Google’s Generative AI Features: What Marketers Should Think

Google has embedded generative AI across more of its product surface than any other company in digital media. From AI Overviews in Search to Gemini inside Google Ads, the changes are structural, not cosmetic. For marketers managing budgets, campaigns, and content strategies, the question is not whether these features matter. It is which ones change how the work gets done and which ones are mostly packaging.

This is a commercial evaluation, not a product review. I am looking at what Google’s generative AI features actually do for marketers, where they create genuine efficiency or reach, and where the gap between the pitch and the reality is still wide.

What Has Google Actually Shipped?

It is worth being precise about scope. When people talk about Google and generative AI, they are usually collapsing several distinct product areas into one conversation. That makes it hard to evaluate anything clearly.

The main areas where Google has deployed generative AI at a scale that affects marketers are: AI Overviews in Google Search (the summary panels that now appear above organic results on many queries), Gemini as a standalone assistant and API platform, generative creative tools inside Google Ads, Smart Bidding and Performance Max (which have used machine learning for years but are now being repositioned under the AI umbrella), and Gemini for Workspace, which integrates AI assistance into Docs, Sheets, Gmail, Slides, and Meet.

Each of these has a different commercial implication. Treating them as one thing produces muddled thinking. I have seen this happen in agency briefings where a client asks “what are we doing about Google AI?” without being able to say which part of Google AI they mean. That ambiguity is usually where bad decisions start.

AI Overviews: The SEO Disruption That Is Already Here

AI Overviews, which Google rolled out broadly in 2024 after testing as Search Generative Experience, is the feature with the most immediate and measurable impact on organic traffic. When Google generates a summary answer at the top of a results page, the click-through behaviour on the results below it changes. For informational queries, this is already visible in traffic data for sites that rely heavily on top-of-funnel content.

I have spent time with clients whose organic traffic profiles look structurally different in 2025 compared to 2023. The sites most affected are those that built their SEO around high-volume informational queries where Google can now synthesise a reasonable answer without the user needing to click anywhere. This is not a future risk. It is a present one.

The strategic response is not to panic about AI Overviews or to try to game the citation logic. The more durable response is to invest in content that cannot be easily summarised: original data, genuine expertise, specific case studies, and content that earns trust rather than just ranking. Moz has written clearly about how E-E-A-T signals interact with AI-generated content environments, and the direction of travel is consistent: demonstrable expertise and first-hand experience become more valuable as AI-generated summaries absorb generic informational content.

There is also an argument that AI Overviews create new citation opportunities. If your content is the source Google draws from for a summary, you gain visibility even without a click. Whether that visibility converts into anything measurable is a different question, and one that most attribution models are not yet equipped to answer honestly.

If you want a broader view of how AI is reshaping content and search strategy, the AI Marketing hub at The Marketing Juice covers the commercial implications across tools, platforms, and workflows.

Google Ads and Generative AI: Useful, But Not the Main Event

Google has added generative creative features to its Ads platform, including the ability to generate headlines, descriptions, and image assets within the campaign interface. These are genuinely useful for teams with limited creative resource. They reduce the friction of producing ad variations and can accelerate the testing process.

But I want to be clear about where the real AI value in Google Ads has always sat: in the bidding. Smart Bidding, which uses machine learning to optimise bids in real time based on conversion signals, has been the most commercially significant AI feature in Google Ads for years. It predates the current generative AI wave by a long stretch. When I was managing significant paid search budgets, the shift from manual bidding to automated bidding strategies was where the performance gains were. The generative creative tools are a useful layer on top of a system that was already doing heavy lifting.

Performance Max, Google’s campaign type that runs across all Google inventory with a single budget, is the fullest expression of this. It uses machine learning to allocate spend across Search, Display, YouTube, Gmail, and Discover based on conversion probability. The results are mixed depending on the account, the conversion data quality, and how well the asset groups are structured. But the underlying intelligence is real. The generative creative features sit within this ecosystem, not above it.

The risk with Google’s AI ad tools is the same risk that has always existed with automated systems: they optimise for what you tell them to optimise for. If your conversion tracking is incomplete, your bidding strategy will be wrong regardless of how sophisticated the AI is. I have seen this pattern repeatedly across accounts where Performance Max was blamed for poor results that were actually caused by broken attribution. The AI was doing exactly what it was asked to do. The problem was the instruction.

Gemini: Google’s Model and Where It Fits for Marketers

Gemini is Google’s large language model, available as a consumer product, an enterprise product via Google Cloud, and increasingly embedded across Google’s suite. Evaluating it as a standalone AI assistant puts it in direct comparison with GPT-4 and Claude. The honest assessment is that the capability gap between the leading models has narrowed considerably. For most marketing tasks, the differences are marginal and the choice often comes down to which ecosystem you are already in.

Where Gemini has a genuine structural advantage is in its integration with Google’s data infrastructure. Gemini with Google Workspace can access your Gmail, Docs, Drive, and Calendar. Gemini in Google Ads can reference your campaign history and performance data. That contextual grounding is where it pulls ahead for teams already operating inside Google’s ecosystem. A standalone AI assistant without access to your actual data is useful for general tasks. An AI assistant that can read your last six months of campaign reports and summarise performance trends is a different proposition.

I have used AI tools extensively in content and campaign workflows, and the pattern I keep coming back to is that the value scales with the quality of the inputs. A model that has access to your actual business context, your tone of voice guidelines, your historical data, produces more useful outputs than one working from a blank prompt. This is where Google’s integration depth becomes commercially relevant rather than just technically interesting.

Ahrefs has been running practical sessions on how to use AI tools in SEO and content workflows that are worth reviewing if you are building a more systematic approach to AI-assisted content production. The framing is practical rather than promotional, which is what you want when you are trying to make real workflow decisions.

Gemini for Workspace: The Underrated Productivity Story

The most consistently underrated part of Google’s AI rollout for marketing teams is Gemini inside Workspace. This is not the headline feature. It does not generate the conference keynote moments. But for teams doing real work, it is where the compounding value shows up.

The ability to draft a brief in Docs with AI assistance, summarise a long email thread in Gmail, generate a slide structure in Slides from a written outline, or pull insights from a spreadsheet in Sheets without writing complex formulas, these are not significant capabilities in isolation. They are incremental time savings that, across a team and across a week, add up to something meaningful. I have seen marketing teams reduce the time spent on internal documentation by a significant margin simply by integrating AI assistance into the tools they were already using every day.

The friction reduction matters. One of the reasons AI adoption stalls in agencies and marketing teams is that people have to context-switch to use it. They are working in Google Docs and have to open a separate browser tab to use an AI assistant. When the AI is inside the tool, the adoption curve flattens. This is not a trivial product decision on Google’s part. It is a deliberate strategy to make AI the path of least resistance.

HubSpot has covered the practical side of AI in marketing automation workflows in ways that translate well to how Workspace AI fits into a broader marketing operation. The integration logic is similar: AI embedded in the workflow beats AI as a separate step.

The Structural Tension Marketers Should Not Ignore

Google is simultaneously the company building AI tools for marketers and the company whose advertising platform those marketers depend on for a significant portion of their revenue. That is a structural tension worth naming clearly.

AI Overviews reduce organic click-through on informational queries. The response Google would likely encourage is more paid search investment to compensate for lost organic traffic. Performance Max automates budget allocation across Google’s own inventory. The more you trust the automation, the less visibility you have into where your money is going. Gemini inside Google Ads generates creative assets and recommendations, but it is doing so in service of a platform that earns revenue when you spend more.

None of this means Google’s AI features are not useful. Many of them are. But the evaluation framework should include an honest question: who benefits most from this feature? When the answer is clearly the advertiser, the feature is worth investing in. When the answer is ambiguous, or when the feature primarily benefits Google’s revenue model, that should inform how much you trust the defaults.

I spent years in agency leadership watching clients accept automated recommendations from platforms without asking this question. The platforms are not adversaries. But they are not neutral advisors either. Keeping that distinction clear is part of the job.

Semrush has a useful breakdown of how to use AI optimisation tools for content strategy that approaches the question from the marketer’s perspective rather than the platform’s, which is a useful corrective when you are trying to build an independent view.

How to Evaluate Google’s AI Features Without Getting Distracted by the Noise

The volume of announcements, updates, and repositioned features coming from Google is high. Not all of it is equally significant. Here is a practical framework for deciding where to pay attention.

First, separate features that affect how your audience finds you from features that affect how efficiently your team works. AI Overviews fall into the first category. Gemini for Workspace falls into the second. These require different responses and different timelines.

Second, look at what has been running long enough to have real performance data. Smart Bidding and automated bidding strategies have years of data behind them. Generative creative features in Google Ads are newer. Weight your confidence accordingly.

Third, test with real budgets and real conversion tracking before drawing conclusions. The number of times I have seen a client form a strong opinion about a Google AI feature based on a campaign that had broken attribution or insufficient data volume is not small. The feature is not the variable. The measurement is.

Fourth, watch what happens to organic traffic on informational queries over the next twelve months. The AI Overviews rollout is still relatively recent in terms of its full impact on traffic patterns. The data will become clearer. Build your content strategy with that uncertainty in mind rather than assuming the current state is stable.

Moz has published a thoughtful piece on how to approach content writing with AI tools that is worth reading alongside any evaluation of how Google’s AI features change the content production calculus. The two questions are related: if AI is changing how content gets found, it is also changing how content should be produced.

For a wider view of how AI is reshaping marketing strategy across platforms and workflows, the AI Marketing section at The Marketing Juice covers the practical and commercial dimensions without the vendor hype.

The Verdict on Google’s Generative AI Position

Google is not behind on AI. The narrative that it was caught flat-footed by ChatGPT and is scrambling to catch up is largely a media story that does not survive contact with the actual product timeline. Google has been running machine learning at scale in its advertising products for years. Gemini is a capable model. The Workspace integration is genuinely useful. AI Overviews represent a real structural change to how Search works.

The more honest critique is that Google’s AI rollout has been uneven in quality and sometimes prioritised speed over reliability. AI Overviews launched with visible errors and has been refined since. Performance Max remains opaque in ways that make it difficult to trust fully without careful monitoring. Gemini’s quality in Workspace is good but not consistently excellent across all task types.

For marketers, the evaluation should be practical and commercial. Which features change how your audience finds you? Which features make your team more productive? Which features primarily benefit Google’s revenue model? Answer those three questions and you have a clearer picture than most of the coverage provides.

Early in my career, when I could not get budget for a new website, I taught myself to code and built it myself. The lesson was not that you should always do everything yourself. It was that understanding how a tool works at a technical level gives you a different kind of judgment about when to use it and when to question it. That principle applies directly to evaluating AI features from any platform, including Google. The more you understand what is actually happening under the surface, the less likely you are to be misled by the presentation layer.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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