On August 10, 2026, Google gave Ask Advisor a much bigger job. Its in-product AI agent can now surface performance changes, answer questions about them, build reports from text prompts, and compare campaign results against similar businesses.
The individual features are useful. The bigger change is how Google wants marketers to work with their data. Instead of opening several reports and manually searching for the reason behind a performance shift, Google wants the first investigation to start with a question.
For paid media teams, that could save time. But faster explanations do not automatically mean better decisions.
What was announced
Google is expanding several AI and agentic features across Google Ads and Google Analytics. The new Google Ads AI tools arrive alongside a matching set of updates in Analytics.
In Google Analytics, new AI Overviews will appear at the top of the homepage. These summaries are designed to highlight changes since the user’s last visit, including traffic movements and seasonal sales patterns. A marketer can then carry that context directly into Ask Advisor for further analysis.
Google is also adding optional phone and email notifications for these insights.
Google Ads is getting an updated homepage with personalised AI insight cards. Advertisers can type questions into a prompt box to generate custom insights around issues such as impression share, competitors, or campaign trends.
This builds on the broader AI direction Google outlined through the Google Marketing Live 2026 announcements, where more campaign planning, bidding, measurement, and execution started moving into Gemini-supported workflows.
Reporting is another major part of the update.
New Google Ads dashboards allow advertisers to describe the report they want using a text prompt. Google then creates the visualisation and generates a real-time AI summary explaining the patterns it finds. Google says similar dashboard capabilities are coming to Analytics, without giving a date.
Finally, Ask Advisor, which Google introduced in May, is gaining Google Analytics benchmarking, allowing marketers to compare campaign performance against anonymised averages from similar businesses.
The new capabilities are built with Gemini and are currently available in beta for English-language accounts.
Why Ask Advisor matters for marketers
Ask Advisor and the reporting updates around it do not introduce a completely new measurement model.
They shorten the distance between seeing a number and investigating why it changed.
That creates several practical implications for marketing teams.
1. Weekly reporting could become much faster
A typical paid media review still involves a lot of mechanical work.
A marketer notices that conversions fell. They compare date ranges, review CPC, check impression share, segment campaigns, inspect traffic quality, open GA4, and then assemble the findings into a report.
Ask Advisor and prompt-based dashboards could remove part of that setup work.
Instead of manually creating every view, a marketer could ask for a comparison or visualisation and spend more time checking whether the explanation actually makes sense.
That fits the broader move toward AI performance marketing, where automation handles repetitive analysis while humans remain responsible for commercial decisions.
2. GA4 becomes easier to interrogate without building another report
GA4 contains plenty of useful data. Finding the exact view you need is often the irritating part.
Ask Advisor gives marketers another way to explore that information without configuring every dimension and filter manually.
Google Analytics AI features have been expanding for a while. The recent GA4 AI Assistant channel group made traffic from AI assistants easier to separate from other referral sources.
Teams that need deeper control can still create custom reports in GA4. Ask Advisor simply gives them a faster first layer for investigation.
For B2B SaaS marketing, that matters because the useful question usually goes beyond traffic.
Teams want to know whether paid traffic produced qualified leads, whether those leads entered pipeline, and whether campaign changes affected acquisition economics.
3. AI explanations need more scrutiny than AI charts
Creating a chart from a prompt is mainly a productivity feature.
Generating an explanation for why the chart changed is where marketers need to be more careful.
A conversion-rate decline might happen at the same time as CPC increases. That does not prove one caused the other.
Landing-page changes, tracking problems, audience mix, sales qualification, competitor activity, seasonality, or a change in the conversion action could all affect the result.
AI-powered reporting can give teams a useful starting hypothesis. It should not become the final diagnosis.
4. Google Analytics benchmarking adds context, but not an answer
Google Analytics benchmarking may be particularly attractive to teams asking whether their performance is “good”.
Comparing results with anonymised averages from similar businesses can help identify unusual gaps.
But benchmark data needs context.
A SaaS company selling a $100-per-month product should not make the same acquisition decisions as one selling $70,000 enterprise contracts, even if Google places them in a broadly similar category.
The benchmark should generate another question, not automatically trigger a budget change.
How Google’s new AI reporting compares with manual analysis
| Task | Traditional approach | New AI workflow |
|---|---|---|
| Find unusual changes | Review dashboards manually | AI homepage summaries surface changes |
| Investigate performance | Build reports and filters | Ask Advisor accepts natural-language questions |
| Create visual reports | Configure charts manually | Prompt-based Google Ads dashboards |
| Interpret results | Analyst reviews multiple signals | AI generates a summary of the data |
| Compare performance | Internal history or external benchmarks | Ask Advisor compares similar businesses |
The obvious advantage is speed.
The tradeoff is that marketers will need to spend less time building the report and more time validating what the report claims.
Google is making a similar shift inside campaign execution. Features such as AI Max for Search campaigns give Google’s systems more responsibility for matching, targeting, and creative decisions.
As automation expands across both execution and reporting, data quality becomes harder to ignore.
What marketing teams should test now
Do not start by replacing your entire reporting process.
Pick one recurring question. For example: “Why did qualified paid search conversions fall this week?”
Run the question through Ask Advisor. Then check the answer against the underlying Google Ads, Analytics, and CRM data.
Repeat that process for several reporting cycles.
The useful test is not whether Ask Advisor produces a convincing explanation. It is whether the explanation consistently points analysts toward the right evidence faster.
Teams should also review their conversion setup before depending heavily on Ask Advisor.
If Google Ads is optimising around low-quality form fills, or GA4 is missing important events, better AI analysis will simply produce a cleaner explanation of bad inputs.
That is the same governance problem appearing elsewhere in Google’s AI advertising stack. The introduction of Google Ads AI transparency labels shows why teams increasingly need to know where AI is operating, what information it is using, and where human review remains necessary.
OneMetrik Takeaway
The most interesting part of the Ask Advisor update is not another AI summary.
It is the change in workflow.
Google wants marketers to move from building reports, to asking questions, to acting on the answer much faster.
That can be useful, especially for lean teams managing several campaigns. But the value comes from reducing reporting work, not outsourcing judgment.
At OneMetrik, we would start with one repetitive reporting workflow, compare Ask Advisor against a normal analyst review for several weeks, and measure whether it gets the team to a verified answer faster.
If it does, expand it.
If it simply produces nicer explanations of the same messy data, fix the measurement first.