Can AI in SharePoint create a Microsoft List from an Excel spreadsheet?

Yes. Point Copilot in SharePoint at an Excel file in the documents library and ask it to create a list from it. AI in SharePoint reads the spreadsheet, infers the column structure, creates the list, and imports every row. Excel serial dates are converted to proper SharePoint dates automatically. The whole process takes about 30 seconds for a 10-row spreadsheet.

Can I query a Microsoft List in natural language?

Yes. Once Copilot in SharePoint is opted in on the site, the agent is grounded in your list data. Questions like 'which clients have a renewal date in April' or 'show me every blocked item' return matching rows. The agent respects column filters, date ranges, and category groupings the same way SharePoint's filter UI does, just expressed in plain English.

Can AI in SharePoint update a Microsoft List item through chat?

Yes. Tell the agent which item to update and what the new value should be. AI in SharePoint locates the item, checks the schema to confirm the value is valid, and applies the update. Existing SharePoint permissions on the list and the item apply — the agent cannot make changes the user would not have been able to make manually.

How does AI in SharePoint format a Microsoft List?

Through chat. Ask for pill formatting on choice columns, conditional row highlighting on date columns, status badges, email highlighting, or alternate row colouring. AI in SharePoint generates the view formatting JSON behind the scenes and applies it to the list. The user never sees or writes JSON.

What if I want to add a column to an existing list?

Ask in chat. 'Add a contract renewal date column as a date column' adds the column to the list. If the column needs choice options, describe them in the same prompt. The new column appears in the default view automatically and is editable through grid view immediately.

Can the agent reason across multiple lists?

Yes within reasonable limits. You can ask questions that span lists on the same site (where the user has access to both). For complex cross-list analysis at scale, wrap the query in a Skill so it runs the same way every time. Single-list queries work directly in chat without a Skill needed.

Do these capabilities work on lists in the modern experience only?

AI in SharePoint capabilities require sites opted into the public preview. The site needs Copilot in SharePoint enabled (either tenant-wide or per-site). Modern list and document library experiences are required. Classic experiences are not supported.

AI in Microsoft Lists, the practitioner walkthrough

Microsoft Lists is one of the most underutilised tools in Microsoft 365. Most teams create one, add a few columns, populate some rows, and never go deeper because the next level of capability (custom views, conditional formatting, automation) requires JSON or Power Automate.

Copilot in SharePoint changes that. Every advanced capability of Microsoft Lists — creation, querying, updating, formatting — is now reachable through chat. No JSON. No automation flow. No documentation deep-dives. The 7-minute walkthrough below shows the full pattern across one demo list.

Create a list from a spreadsheet in one prompt

The video starts with a clients Excel spreadsheet in the documents library and a request:

"Create a list from this file."

AI in SharePoint reads the contents of clients.xlsx, identifies the column structure (seven columns, ten rows), checks for an existing list with the same name to avoid duplicates, and creates the list. It also flags that the Contract Renewal column contains Excel serial date values and converts them to proper SharePoint date values during import.

About 30 seconds end to end. The list is created with the correct column types (client name, client type, address, contact name, email, status, contract renewal — each as the appropriate column type) and all ten rows are populated.

What changed compared to the manual flow: no list creation through the UI, no column-by-column type configuration, no Excel-to-SharePoint copy-paste, no manual date conversion. The user typed one sentence and pointed at one file.

Query list data in natural language

With the list populated, the second pattern in the demo is querying.

"Do we have any contracts being renewed in April?"

Copilot in SharePoint is grounded in the list data once the list exists on the site. The agent applies the filter (Contract Renewal in April), returns the matching rows, and surfaces the answer in chat. In the demo, the answer is: "Fusion Digital Group's contract renewal is on the 17th."

This is the equivalent of going to the list, opening the filter pane, selecting the Contract Renewal column, picking April. Through chat, it is one sentence.

The same pattern works for:

Date-range queries. "Which clients are renewing in the next 30 days?"
Status filters. "Show me every client with a Pending status."
Multi-condition queries. "Which vendors have a contract expiring this quarter and a status of Current?"
Aggregations. "How many clients have we got of each type?"

The agent reasons over the metadata, not just the cell values, so it can handle column-aware questions naturally.

Update records through chat

The third pattern is updating a record without leaving chat.

"Can you please update the status of Fusion Digital Group to current?"

AI in SharePoint:

Locates the Fusion Digital Group item in the list
Checks the Status column schema to confirm Current is a valid choice value
Applies the update

The change is reflected in the list view immediately. Existing SharePoint permissions on the list apply — the user cannot make changes through chat that they could not have made through the UI manually.

For bulk updates, the same pattern works:

"Set every contract with a renewal date before this month to status Expired."
"Tag all the vendors in New South Wales with the region East Coast."

Bulk operations through chat respect the same permission boundaries as bulk operations through the UI.

Apply view formatting through chat

The fourth pattern is making the list readable without writing JSON.

"Enhance the readability of this list."

AI in SharePoint reads the list structure, identifies the column types and likely visual hierarchy, then applies a coordinated set of formatting:

Status column gets pill formatting with green for Current and red for Non-current
Email column gets highlighted text styling to make addresses stand out
Rows with expired contracts get a coloured background to flag them
Rows with pending statuses get a different colour to flag those

In the demo, this transforms a flat black-on-white list into a visually structured view in about 20 seconds. Manually, this is roughly 100 lines of view-formatting JSON spread across three columns.

For more targeted formatting, ask for the specific change you want:

"Add green and red pill colours to the Status column."
"Highlight any contract renewing this month in red."
"Show email addresses as clickable links in blue."

Each one is one sentence; the agent generates and applies the JSON.

What this changes for Microsoft Lists adoption

Three patterns I see in client tenants once AI in SharePoint is enabled on lists.

Lists actually get used. Teams that had a few unmaintained lists from 2019 sitting in their site now ship new lists weekly. The friction of creating, structuring, and formatting a list used to be high enough that teams reached for SharePoint document libraries (less structured) or Excel (worse) for use cases that genuinely belonged in lists.

Existing lists get cleaner. Lists with months of accumulated bad data get tidied up through chat. "Find every item with a missing Owner column and assign the team lead as the default." A clean-up that would have taken a half-day of manual editing now runs in minutes.

Lists become the system of record. Once a list is easy to query and update through chat, teams start treating it as the canonical place where the information lives. The Excel spreadsheet copy stays in someone's OneDrive; the team works from the list.

The pattern beyond Microsoft Lists

The same conversational interaction extends to document libraries (covered in the document library article), site pages, and library metadata. AI in SharePoint reasons over content and metadata across the site, not just chat-anchored prompts.

For the higher-level pattern of using SharePoint.md to give every list and library on the site shared context (naming conventions, voice rules, validation logic), see the SharePoint.md file article. The combination of SHAREPOINT.md plus list-level chat interactions is what most teams will want for production use.

What to do this week

If your team uses Microsoft Lists at all, three actions.

Open one existing list and ask Copilot in SharePoint to "enhance the readability" of it. See what formatting it applies. If you like the result, the work was 10 seconds; if not, ask for specific changes.
Query the same list in natural language. Pick a question you would normally answer by opening the filter pane and clicking through. Ask it in chat. Note how the answer compares to what you would have got manually.
Update one record through chat. Pick a low-stakes update you needed to make anyway. See it go.

For teams that build lists regularly (vendor registers, project trackers, asset inventories, request logs), the create-a-list-with-voice walkthrough covers the build flow end to end. The pattern is the same.

If your team wants to learn this across a full day of hands-on work in your tenant rather than picking it up incrementally, the Copilot in SharePoint Immersive covers list creation, querying, updating, and formatting alongside Skills and the SharePoint.md context file.