How to Connect Make to Excel (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of Make

You have an Excel workbook full of data — organization IDs, client accounts, user email lists — and you need it to talk to Make. Either you're pulling scenario metadata into the workbook for a report, or you're using the workbook as the source of truth for a bulk operation. Either way, the actual exchange of that data with Make's API is more friction than it looks.

Make is good at building visual automation workflows between apps. But Make itself is not easy to query from a spreadsheet. The default approach is to build a Make scenario that writes data somewhere you can then access — which is a bit like going around the barn to check the fence.

Below are the four common ways teams handle this. Only the last one scales.

Method 1: Manual Copy-Paste

The default for Excel users often starts with a CSV export from whatever Make-adjacent tool they're using — then they open it in Excel, reformat the columns, drop it into the right workbook. For data that lives purely inside Make's interface (organizations, team structures, usage stats), there's no export button — so it's straight copy-paste from the UI.

For a one-time lookup, this is manageable. But Make accounts at scale look different: a platform engineer keeping a provisioning checklist for 15 client orgs, a reseller comparing operations consumption across accounts, a RevOps lead documenting team ownership before an audit. Each time the data changes, the workbook goes stale. Someone goes back to Make and does it again from scratch.

That's not a workflow. That's just administration with no end date.

Method 2: Power Automate

Power Automate can call the Make API via HTTP action and write results into an Excel table. You configure the HTTP request with your Make API key, map each field in the response, and point the output to the right columns in your workbook.

Before you continue — do you know what an HTTP action is in Power Automate? A response schema? Dynamic content parsing? If those aren't concepts you work with regularly, this path is probably not for you. Skip to Method 3 or 4 and save yourself a few hours of confusion.

For those still here: yes, this works. Power Automate handles the scheduled trigger, the API call, and the Excel write-back. The flow runs, the workbook updates.

The structural ceiling is the same as any row-by-row automation: one call per organization, chained in an Apply to Each loop, with no native way to aggregate or filter across the full dataset.

You probably just need the usage numbers for your client workbook. You probably have no idea how to parse a paginated API response inside a Power Automate flow — and you shouldn't need to. So the flow either goes to whoever on your team builds Power Automate solutions, or it sits unfinished while you wait. And once you need to join data across multiple Make endpoints, you're looking at sub-flows and custom connectors, which is a separate project entirely.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable spreadsheet ↔ API workflows was a category of add-ons that let you configure column mappings and saved query templates. You picked your endpoint, tagged your fields, saved a config, ran it.

That was genuinely better than doing it by hand. Outputs were consistent, configs were reusable, and the team didn't have to rebuild formatting every run.

But you were still responsible for everything: the endpoint selection, the field mapping, the schedule, the filter logic, the column naming. The tool moved the data, but every decision about which data to move was still on you. And the moment Make updated their API response shape, your saved config silently broke until someone noticed the columns were off.

That's the previous generation. It got data through. It didn't do the thinking.

The Easy Way: Using SheetXAI in Excel

There is a different way entirely. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands what you're looking at, and through its built-in Make integration it can push to or pull from Make for you. No flow building, no field mapping, no debugging malformed responses. You just ask.

Example 1: Pull all Make organizations and teams into a structured inventory

Build a full Make org-and-team inventory in Excel — one row per team with columns for org name, org ID, team name, and team ID

Every org and every team under it lands in the workbook. No Apply to Each loop, no HTTP action, no sub-flow.

Example 2: Fetch 30 days of usage data for every org ID in column A

Pull 30 days of Make operations usage for all org IDs in this sheet and add a totals row at the bottom summing operations and data transfer across all organizations

The pattern: instead of building a flow to loop over column A and writing results to a separate location, you ask for the lookup, the fill, and the summary in one prompt. SheetXAI handles the per-row iteration and the aggregation inline.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with Make organization IDs or account data, then ask it to do one of the tasks above. The Make integration is included in every SheetXAI plan.