Google Intros AI Tools to Help Businesses Scale Search Campaigns

Google has recently announced the introduction of AI tools designed to assist businesses in scaling their search campaigns. The company explained on its blog that these tools enable companies to test different budgets and ROI targets across multiple Search campaigns through a single A/B test. This development will roll out in September and will aid businesses in understanding the impact of scaling their campaigns on their bottom line.

For businesses that require specific brand or location controls, A/B testing has become more straightforward with the new AI Max experiment capabilities. These capabilities allow companies to run tests while maintaining these settings, enabling them to evaluate the effects of AI Max without compromising those restrictions.

Moreover, Google introduced an enhancement to its Performance Planner. The updated tool now enables businesses to visualize the possible effects of modifications such as bidding or budget targets on their campaign performance. According to the blog post promoting Google's upcoming "Rethink 2026" event, these AI tools allow companies to apply suggested changes with just a single click.

PYMNTS, in a report published last week, highlighted the latest AI models from both Google and OpenAI, both of which emphasized speed as a crucial factor. Each company rushed to market with a new tier featuring an ultrafast model. OpenAI unveiled its "Ultrafast" tier, running its GPT-5.6 Sol model up to 14 times faster than the standard tier, capable of processing up to 750 output tokens per second.

On the other hand, Google launched Gemini 3.7 Flash, positioning it as its "most intelligent workhorse model yet for coding and agents."

PYMNTS argued that the significance of speed in AI pricing is growing. For the last two years, the pricing of AI models has primarily been influenced by their capabilities and the extent of their utilization. However, speed is now being treated as an independent feature that businesses are willing to pay for separately. The report suggested a future where companies may divide their AI spending into "fast" and "slow" lanes.

The "fast" lane would cater to tasks where delays could be costly, such as live customer interactions or real-time fraud detection, while the "slow" lane would be suitable for tasks where waiting is less critical, like overnight reporting. The PYMNTS post on Google's AI tools for businesses scaled search campaigns was originally published on PYMNTS.com.