Will AI replace my need for dashboards entirely?

Not entirely. Dashboards are still great for real-time monitoring and visual overviews that you glance at throughout the day. AI excels at ad hoc analysis, deep dives, and answering specific questions. The two complement each other — dashboards for monitoring, AI for analysis.

Can AI handle the same level of detail as my spreadsheet analysis?

For most marketing analysis, yes. AI can compare date ranges, calculate derived metrics, identify trends, and cross-reference data across platforms. For very specific financial modeling or custom formulas, spreadsheets still have an edge. But 80% of the analysis marketers do in spreadsheets can be done faster through conversation.

What if I need to share the analysis with someone who does not use AI?

AI responses are text that you can copy, paste, and format in any tool. Many marketers copy AI analysis into Google Docs, Slack, or email. For formal reports, you can ask the AI to format its response as a structured summary ready for a deck or document.

Insight

Stop Exporting CSVs From Ads Manager

The export-to-spreadsheet workflow is a relic. There is a faster way to analyze your ad data.

7 min readAd SuperpowersUpdated 2026-02-11

You know the routine. You need to check campaign performance, so you open Ads Manager. Click into campaigns. Set your date range. Add columns for the metrics you care about. Wait for it to load. Realize you forgot a column, add it, wait again. Export to CSV. Open the spreadsheet. Build your pivot table. Create your formulas. Format the numbers. Fifteen minutes later, you finally start the actual work: understanding what the data is telling you.

Now multiply that by every platform you manage. Meta. Google Ads. LinkedIn. TikTok. Each with its own UI, its own export format, its own column names for the same metrics.

This workflow made sense in 2020. In 2026, it is a waste of your most valuable resource: attention. There is a better way, and it does not involve a single CSV file.

The Real Cost of the CSV Workflow

Let us be honest about what the export-to-spreadsheet workflow actually costs:

Time: The average digital marketer spends 30-45 minutes per day on data extraction and formatting. That is 10-15 hours per month spent not on strategy, creative, or optimization — but on getting data from point A to point B.

Context switching: Every time you leave your analysis to go back to the platform for more data, you lose your train of thought. "Wait, what was the CTR for that ad set in the second week?" Back to Ads Manager. Re-set the filters. Export again. By the time you get back to your analysis, you have lost the thread.

Staleness: The moment you export a CSV, it is already outdated. Campaigns run 24/7. Your snapshot from this morning does not reflect the budget changes you made at lunch. If you need fresh data, you export again.

Errors: Manual data handling introduces errors. Wrong date ranges, mismatched columns, formula mistakes, copy-paste errors between platforms. A single wrong filter in your export can lead to a wrong conclusion and a wrong optimization decision.

The irony is that marketers who are obsessive about data accuracy spend hours in workflows that are inherently error-prone.

What Replaced Spreadsheets Before AI (and Why It Was Not Enough)

The industry tried to solve this with dashboarding tools. Supermetrics, Funnel.io, Looker Studio, Power BI — these tools pull data from ad platforms into centralized dashboards. And they are genuinely useful for certain things.

But dashboards have their own limitations. They show you pre-configured views. When you have a question that does not fit your dashboard layout, you either build a new widget (which takes time) or fall back to the CSV export workflow.

Dashboards are great for monitoring. They answer "how are things going?" But they struggle with ad hoc analysis — the kind of question that pops into your head at 3 PM on a Tuesday: "Is my retargeting campaign cannibalizing my prospecting campaign's conversions?" That question requires pulling specific data, cross-referencing audiences, comparing attribution windows, and thinking through the logic. No pre-built dashboard covers that.

The real problem was never about dashboards versus spreadsheets. It was about the gap between having a question and getting an answer. Every tool until now required you to translate your marketing question into a data query — whether that meant clicking through a UI, writing a SQL query, or configuring a dashboard widget.

The Conversational Alternative

Now picture a different workflow. You open your AI assistant — Claude, ChatGPT, or whatever tool you prefer. You type:

"Compare my Meta retargeting campaigns' ROAS this month versus last month. Break it down by ad set and flag any that dropped more than 15%."

Five seconds later, you get a formatted response with the comparison, a clear flag on underperformers, and a suggestion for what might be causing the decline (creative fatigue, audience saturation, budget distribution).

Then you ask a follow-up: "For the ad sets that dropped, what is the frequency? Are we over-saturating?"

Another five seconds. Claude pulls the frequency data, cross-references it with the ROAS drop, and confirms that two ad sets have frequencies above 4 with declining performance — classic fatigue signals.

You just completed an analysis that would have taken 20 minutes with exports and spreadsheets. And you did it conversationally, in the same way you would ask a colleague.

This is what happens when your AI tool has direct access to your ad platforms through MCP. There is no export step. No spreadsheet. No context switching. Just questions and answers.

Real Scenarios Where This Changes Everything

Here are scenarios that every digital marketer recognizes — and how they change when you stop exporting CSVs:

Monday morning check-in: Instead of opening 3 platforms and building 3 reports, you ask "Give me a weekend performance summary across all platforms. Highlight anything that needs attention." One question, all platforms, 30 seconds.

Client call prep: You used to spend 45 minutes before every client call building a performance deck. Now you ask your AI to "Write a performance summary for Client X's Google Ads account covering the last 2 weeks. Focus on search campaigns, conversion trends, and budget pacing." Copy-paste into your deck. Done.

Anomaly investigation: You notice a spend spike. The old way: export hourly data, build a chart, find the spike, cross-reference with campaign changes. The new way: "My Meta spend spiked yesterday afternoon. What caused it? Was it one campaign or across the board?"

Budget reallocation: End of month, budget left over. The old way: export performance data for every campaign, build a comparison, identify the best ROAS. The new way: "I have €2,000 to reallocate. Based on this month's performance, which campaigns should get more budget and why?"

Cross-platform comparison: "Is my cost per lead lower on LinkedIn or Meta for B2B targeting this quarter?" This question normally means two exports, manual normalization (different column names, different date formats), and a comparison table. Now it is one question.

But What About My Spreadsheet?

Let us address the elephant in the room: some people love spreadsheets. They like the control, the formulas, the formatting. And that is fine — spreadsheets are not going away.

The point is not to eliminate spreadsheets. It is to eliminate the mindless parts of the workflow — the exporting, the reformatting, the column matching, the date range fiddling. Those are not valuable uses of your spreadsheet skills.

When you actually need a spreadsheet — for detailed modeling, for client deliverables with specific formatting, for historical data archiving — you can still use one. But you do not need to start every analysis there.

Think of it this way: a carpenter does not use a hammer for every task. They have a toolbox. AI-connected ad platforms are a new tool in your toolbox. Use the right tool for each task: conversational AI for quick analysis and ad hoc questions, spreadsheets for detailed modeling, dashboards for ongoing monitoring.

How to Make the Switch

If you are ready to stop the CSV export cycle, here is the practical path:

Start with one platform. Pick the ad platform you spend the most time exporting from — usually Meta Ads or Google Ads. Connect it to your AI tool through an MCP server. Spend a week using conversational analysis alongside your existing workflow. Compare the speed and accuracy.

Build your question library. The hardest part of switching is knowing what to ask. Start with the questions you already answer regularly: "How did campaigns perform this week?" "What is my best ROAS campaign?" "Which creatives are fatiguing?" Once you see how fast you get answers, you will naturally start asking more ambitious questions.

Replace one report. Pick your most time-consuming recurring report — maybe your weekly client summary or your Monday morning review. Do it entirely through your AI tool instead of exports. Time both approaches. The difference will convince you.

Then expand. Add more platforms. Try cross-platform questions. Use workflow templates for structured analyses. Within a month, you will wonder how you ever tolerated the CSV export workflow.

The setup takes about 2 minutes. Create a free account at app.adsuperpowers.ai, connect your platforms, and add the MCP server to your AI tool. Our guides walk you through every step.