Time to move Google Ads into AI: practical steps that work for B2B and B2C

For many years, keyword-based Google Ads search campaigns played a crucial role in digital advertising strategies – in many cases, they were the foundation of marketing tactics.
Today, the situation looks very different. The Google Ads ecosystem has moved into the era of artificial intelligence (AI) and automation: a 100% keyword-driven strategy is no longer the most effective or dominant approach. Most advertising budgets are now flowing into AI-optimized campaigns – from smart bidding strategies to Performance Max and next-generation search campaigns integrated with generative search.

Why did this happen?

• The search system now understands user intent much better than just the exact word combination;
  
  
• keyword matching has become more “liberal”, and AI models understand synonyms, phrases and context much more accurately
  
  
• a large part of the user journey has moved from “only search” into different channels: video, news feeds, apps, social networks.
  
  

The result: keywords are still important, but the era of keyword-only campaigns is ending. If you are still trying to control everything via match types and manual bid adjustments, you are competing with algorithms that see hundreds of times more and wider signals in real time.

Why AI campaigns beat keyword-only advertising

1. AI optimizes for the goal, not just the word

AI-driven campaigns answer the question “who is most likely to convert?”, not “which keyword should we bid on?”.

Modern search and Performance Max-type campaigns:

• use broad match, audience signals, website content and first-party data;
  
  
• automatically look for new, previously unseen queries and segments;
  
  
• optimize for outcomes (conversions, conversion value), not for the number of clicks.
  
  

In practice this means: instead of trying to come up with a thousand keyword combinations, you clearly define the goal (inquiries, sales, new customers), feed the system with data and let AI find the path.

2. One campaign – all channels (and the whole funnel)

Traditional keyword advertising lives only in search. AI campaigns – especially Performance Max – operate across the entire Google network:

• search
  
  
• YouTube
  
  
• Display Network
  
  
• Discovery
  
  
• Gmail
  
  
• Maps and more
  
  

This matters for both B2B and B2C:

• B2C: a user might see your product first on YouTube, then a few days later as a Display banner, and finally a search ad when they are looking for a specific solution. Your message follows them throughout the journey.
  
  
• B2B: decision-makers watch educational videos, read articles, visit conference websites – an AI-driven campaign can reach them even before the moment they type in an exact query in search.
  
  

Instead of a single “last click” campaign, you get full coverage of the buyer journey.

3. Better results with the same (or even smaller) budget

Case studies published in 2024–2025 show that:

• AI-driven campaigns consistently increase the number of conversions without worsening – and often improving – CPA/ROAS;
  
  
• optimization across multiple channels allows you to reach part of your audience more cheaply (for example, via video or display), while reserving more expensive search clicks for people who are already close to buying.
  
  

In short: AI often manages to do more with the same money, because it sees the full picture and shifts budget at a micro level to where it is most effective at that moment.

4. The systems learn with your data

The key change since 2024 is the importance of first-party data.

When you:

• import CRM conversions (real sales, not just form submissions)
  
  
• mark which leads are qualified
  
  
• pass customer value (LTV, revenue)
  
  

AI starts optimizing not for “who clicked” or “who filled out a form”, but for who actually brought in money. Both in B2C (average basket value, repeat purchases) and B2B (actual contracts, MRR, pipeline) this can be your optimization goal – as long as the data is sent back into the system.

This is the moment when the machine starts truly working for you, not for click statistics.

5. Less manual micromanagement, more strategy

AI campaigns take over the work that used to consume most of the time in marketing teams:

• constant bid adjustments;
  
  
• maintaining thousands of keyword lists;
  
  
• overly granular structures that today only get in the algorithm’s way.
  
  

In return, you get space for what really creates an advantage:

• a clear offer and positioning;
  
  
• high-quality content and creatives;
  
  
• improving the buyer journey (UX);
  
  
• data architecture (what you measure and how).
  
  

In other words, the machine executes the tactics, and the human sets up and oversees the strategy.

Performance Max and the new search: the new standard for B2B and B2C

Since 2024, Performance Max has effectively become the main Google campaign type in many accounts.

How does PMax work?

• You define the goal (sales, leads, new customers);
  
  
• you provide creative assets (copy, visuals, video);
  
  
• you upload audience signals and conversion data;
  
  
• the system itself chooses channels, audiences and combinations.
  
  

PMax for B2C

In a B2C context, PMax is particularly attractive because it:

• can use a product feed (Merchant Center);
  
  
• shows products both in search and in shopping format;
  
  
• relies on real purchase and basket data.
  
  

Here AI allows you to:

• automatically surface the most likely-to-sell products;
  
  
• prioritise new or higher-margin products (if you pass margin data);
  
  
• use dynamic remarketing without additional manual setup.
  
  

PMax for B2B

B2B marketers are often sceptical about PMax – they fear low-quality leads and “spam” form fills. This is solved not by turning AI off, but by giving it a better brief:

• importing only qualified leads as conversions;
  
  
• separating “low quality” and “high quality” inquiries;
  
  
• segmenting campaigns by funnel stage (demand generation vs lead generation).
  
  

Once AI “sees” which leads actually turn into deals, it starts looking for people like that – even if their search queries and behaviour differ slightly from your traditional “ideal customer profile” description.

Control and transparency: where does the human stay?

Yes, with AI there is a certain “black box” feeling. Instead of detailed keyword reports, you often see:

• performance at channel level;
  
  
• insights about audience segments;
  
  
• total number and value of conversions.
  
  

The human role is to:

• ensure AI does not burn too much budget on brand terms;
  
  
• clearly separate campaigns by goals (not throw everything into one PMax pot);
  
  
• constantly check whether you are measuring what actually matters.
  
  

AI is not an excuse to “disconnect from the account”. It is a signal to change focus: from clicks and CPC to business outcomes and data quality.

B2B and B2C: what changes when you bring more AI into your marketing?

B2C: scale and personalization

In B2C, AI works best where there is high volume, lots of purchases and fast iterations. Algorithms can aggressively test different creatives, audiences and channels because every day they collect enough data to learn.

That is why three things are especially important for B2C businesses:

• first, a clean and complete product feed, so that the system “sees” your full assortment and can show the right products to the right people;
• second, margin and basket-value data, so AI can optimize not just for the number of sales but also for profitability;
  
  
• third, a clear remarketing logic – who sees what and when, and which stages a person should go through before purchasing.

When these three elements are in place, AI effectively solves two classic B2C problems: how to allocate budget smartly when you have 1,000+ products, and how to manage remarketing across several countries or markets without burning out audiences and wasting budget on the same people.

B2B: narrow audience and quality control

In B2B advertising, traffic is smaller, leads are more expensive and the sales cycle is longer than in B2C. That means AI has far less data to learn from and, if you don’t give it additional information, it may optimize in the wrong direction. This is why first-party data is critically important in the B2B sector.

To get AI to look not just for a large number of inquiries but for the right type of customer, you need to take several steps:

• integrate your CRM and pass offline conversions, so the system understands which leads turned into real deals;
• 
  segment conversions by qualification level, so the algorithm can distinguish valuable contacts from noise;
• 
  and avoid allocating all budget only to the bottom of the funnel – AI also needs awareness signals to find new people outside your existing pipeline.

The practical conclusion is straightforward: a B2B company that “tightens” AI and leaves it only a few brand keywords without passing lead quality data usually does not see a breakthrough. Meanwhile, a B2B company that feeds AI real sales data and lets it explore more broadly has a chance to lead its niche and reach audiences that traditional campaigns would never touch.

Recommendations for 2026 planning

1. You don’t need endless keyword lists

• Clean up your structure: fewer, but clearer campaigns;
  
  
• keep keywords only where they are truly needed (high-intent, brand, very specific B2B terms);
  
  
• give the rest of the search space to broad match + AI optimization.
  
  

The goal is not to have as many keywords as possible, but to have a strong, clear signal for the algorithm.

2. Invest in data

• Fix your conversion tracking (what exactly counts as success?);
  
  
• separate microconversions (for example, PDF downloads) from real goals (inquiry, purchase, demo booking);
  
  
• integrate your CRM: let AI see which leads turned into contracts and which did not.
  
  

Without this, AI will work for “volume”, not for quality.

3. PMax as channel conductor

• Define clear PMax goals (for example, “new customers, not remarketing”);
  
  
• use audience signals (customer lists, similar audiences, behavioural segments);
  
  
• prepare separate asset groups for different segments (B2B segments / B2C personas / markets).
  
  

Don’t treat PMax as a “set and forget” tool. See it as a way to select and orchestrate creatives and signals, and let it work at channel level.

4. Reserve a separate budget for brand

For both B2B and B2C:

• have a clear “brand” line of activity: video, educational content, stories;
  
  
• use AI campaigns for demand generation (YouTube, Demand Gen, Discovery);
  
  
• don’t try to “save money” by switching off everything that doesn’t look good in a last-click report.
  
  

The demand you don’t see now later shows up in search and remarketing. If your competitor captures it first, you lose without even entering the game.

5. Think like an “AI trainer”, not a “button pusher”

In 2026, the questions you should be asking yourself are:

• have I clearly defined what I want from this campaign in terms of business metrics, not clicks?
  
  
• Do I have a clean, logical structure that helps AI rather than hinders it?
  
  
• Do I have strong creatives (copy, visuals, video) AI can recombine into effective ads?
  
  
• Is my data flowing back into the system correctly and on time?
  
  

This is the new profile of a specialist both in an agency and in-house: not “the person who clicks buttons in Ads Manager”, but the person who trains AI according to business logic.