How does your brand prepare for AI-search advertising in 2026?

AI-search advertising is the next major shift in paid search: search engines increasingly use large language models to assemble results, which changes how ads are displayed, evaluated, and clicked. Brands that prepare structurally for this shift now are building a competitive position that will be very difficult for others to close later.

Key takeaways

• AI-search advertising requires clean conversion data, strong first-party signals, and flexible campaign structures.
• Conversational search queries are rising fast, making broad match and Smart Bidding increasingly relevant.
• Performance Max and Responsive Search Ads benefit most from AI-driven management.
• Brands without automated search term mining lose budget daily to irrelevant queries.
• AdBrains automates the full AI-search advertising infrastructure: from keyword incubation to server-side signal enrichment.

What exactly is AI-search advertising?

AI-search advertising refers to the combination of campaigns, bidding strategies, and optimisation systems where artificial intelligence forms the core of decision-making, both on the platform side and the advertiser side. Google Ads has deeply embedded its own AI through Smart Bidding, Performance Max, automatic ad combinations, and AI-generated recommendations. But AI-search advertising goes further than platform features alone: it also concerns how you as a brand organise your own data, structure, and signals so that AI can function at its best. That scope increasingly extends beyond Google too, as more brands begin advertising on ChatGPT to meet users directly within conversational AI interfaces.

Consider the rise of conversational search queries. Users increasingly type long, specific phrases rather than short keywords. According to Think with Google (2026), around 70% of search queries now carry conversational intent. That directly affects which keywords you use, how your ad copy is structured, and which landing pages you direct traffic to.

The foundations: data and signals in order

Why AI-search advertising is a priority in 2026

Key industry benchmarks

70%

of search queries now contain conversational intent (Think with Google, 2026)

Day 1

AdBrains automatically mines search terms and adds negative keywords from day one

4x

Independent AI agents verify every optimisation decision before execution

24/7

Automatic tCPA/tROAS adjustment without manual intervention

Before you even think about campaign structure or bidding strategy, the foundation needs to be solid. AI-search advertising depends entirely on the quality of signals you send to the advertising platform. Much of this groundwork overlaps with generative engine optimization, since both disciplines rely on the same clean, structured brand signals to perform well in AI-driven environments. Three foundations are essential here.

The first is reliable conversion tracking. If your conversion data is incorrect, your Smart Bidding algorithm accelerates at the wrong moments. In practice, we regularly see duplicate conversions, incorrectly attributed values, or missed micro-conversions when onboarding new clients. Server-side tracking via a dedicated sGTM container resolves many of these issues: you become less dependent on browser cookies and deliver richer, more reliable signals to the platform. Google itself states that Enhanced Conversions can deliver up to 5% more measured conversions for advertisers using server-side signals (Google Ads Help, 2026).

The second foundation is first-party data. Customer lists, buyer email addresses, segments based on purchase history: all of this can be used via Customer Match as an audience signal for Smart Bidding and Performance Max. The richer that data, the better the algorithm understands who your most valuable visitors are.

The third foundation is a clean account structure. Campaigns logically organised around themes or product categories, with clear ad groups and matching landing pages, give the algorithm a clear framework to work within. Account sprawl leads to conflicting signals and slower learning.

Campaign structure for AI-search advertising

The campaign structure that best suits AI-search advertising differs from the traditional, tightly segmented approach in several ways. The goal is no longer perfect control over every individual search term, but creating the right learning environment for the algorithm.

For e-commerce businesses like ToetsJeKennis.nl, which sells online exams and courses, a structure where product categories run as separate campaigns works well. Each campaign gets a Target ROAS (tROAS) aligned with the margins of that category. Ad groups within each campaign are broad enough to generate sufficient search volume for the Smart Bidding algorithm, but narrow enough to write relevant ad copy.

For lead generation, such as Clima-Active.nl generating quote requests for air conditioning and heat pump installations, the focus is on a healthy flow of qualified leads at an acceptable Target CPA (tCPA). Here it is essential that the conversion value of a lead is set correctly: a lead for a heat pump installation is worth considerably more than a request for a minor service.

Two campaign types deserve extra attention in 2026:

• Responsive Search Ads (RSA): Google automatically combines the best headlines and descriptions based on context and user behaviour. A high Ad Strength is not a guarantee of success, but a low Ad Strength is a clear signal that work is needed.
• Performance Max (PMax): This campaign type gives Google the most freedom and works best when you provide sufficient asset variety, strong audience signals, and a reliable conversion value. Without those three ingredients, PMax often underperforms.

Broad match keywords play a bigger role in AI-search advertising than they used to. Combined with Smart Bidding, broad match can capture search traffic you would never reach with phrase match or exact match alone. The risk is budget leaking to irrelevant queries; you manage that risk with automated search term mining and a strict negative keyword policy.

How AdBrains automates AI-search advertising

AdBrains built its own AI infrastructure from the ground up, designed around what AI-search advertising actually demands from account management. No third-party plug-and-play tools. A proprietary system that runs daily across our clients' accounts and steps in at every optimisation point where manual management breaks down.

At the centre of that system sits the multi-agent verification process. Before any optimisation action is executed, whether that means adjusting a tCPA target, adding a negative keyword, or pausing an underperforming ad group, four independent AI agents each assess the decision separately. The purpose is straightforward: stop impulsive adjustments triggered by temporary noise in the data, which is something manual management gets wrong with some regularity.

Automated search term mining is where clients tend to notice the difference first. Every day our AI works through all search terms that generated traffic. Irrelevant terms are added as negatives automatically. Terms that look promising but haven't been added as keywords yet go into our Keyword Incubator, a separate test campaign where new keywords run on a limited budget before being moved into the production campaign. For E-4motion.com, the webshop selling new electric folding bikes, this means queries around specific models or commute distances get picked up and tested systematically, without touching the main budget.

From search query to conversion: Keyword Incubator flow

Example: throughput in % per stage, starting point = 100%

Search terms detected

All relevant queries are captured

Incubator phase (test)

Filtered on relevance and quality

Promoted to production

Proven keywords go live

Conversions realised

Efficient budget allocation

28% of detected terms leads to measurable conversions

Automatic tCPA/tROAS optimisation adjusts bidding strategies daily based on live conversion volume and the margin targets we've set for each client. That's a different approach from the default Smart Bidding setting in Google Ads, where the advertiser decides manually when to move targets and by how much. Our system handles it proactively, working from a fixed rule set calibrated to the client type.

The RSA improvement system flags any ad copy carrying a POOR Ad Strength score and rewrites it automatically. Our AI looks at which headlines and descriptions are underperforming, then produces alternatives that fit the search intent of the audience in question. For HACCP-cursus.com, the output is ad copy that speaks directly to the food safety requirements people are actually searching for, rather than generic messaging that misses the mark.

Our server-side signal enrichment works through a dedicated sGTM configuration that strengthens the conversion signals sent to Google Ads. First-party client data is combined with Enhanced Conversions, so the Smart Bidding algorithm always has the fullest possible picture. Setting this up correctly requires per-client customisation; it's not a standard configuration you can copy and paste.

Keyword strategy in the AI era

The traditional keyword strategy, manually building and maintaining a detailed list of exact match and phrase match keywords, is losing ground. Keywords themselves are still relevant. What changes is how you work with them.

In an AI-search advertising environment, keywords function primarily as signals for the algorithm rather than hard filters. Broad match combined with Smart Bidding gives Google room to find the most relevant interpretation of a query, but that comes at a cost: you as an advertiser have less grip on which exact queries actually trigger your ad. A well-maintained negative keyword list is the most direct way to compensate for that.

A workable keyword approach for AI-search advertising rests on a few elements:

1. A core set of exact match keywords for your highest-value search terms, as an anchor for your campaign structure.
2. Broad match keywords for discovering new search intent, supported by Smart Bidding.
3. An actively maintained negative keyword list, ideally automated daily.
4. An incubation process for new keywords, preventing budget burn on unproven terms in your main campaign.
5. Regular analysis of search term overlap between campaigns to prevent cannibalism.

For LeroyBrouwer.nl, one of our lead generation clients, running an incubator means we surface dozens of new keywords every month that manual research would have missed entirely. They tend to be long and conversational. That is exactly the type of query growing fastest in 2026.

First-party data as a strategic foundation

Manual management vs. AI-search advertising

How the approach differs across key areas

Manual management

• Keywords tracked manually
• Bids adjusted weekly/monthly
• Search terms checked sporadically
• Ad copy refreshed periodically
• Signal loss due to cookies
• Reporting after the fact

AI-search advertising (AdBrains)AI approach

• Automatic keyword detection and expansion
• Daily tCPA/tROAS optimisation
• Daily search term mining
• RSA improvement based on Ad Strength
• Server-side signal enrichment
• Real-time insight and proactive action

In an environment where third-party cookies are increasingly unreliable, first-party data is the most valuable asset an advertiser has. Google Ads offers Customer Match, which lets you use your own customer data as an audience signal. This works best when your data is current, segmented, and of sufficient size.

In practice, this means your brand needs a systematic approach to collecting, storing, and activating customer data. A list of existing buyers can be uploaded as a Customer Match audience and used as a positive signal for Smart Bidding: the algorithm learns who buys and looks for similar users. For Performance Max this is even more direct: you explicitly provide audience signals based on your best customers, giving the system a head start.

Two practical applications worth highlighting:

• Remarketing audiences: Segment visitors based on behaviour, such as viewing a specific product page or abandoning a checkout, and serve them targeted ads with a specific message or offer.
• Lookalike targeting via PMax: Provide Performance Max with your best customer segment as an audience signal. The system then independently searches for new users who resemble your existing buyers.

Our audience management automation ensures PROD, Incubator, and RLSA audiences are created and maintained automatically every week. That saves time and guarantees your audiences are always current, so no outdated segment is steering the algorithm in the wrong direction.

Measuring in an AI-search advertising environment

Measurement becomes more complex as AI takes over more tasks. Google Ads reports on a growing number of AI-driven optimisations whose mechanics are not always transparent. Still, clear measurement principles help you maintain grip on results.

Use ROAS and CPA as primary KPIs, but also look at incremental impact. When using Smart Bidding it is tempting to only check conversions inside Google Ads. By also looking at revenue per channel and conversions in your own backend, you quickly see whether the campaign is genuinely driving growth or mainly harvesting existing customers.

Set clear rules for campaign pauses. Our strategy-switch system automatically pauses campaigns when conversion volume is too low for Smart Bidding to work reliably, and reactivates them once volume recovers. That prevents an algorithm without data from burning through your budget with no direction.

For a webshop like Elletens.nl, where order values vary, setting the correct conversion value is essential. If you give Smart Bidding a fixed CPA target when you should actually be optimising on ROAS, the algorithm pursues the wrong goal. Getting that alignment right is one of the first things we check with every new client.

Frequently asked questions about AI-search advertising

What is the difference between AI-search advertising and regular Google Ads?

AI-search advertising is not a separate product but a way of working: you place artificial intelligence at the centre of every decision, from keyword selection to bidding strategy and ad creation. Regular Google Ads campaigns also run on Google's platform, but in manual management the advertiser makes most decisions based on periodic analysis. AI-search advertising means both the platform (via Smart Bidding, PMax, and RSA) and the manager (via automated tools and proprietary AI systems) optimise continuously based on live data. This also raises the question of AI search impact on SEO, since the same AI systems driving these ads increasingly shape organic visibility as well.

Do I have to use Performance Max for AI-search advertising?

Performance Max is not mandatory, but it is the campaign type built most specifically for AI-driven management. PMax combines search, display, YouTube, Shopping, and Discovery in one campaign and lets the algorithm decide where budget is best spent. That works well when you have sufficient conversion data, provide strong assets, and supply good audience signals. For smaller advertisers with limited monthly conversion volume, it can be smarter to start with regular Search campaigns and Smart Bidding before moving to PMax.

How do I protect my budget from irrelevant queries when using broad match?

Broad match delivers the most reach but also the highest risk of budget waste on non-relevant queries. The most effective protection is a combination of daily search term mining, an active negative keyword list, and a Keyword Incubator structure. By analysing every day which search terms were triggered and which were irrelevant, you structurally limit the leak. Done manually this is time-consuming and error-prone. Automated systems like those at AdBrains handle this every day without manual intervention.

How long does it take for AI-search advertising to deliver results?

Smart Bidding typically needs a learning phase of two to four weeks, depending on conversion volume. Google Ads Help (2026) advises a minimum of 30 to 50 conversions per month per campaign for reliable tCPA optimisation. Performance Max has a comparable learning phase. This means you will see more variation in results in the first few weeks. Clean, stable conversion data significantly speeds up the learning process. In our practice, clients who start with a properly configured sGTM setup and Enhanced Conversions exit the learning phase faster than those starting with standard browser-based tracking.