Google Ads strategies in 2026

It is still early in 2026 and, as Bob Dylan put it, you better start swimming or you will sink like a stone, for the times they are changing.

That is an important caveat (anything below can and likely will change).

Strategies are emerging, shifting and sometimes unravelling within weeks. AI overviews are appearing in more search journeys. AI search integration is becoming less of a side project and more of the direction of travel. What worked neatly in 2023 or even mid 2025 now feels… unsettled. 

If you manage Google Ads accounts for a living, you have probably felt it. The goalposts are moving. 

Quietly in some places, dramatically in others. 

As someone deep in Google Ads management within a digital marketing agency here in (a rather rainy) Oxfordshire, I have found myself questioning long-held beliefs. Not because they were wrong at the time, but because the environment around them has changed. Let us start with the old battleground.

The match type conundrum

In the early days our understandng of match types were simple. 

• Broad match was chaos expansive, sometimes unpredictable, but useful for discovery
• Phrase match was control with flexibility.
• Exact match meant exact. 

The strategy was clean. You built tightly themed ad groups, leaned heavily on exact and phrase, and layered in negatives with care. Broad match was often used as a learning tool or placed into controlled experiments, rather than forming the backbone of performance campaigns. 

Then came the infamous suggestion.

A Google representative or auto recommendation gently nudges you to convert to broad match. You stare at the screen thinking, “Sure, if I want to open the floodgates.” You picture search terms spiralling out of control. Irrelevant queries creeping in. Questionable conversions. Budget drifting into places you would never have approved manually. Most of us have had that reaction. 

But here is the uncomfortable truth.

Exact match is no longer exact. 
Phrase match is no longer what phrase match used to be. 

Google has been clear about its direction. Keywords are signals. Intent is the priority. And broad match, powered by audience data and machine learning, is positioned as the intent play. 

Google openly shares that broad match can help with AI visibility, whereas phrase and exact may not benefit in the same way. If search is moving further towards AI-assisted results, we need to pay attention to that. 

Broad match has evolved

Broad match in 2026 is not broad match in 2016. 

It is now heavily tied into: 

• Smart bidding
• Audience signals
• Conversion data
• AI-assisted intent modelling 

Used recklessly, it can still burn the budget. Used strategically, however, it can surface demand you would never have captured through manual keyword expansion. 

Many PPC managers, myself included, are testing a new 2026 AI-friendly hybrid structure.

The AI-friendly Google Ads strategy

The structure many are now testing is as follows:

You build a small, precise pool of high intent exact match keywords. These represent your core commercial terms. They anchor performance and give you clean, measurable signals. 

Alongside them, you introduce a carefully selected set of broad match keywords, often longer tail and aligned to themes rather than rigid phrases. 

The result is a blend of:

• Focused visibility from exact match
• Intent expansion from broad match
• Control through strong negative strategies
• Data fuel for automated bidding

A strong negative list is not optional in this structure. It is foundational. Broad match will explore. That is its job. Without disciplined exclusions, it will explore in the wrong directions. 

It's about negative list maintenance 

Ongoing negative maintenance becomes just as important as keyword expansion. Search term reviews, regular pruning of irrelevant queries, and tightening thematic boundaries are what keep performance stable. This is not a set-and-forget model. 

It demands active management. It feels uncomfortable at first. You have to loosen your grip, but only within defined guardrails. 

So far, from our own tests, it is too early to call definitive winners. Performance patterns are still forming. However, across LinkedIn and YouTube, experienced PPC professionals are sharing genuine success stories with this approach. 

The pattern is consistent. When broad match is paired with strong conversion tracking, clean data, disciplined exclusions, and continuous negative list refinement, it performs very differently to the horror stories of old.

Google cares about intent

There is another shift happening here. 

For years, PPC management was about refinement. You cast a small to medium sized net. You analysed search terms obsessively. You tightened, trimmed, refined and repeated. 

Now it is often about casting a wider net and controlling via exclusions rather than inclusions. 

Negative keyword strategy is becoming more important than ever. Account structure still matters, but machine learning is doing more of the heavy lifting in query matching and bidding decisions. 

For businesses investing in PPC through a digital marketing agency, this means the conversation is changing. It is less about micromanaging every search term and more about:

• Ensuring conversion tracking is watertight
• Feeding the algorithm clean, meaningful data
• Structuring campaigns to support intent modelling
• Auditing exclusions regularly and decisively

In other words, the fundamentals still matter. They just sit in different places. 

If your tracking is weak, broad match will amplify the problem. If your data is strong, it can amplify opportunity.

The rise of AI efficiency 

One of the more interesting developments has been Google’s AI assistant within the platform. 

I will admit, I was sceptical. However, in practice, it has been surprisingly useful for: 

• Reviewing campaign performance summaries
• Identifying anomalies faster
• Spotting trends across date ranges
• Highlighting shifts in search behaviour

It is not replacing human analysis. But it is accelerating it. 

In an environment where things are changing weekly, sometimes daily, speed of insight matters. If you are running multiple accounts, this layer of assistance can reduce time to diagnosis significantly. 

For agencies delivering PPC alongside UX, development and digital strategy services, that time saving translates into more time spent on strategic thinking rather than manual reporting.

AI Max PMAX and the march towards automation

Performance Max was an early signal. AI Max reinforced it. 

Google’s direction is clear. The platform is moving towards a more automated, semi-autonomous ad creation and management experience. 

Creative assets are mixed and matched dynamically. Audiences are inferred. Queries are broadened. Bidding is automated. 

For some advertisers, that loss of granular control feels uncomfortable. For others, it unlocks scale that would be impossible manually. 

The important thing is not to resist automation outright. It is to understand where automation performs well and where human oversight still matters. Creative messaging, landing page experience, conversion strategy and business context are not things the platform fully understands. 

That is where experienced PPC managers still make a difference (wink wink)

What this means for 2026 and beyond

If you are managing Google Ads in 2026, here is my advice. Keep your fingers firmly on the pulse. 

Test, but test with structure. 
Adopt automation, but do not abandon accountability. 
Strengthen your tracking before you broaden your targeting. 

The pre AI era of PPC was about tight control and steady refinement. The current era is about intelligent expansion supported by data discipline. 

If you want lower spend, stronger visibility and better return, the path forward likely includes:

• Strategic use of broad match
• Clean exact match anchors
• Relentless negative keyword hygiene
• Advanced conversion tracking through GA4 and GTM
• Willingness to experiment with AI-driven campaign types

As a team that is actively testing and shaping AI-led marketing strategies, we are seeing this shift play out across every sector we work in. The accounts that lean into experimentation and data discipline are the ones moving ahead.

What happens next for your Google Ads strategy

If your current account structure is still built around tight match type control and manual refinement alone, it may be time to reassess. The platform is evolving quickly, and the advertisers seeing momentum are the ones testing new combinations of automation, intent modelling and disciplined tracking. That does not mean abandoning fundamentals; it means repositioning them where they have the most impact. 

We are continuing to test, challenge and refine these approaches across live accounts, not as theory but in practice. If you are reviewing your paid search performance and want an honest perspective on how AI-led strategies could fit into your setup, we are always open to a considered conversation.