How can marketers use AI-powered Search ads to serve their business needs?
Correct Answer
By utilizing broad match, Smart Bidding, and Responsive Search Ads together
Why is this the correct answer?
Marketers can use AI-powered Search ads to serve their business needs by utilising broad match, Smart Bidding, and Responsive Search Ads together. This combination — sometimes called the 'AI-powered trifecta' — lets Google's machine learning work across all three layers of a campaign: broad match expands keyword reach to relevant queries, Smart Bidding optimises bids in real time using contextual signals, and Responsive Search Ads test multiple headline and description combinations to surface the most effective creative. Each component feeds signals to the others, compounding their individual effectiveness.
Why are the other options incorrect?
By using exact match keywords as a means of helping AI-powered Search reach particular audiences
Exact match keywords restrict Google's AI to only the specified query, which limits the system's ability to discover new relevant searches — the opposite of leveraging AI to its full potential.
By relying on manual bidding for queries that marketers believe will drive the most value
Manual bidding removes Google's AI from the bidding process, undermining Smart Bidding's ability to respond to real-time contextual signals — it is not a recommended approach for AI-powered campaigns.
By lowering paid-search budgets and reinvesting in high-quality image assets
Lowering budgets and shifting spend to image assets describes a Display strategy, not a Search strategy — AI-powered Search ads require sufficient budget to generate the conversion data Smart Bidding needs to learn.
Real-World Example
A financial services company switches to AI-powered Search by activating broad match on all ad groups, setting Target ROAS Smart Bidding, and replacing static text ads with Responsive Search Ads featuring 10 headlines. Over 90 days, the combination increases conversion volume by 47% and reduces cost per acquisition by 31% — as the three components reinforce each other by feeding richer data into the bidding model.