – and often benefits agencies more than advertisers.
Creative volume only helps when ad platforms receive enough high-quality conversion signals.
Without those signals, most ads simply state multiple properties to be rotated. AI has nothing useful to learn from it.
Andromeda generated a lot of attention in 2025, and gave advertisers a new opportunity to engage.
In fact, Andromeda is one part of the Meta ad discovery system:
- “This section [Andromeda] you are tasked with selecting ads from tens of millions of candidates to a few thousand suitable candidate ads.”
That stop coincided with Meta’s broader pivot from narrative metaverse to AI. It worked.
But it has also led some groups to conclude that creative diversity is now necessary – more hooks, more formats, more variations, produced according to productive AI.
Similar to Google Ads’ push for automatic bidding, broad match, and responsive search ads, Andromeda has become the perfect reason to embrace Advantage+ targeting and Advantage+ creation.
Those methods can do well in the right circumstances. They are not universally reliable.
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How to fix this
Creative diversity helps platforms match messages to people and situations. That number is real. It’s not new either. The same basics still apply:
- Smart testing requires a strategy. Aimless testing wastes resources.
- Measurements should be planned in advance. Otherwise you are setting yourself up for failure.
- Business-level KPIs need to exist in sufficient volume to matter.
This myth most clearly backfires when resources are limited – budget, skills, or time. In those cases, platforms tend to rotate ads in a less signal-driven way.
When resources are constrained, CRO is the best use of your resources:
- Update tracking. More tracked conversions improve performance.
- Improve the customer journey to increase conversion rates and signal volume.
- Map high-margin products to support efficient spending.
- Explore new channels or networks using the budget saved from overproduction.
The pattern is consistent. The creative scale follows the signal scale, not the other way around.
Myth 3: GA4 and attribution are infallible, but sales mix modeling will provide clarity
Can you think of 10 marketers who believe GA4 is a great tool? Probably not.
That alone speaks to how Google mishandled the release.
As a result, many clients now say the same thing: GA4 does not match the data of the ad platform, and it feels reliable, and a “serious” solution should be needed.
More often than not, that approach leads to high costs and mediocre results.
Most brands simply don’t have the budget, scale, or sophistication required for MMM to generate meaningful insights.
Instead of adding another layer of abstraction, they can be better served by learning to use the tools they already have.
For most brands, the setup looks familiar:
- Media consumption is concentrated in two or three channels at most – Google and Meta, with YouTube, LinkedIn, or TikTok as secondary options.
- The business depends on a recurring but small customer base, which creates long-term weakness.
- Without that core audience, marketing doesn’t scale at all, if at all.
In those cases, MMM does not add clarity. Add translation.
With such limited channel coverage, the focus should remain on the essentials.
The challenge is not to model the complex, but to identify what is contributing.
How to fix this
The values below deliver greater value than MMM in these cases:
- Clearly differentiate from competitors.
- Increase margins, and even basic budgeting can move the needle.
- Build a strong data base, including tracking, CRO, and conversion pipelines.
- Separate channels or ad networks.
- Lock in smart action on real customer pain points.
- Optimize marketing performance wherever it happens.
MMM – like any advanced tool – becomes useful when complexity demands it. Not before.
If used too early, it replaces accountability with guesswork, not understanding.
The truth behind the myths
The common thread in all three tales is not AI, art, or math. It is misused.
Platforms do exactly what they are asked to do. They work well against the given characteristics, within the limits of budget and layout.
When business fundamentals break down, AI can’t fix the problem.
2026 is not the next rush. It’s about business and ops focus, coupled with efficiency, to grow profitably.
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