新しいAIツールでマーケティングを進化させるEvolve your marketing with new AI tools

Google is expanding the role of artificial intelligence across its advertising and measurement products, rolling out new AI and agentic experiences in Google Ads and Google Analytics that are meant to streamline everyday marketing work. For the businesses and agencies that rely on these platforms to find and understand customers, the update matters because it pushes more of the routine labor of campaign management and data analysis toward automation, potentially lowering the technical barrier to running sophisticated marketing programs.

At the center of the changes is the idea of "agentic" experiences. Where earlier generative tools mostly produced a piece of text or an image in response to a single prompt, agentic AI is designed to handle multi-step tasks with a degree of independence, moving through a workflow and taking actions on the user's behalf. In a marketing context, this appears to mean a marketer could describe a goal in plain language and have the system help assemble the relevant pieces, from building a campaign to surfacing the metrics that explain how it is performing, while the person remains in the loop to review and approve.

In Google Ads, the emphasis is on simplifying how campaigns are created, launched, and optimized. The stated goal is to make campaign management more automated and accessible, so that advertisers spend less time on manual setup and more on strategy. This builds on a direction Google has pursued for several years through AI-driven products such as Performance Max, which automates targeting and creative delivery across Search, YouTube, Display, Gmail, and Maps from a single campaign. Layering agentic features on top of that foundation is a logical next step, though the practical impact will depend on how much control marketers retain over budgets, bidding, and brand safety.

On the measurement side, the new capabilities in Google Analytics are aimed at making data analysis faster and easier to interpret. Rather than manually building reports or navigating complex menus, marketers are expected to be able to ask questions about their data and receive summarized insights and recommended actions. This is a meaningful shift for a platform that many users have found challenging since the transition to Google Analytics 4, which replaced the older Universal Analytics in 2023 and introduced a different, event-based data model. Conversational, AI-assisted analysis is likely intended to close the gap between the volume of data these tools collect and the ability of non-specialists to act on it.

Technically, these features are consistent with Google's broader push to embed its Gemini models throughout its product line, from Search and Workspace to developer tools. Agentic marketing assistants draw on large language models to understand instructions, generate assets, and reason across multiple steps, but they also need access to account data, performance signals, and platform controls to be useful. That combination is what distinguishes an "agent" from a simple chatbot, and it is also what raises the stakes around transparency, oversight, and data handling.

The move fits a competitive pattern across the industry. Microsoft has integrated its Copilot assistant into Microsoft Advertising, and Meta has expanded its Advantage+ suite to automate ad creation and audience selection across Facebook and Instagram. Advertising and analytics vendors more broadly are positioning automation and natural-language interfaces as core features rather than add-ons. Against that backdrop, Google's update looks less like a standalone product launch and more like an ongoing effort to keep its dominant advertising and measurement platforms aligned with where marketing software is heading.

For marketers, the potential benefits are clear: less time on repetitive configuration, quicker access to insights, and a lower learning curve for teams without dedicated analysts. The trade-offs deserve attention as well. Greater automation can reduce visibility into why a system made a particular decision, and agentic tools that take actions on an account will require careful review to ensure they align with business goals, budgets, and privacy obligations. As with earlier automated campaign types, the results are likely to vary by advertiser, objective, and the quality of the underlying data.

As these experiences roll out, the practical advice is measured. Marketers can begin testing the new tools on lower-risk campaigns and reports, compare outcomes against existing workflows, and keep human oversight in place while they assess how well the automation performs in their specific context.