AI-powered marketing reporting helps in-house teams retrieve, analyze, and explain performance data using natural-language questions. Hawaiian Airlines used the same foundation to replace an eight-person, eight-day reporting process and save more than 500 hours a month.
Key takeaways
- Reporting automation removes the manual collection step: Hawaiian Airlines moved from a monthly Google Sheets assembly to daily automated transfers across more than 50 accounts. AI-powered reporting then makes that data investigable in plain language.
- AI-powered reporting depends on complete, consistently structured, and current data. An AI model can't fix incorrect source data or missing tracking.
- AI-powered reporting answers unplanned questions that a fixed dashboard doesn't display; dashboards still win for the recurring metrics a team checks every week.
- Hawaiian Airlines used Supermetrics to replace an eight-person, eight-business-day process with automated daily collection across over 50 accounts, saving more than 500 hours a month across internal teams and agencies.
- Verdict: In-house teams should treat AI as an analysis layer built on reliable reporting infrastructure, not as a substitute for that infrastructure.
Manual marketing reporting becomes harder to sustain as in-house teams add accounts, markets, and stakeholders. In an August 2026 Hawaiian Airlines marketing intelligence webinar, the airline explained that it once needed eight people and eight business days to assemble a monthly performance snapshot in Google Sheets. Teams copied data from individual platforms, then worked with external agencies to reconcile errors.
Supermetrics, a marketing intelligence platform, automated those data transfers, bringing daily data from more than 50 advertising, social, and Google Search Console accounts into BigQuery. Hawaiian Airlines gained a more timely view of performance, expanded some reporting tables from 22 metrics and dimensions to more than 100, and saved more than 500 hours a month across internal teams and agencies.
Centralizing Hawaiian Airlines' marketing data in BigQuery is also what made AI-powered reporting possible. Hawaiian Airlines is now testing and using AI assistants to investigate performance using detailed, daily marketing data.
This guide explains how in-house marketing teams can make the same shift. You'll see the sequence Hawaiian Airlines followed, the data checks that came first, and the six steps to run the same shift on a smaller footprint. If you manage reporting for multiple clients, read our guide to AI-powered marketing reporting for agencies.
What is AI-powered marketing reporting?
AI-powered marketing reporting for in-house teams connects artificial intelligence to approved marketing data, meaning only the accounts and datasets the organization has explicitly permitted the AI to read. Marketers can ask questions in plain language and receive answers based on that business data rather than manually extracting figures or relying only on predefined dashboards.
For example, a performance marketing leader could ask: “Which campaigns are pacing above budget this month, and which ones are below target ROAS?”
An AI-powered reporting system retrieves the figures the question asks for and produces a structured response. The marketer can then check the data, add business context, and decide whether further investigation is necessary.
The AI-powered marketing reporting workflow has four steps:
- Data collection: Marketing data is retrieved from approved advertising, analytics, CRM, and other business platforms.
- Data preparation: The data is structured and delivered to a reporting destination, data warehouse, or supported AI workspace.
- AI analysis: An AI model examines the relevant information and produces a comparison, explanation, or summary.
- Human review: A marketer verifies the important figures and decides what the findings mean for the business.
Supermetrics is a marketing intelligence platform that owns the first two stages and connects directly into the third: it collects and prepares marketing data, then delivers it to the reporting or AI environment where the analysis happens. The AI model interprets the data, while the marketer provides commercial context and remains responsible for the final decision.
How is AI-powered marketing reporting different from reporting automation?
Reporting automation collects and refreshes predefined data on a schedule, without anyone repeating the process by hand. AI-powered marketing reporting builds on that automated foundation, letting marketers investigate questions and explain changes through natural-language prompting.
| Reporting approach | What it does | Best suited to |
|---|---|---|
| Manual reporting | Requires someone to export, combine, check, and present data | Small or occasional reports with limited sources |
| Automated reporting | Collects and refreshes predefined data on a schedule | Recurring dashboards and standard reports |
| AI-powered reporting | Analyzes data in response to a natural-language request | Investigation, explanation, and flexible summaries |
| Agentic reporting | Emerging approach: completes multistep reporting tasks within defined rules | Repeatable workflows that can be reviewed or approved by a marketer |
These reporting approaches can overlap. Supermetrics Studio combines the recurring visibility of a dashboard with AI-assisted dashboard creation and editing. Supermetrics Studio fits teams that want a shared, continuously updated view rather than a one-off AI analysis. Unlike BI-native AI summaries that are limited to the data available in a particular dashboard view, Supermetrics can deliver a broader connected field set into AI workflows, so marketers can investigate questions that were not anticipated when the dashboard was built.
Agentic reporting goes one step further, carrying out defined workflows on the marketer's behalf.
What data foundation does AI-powered marketing reporting require?
AI-powered marketing reporting requires data that is complete, consistently structured, current, and ready for the AI to interpret. Building this foundation before connecting an AI model reduces unreliable comparisons and gives marketers more useful answers.
Is your marketing data complete enough for AI to answer new questions?
An AI reporting system needs enough data to answer questions beyond those covered by a standard dashboard. Dashboards present a selected set of metrics and dimensions, while new questions may depend on fields that aren’t displayed.
Hawaiian Airlines expanded some of its reporting tables from 22 metrics and dimensions to more than 100 after automating data collection. Jake Wood, Senior Digital Media Analyst at Hawaiian Airlines during the project, explained why that wider dataset matters: “You don’t need all 100, but when you get that one request from your boss... you’re kind of glad that that was being transferred.”
This doesn’t mean collecting every available field. Teams should start with the questions they want AI-powered reporting to answer. Those questions determine which sources, reporting periods, and levels of detail the system needs.
Are campaign names, currencies, and conversions consistent across accounts?
Consistent marketing data uses shared definitions so the AI can compare campaigns, markets, and channels correctly. Teams may need to align campaign names, currencies, time zones, and conversion definitions before combining their data.
This becomes especially important when an organization adds brands or merges marketing operations. Hawaiian Airlines and Alaska Airlines brought different audiences, markets, and taxonomies into one reporting environment, so the team had to map how the two organizations classified and discussed marketing data.
An AI model may confidently compare two categories that don’t mean the same thing. The reporting layer must define those relationships instead of expecting the model to infer the company’s internal terminology.
As Jake Wood says: “To have artificial intelligence, it needs a reservoir of clean data to operate from. When you’re training AI agents off Google Sheets with limited data and quality issues, you don’t get good AI feedback. Garbage in, garbage out.”
How current does marketing data need to be for AI reporting?
AI-powered reporting needs current data that has been checked against results the team already trusts. For fast-moving performance questions, daily refreshes provide a practical baseline; some use cases may require more frequent updates.
Hawaiian Airlines replaced its manual Google Sheets process with automated daily transfers from more than 50 advertising, social, and Google Search Console accounts. This gave its teams more timely data while reducing errors caused by manual transfer.
Automation doesn’t guarantee that every figure is correct. Tracking problems and incorrect platform settings can still affect the result. Teams should monitor data connections and test the AI against several questions their analysts have already answered, comparing both the figures and any omissions before widening access.
Why marketing metrics should be calculated before the AI sees them
Marketing data should reach the AI in a clear format with figures that affect budget, forecast, or attribution decisions already calculated. The AI can then focus on comparing results and explaining changes rather than reconstructing metrics from raw inputs.
Zach Bricker, Lead Solutions Engineer at Supermetrics, described this as a “hard and fast rule”: “We don’t let LLMs do math.” Supermetrics performs calculations before the data reaches the model and supplies ready-to-use figures, such as an already-calculated CPA or aggregated campaign total. The LLM can cite and interpret those figures without recreating the calculation itself.
This separation also makes the answer easier to verify. The reporting system produces the figures, while the LLM interprets and communicates them. Our guide to making marketing data AI-ready explores the preparation process in more detail.
How does AI-powered marketing reporting work?
AI-powered marketing reporting works by retrieving approved data in response to a marketer’s question, passing that data to an AI model for analysis, and returning an answer for human review. The exact data route depends on how the organization stores and manages its marketing information.
1. The marketer asks a specific marketing question
The AI-powered reporting workflow begins with a clearly scoped marketing question. The prompt should identify the relevant accounts, dates, metrics, comparison period, and expected output.
A broad prompt such as “How are our campaigns performing?” forces the model to make too many choices. A more useful request would be:
“Compare Google Ads and Meta Ads performance in the US over the past seven days with the previous seven days. Show changes in spend, conversions, CPA, and ROAS. Identify which campaigns drove any material movement and separate confirmed findings from possible explanations.”
A specific prompt helps the reporting system retrieve the right data and makes the resulting answer easier to check.
2. Supermetrics retrieves the approved marketing data
The reporting system retrieves the marketing data required to answer the question. It can deliver that information directly to a supported AI workspace or make it available through a centralized data warehouse.
| Data route | How it works | Best suited to |
|---|---|---|
| Direct to an AI workspace | Supermetrics retrieves selected marketing data for a supported AI workspace | Focused conversational reporting |
| Through a warehouse | Supermetrics transfers data to a warehouse that an internal AI system can query | Reporting environments that need more history, modeling, or downstream uses. |
Supermetrics for Claude and Supermetrics for ChatGPT let marketers work with connected marketing data inside those AI environments. Supermetrics also supports AI integrations through its broader AI ecosystem, including MCP-based connections to tools such as Gemini and Copilot. Supermetrics can also transfer data to a marketing data warehouse such as BigQuery or Snowflake.
Hawaiian Airlines follows the warehouse route. Its marketing data flows into BigQuery, where it can support both established reporting and AI-assisted analysis. As Jake Wood explained, “Supermetrics has allowed us to centralize all of our analytics into one singular warehouse [where] it’s easy for us to connect to an AI agent.”
3. The AI model analyzes the retrieved marketing data
The AI model analyzes the figures supplied by the reporting system and produces a response in the requested format. Depending on the prompt, it might compare reporting periods, identify the campaigns behind a change, or summarize performance for a stakeholder.
The roles within the workflow remain distinct. The source platforms record campaign activity. Supermetrics retrieves and delivers the requested data. The AI model interprets that information rather than creating its own performance figures.
4. The marketer reviews and validates the AI’s answer
The marketer checks the AI-generated answer against the retrieved data and adds business context before acting on it. A reliable response should make clear what the data confirms and what still requires investigation.
For example:
- Confirmed finding: CPA increased by 18% over the comparison period.
- Data-supported contributor: Two high-spend campaigns accounted for most of the increase.
- Possible explanation: A recent audience change may have reduced conversion efficiency.
The data supports the confirmed finding and the contributing campaigns directly. The possible explanation stays a hypothesis until the marketer checks the campaign history and other evidence.
Human review therefore remains part of AI-powered reporting. The AI can accelerate the investigation, but the marketer remains responsible for deciding what the findings mean and what action to take.
What can in-house teams use AI-powered marketing reporting for?
In-house teams can use AI-powered marketing reporting to investigate performance changes, compare markets, monitor budgets, and prepare stakeholder updates. AI adds the most value when a question requires more flexibility than a predefined dashboard provides.
| Reporting need | Example question | Useful output |
|---|---|---|
| Campaign investigation | Which campaigns drove the fall in conversions? | A campaign-level contribution analysis |
| Promotion analysis | How did the latest sale perform? | A comparison using the promotion’s exact dates |
| Market comparison | Which regions improved efficiency this month? | A standardized comparison across markets |
| Budget pacing | Are campaigns on track to spend the monthly budget? | Current pace and projected month-end spend |
| Stakeholder reporting | What should the CMO know about this month? | A concise summary with supporting figures |
Marketing investigations rarely end with one question. A marketer investigating a rise in CPA might first identify the affected market, then ask which campaigns drove the increase. Further questions could compare those campaigns with their targets or previous performance. This conversational process lets marketers follow the evidence without asking an analyst to build a new report at each stage.
Self-service access moves routine lookups off the analytics team, so analysts can spend more time defining verified metrics and investigating findings that require deeper expertise.
AI-powered reporting should still support investigation rather than replace judgment. Access must remain permission-based, and marketers should review material findings before changing budgets or sharing them with stakeholders. Performance data can show what changed and identify likely contributors, but it rarely proves why the change occurred.
Which AI-powered reporting use cases suit which team size?
Small in-house teams get the most value from focused use cases such as campaign investigation, promotion analysis, and stakeholder summaries. These can work through a direct AI-workspace connection without requiring a warehouse. Larger teams managing multiple markets, brands, or dozens of accounts benefit more from a warehouse route because cross-market comparisons and budget pacing depend on consistent definitions, broader history, and centralized governance.
How did Hawaiian Airlines prepare for AI-powered marketing reporting?
Hawaiian Airlines prepared for AI-powered marketing reporting by automating its data collection and centralizing the results in BigQuery. The change saved more than 500 hours a month while creating a cleaner, more detailed dataset that AI assistants could analyze.
Before Supermetrics, eight people spent eight business days producing a monthly performance snapshot. Teams manually copied platform data into Google Sheets and worked with the airline’s agency to reconcile errors. The merger with Alaska Airlines increased the reporting footprint from around 30 accounts to more than 50.
| Before Supermetrics | After Supermetrics |
|---|---|
| Manual Google Sheets workflow | Automated transfers into BigQuery |
| Eight people and eight business days | Automated daily collection, more than 500 hours saved each month |
| Monthly reporting | Daily data collection |
| 22 metrics and dimensions | More than 100 in some reporting tables |
| Manual error reconciliation | Data retrieved directly from source platforms |
The move to daily data changed more than the speed of reporting. Hawaiian Airlines could investigate shorter periods around sales or campaign launches instead of relying on month-level snapshots. Its broader dataset also gave the team enough detail to answer questions that standard reports hadn’t anticipated.
That centralized data now supports Hawaiian Airlines’ emerging AI reporting workflows. The order matters. AI didn’t create the reporting foundation or produce the 500-hour saving. Automation did. AI-powered reporting became possible because Hawaiian Airlines first made its marketing data current, accessible, and ready to analyze.
Because the data lives in BigQuery, the team can also query it through Google’s own assistant. As Jake Wood explained, “I’m just one analyst, and I can’t be on every meeting. But what team members can do is have the Gemini AI assistant running during meetings. If they get a random question they don’t know the answer to, they can ask it and get a response grounded in real analytics. It keeps the flow of the meeting going without anyone needing to open a dashboard or write a query.”
How can in-house teams keep AI-powered marketing reporting accurate and secure?
In-house teams can improve the accuracy and security of AI-powered reporting by connecting AI to governed marketing data, limiting access to approved accounts, and requiring human review. Direct access to source data reduces manual errors and guesswork, but it doesn't guarantee that every AI-generated conclusion will be correct.
AI models predict plausible answers rather than independently verifying facts. Giving an AI model verified marketing data reduces the need to guess, although tracking errors and incomplete historical data can still weaken the analysis. Our guide to reducing AI hallucinations in marketing recommends starting with a small dataset, asking specific questions, and checking the output against known results.
Security depends on the products and architecture the organization approves. As of 2026, Supermetrics undergoes annual SOC 2 Type II audits, is ISO 27001 certified, supports GDPR and CCPA requirements, and provides access controls and data-region options for supported storage configurations. Review the Supermetrics security practices alongside the security and privacy terms of the chosen AI provider.
To keep AI-powered reporting as accurate and secure as possible, in-house teams should do the following:
- Use verified marketing data. Connect the AI to approved source-platform data rather than asking it to estimate performance or work from an incomplete export.
- Control access. Limit each user and AI workflow to the accounts and datasets required for the task.
- Ask specific questions. Define the accounts, metrics, date range, comparison period, and expected output.
- Check data limitations. Confirm whether platform APIs provide enough history and whether any sources or fields are missing.
- Separate findings from explanations. Label measured changes as facts and treat proposed causes as hypotheses until someone verifies them.
- Standardize recurring tasks. Use shared prompts, projects, or skills to provide consistent instructions and business context.
- Review important outputs. Check figures that affect budgets, forecasts, attribution conclusions, or executive reporting against the source.
- Keep a person accountable. A named marketer or analyst should approve material conclusions and any resulting action.
Check what the AI provider retains. Confirm whether the AI vendor trains on submitted data, how long prompts and outputs are stored, and whether an enterprise agreement changes either.
AI-powered reporting should reduce the work required to reach a credible answer. However, it shouldn't remove the controls that make the answer credible.
How should an in-house team get started with AI-powered marketing reporting?
An in-house team should start with one familiar reporting task, connect only the data required for that task, and compare the AI’s answers with the existing report. Setup time depends on how many accounts and sources need connecting and whether a warehouse is already in place, so start with a small use case that the team can validate quickly.
- Choose a controlled use case. Start with a weekly performance summary, budget-pacing check, or campaign investigation the team already understands. Avoid beginning with automated budget changes or high-stakes forecasting.
- Document the current process. Record the people involved, hours required, data sources, common errors, and time to final delivery. The baseline will help the team measure whether the new workflow saves time or improves reporting coverage.
- Connect and validate the data. Automate the required data flow and confirm the accounts, metrics, date ranges, currencies, and historical coverage. Supermetrics can move marketing data from sources such as Google Ads, Meta Ads, LinkedIn Ads, and GA4 into destinations including BigQuery, Snowflake, Looker Studio, Power BI, Google Sheets, and supported AI workspaces.
- Test questions with known answers. Ask the AI to analyze a performance change the team has already investigated. Compare the figures, explanation, and omissions with the analyst’s conclusion.
- Standardize what works. Turn successful instructions into reusable prompts or workflows. Define who can access the data, who reviews the output, and which questions still require an analyst.
- Expand once the results hold. Add more sources, use cases, or users after the initial workflow has matched the analyst’s conclusions across several test questions and shown a measurable time saving.
Build your AI-powered reporting foundation with Supermetrics
Hawaiian Airlines shows how to get your AI-powered reporting foundation right: automation first produced the 500-hour saving, and the AI layer became useful only once the data underneath it was daily, complete, and consistent. If you’re deciding where to start, start with the pipeline, not the assistant.
You don’t need to transform your entire reporting setup at once. Start with one recurring question and automate the data required to answer it. Test the AI’s output against results your team already trusts, then expand the workflow as confidence grows.
Supermetrics automates data flows from marketing platforms into data warehouses, reporting tools, and supported AI workspaces such as Claude and ChatGPT. Pricing depends on the plan, data sources, destination, and usage. Supermetrics also offers a 14-day free trial, so teams can validate a focused reporting use case before committing to a broader rollout.
FAQs
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Claude and ChatGPT can both create marketing reports when connected to verified marketing data through Supermetrics. Supermetrics’ broader AI ecosystem also supports connections to Gemini and Copilot, while BI tools such as Looker Studio and Power BI can support AI-assisted analysis in their own reporting environments. The best option depends on the team’s data architecture, governance requirements, and preferred workflow.
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ChatGPT can create marketing reports when it has access to the relevant performance data and clear instructions. ChatGPT can compare periods, identify material changes, and draft summaries. The quality of the report still depends on the data provided, so marketers should verify important figures and review any explanations before using them.
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An in-house team doesn’t always need a data warehouse for AI-powered reporting. Teams can connect approved marketing data directly to supported AI tools for focused use cases. A data warehouse becomes more valuable when the organization needs historical storage, custom data models, detailed access controls, or several reporting and AI systems using the same data.
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AI-generated marketing reports can be reliable when the AI works from trusted data, receives specific instructions, and remains subject to human review. Direct access to source data reduces the risk of invented figures, but AI can still misinterpret accurate information. Marketers should check important numbers, treat proposed causes as hypotheses, and follow the practices in our guide to reducing AI hallucinations in marketing.
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The time saved through marketing reporting automation depends on the number of accounts, reports, contributors, and manual steps involved. Hawaiian Airlines reports saving more than 500 hours a month across internal teams and agencies after replacing an eight-person, eight-business-day reporting process with automated daily data collection.
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AI won't replace the full role of a marketing analyst. AI can reduce time spent retrieving data and preparing first-draft summaries. Analysts remain responsible for measurement design, data quality, complex investigation, and business interpretation.
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AI-powered marketing reporting can’t fix incorrect source data, missing tracking, or gaps in platform API history, and it can’t establish why a metric moved. It can suggest plausible explanations from the figures it receives, but a stated cause remains a hypothesis until someone checks campaign history and other evidence. Teams with data in one platform or only a small number of accounts may get more value from a standard dashboard than from an added AI layer.
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The cost has two parts: the data automation layer and the AI workspace. Supermetrics pricing depends on the plan, data sources, destination, and usage, while AI workspace seats are billed separately by the provider. Supermetrics offers a 14-day free trial, so teams can test a focused use case before expanding the setup.
