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Data types

Selling Slack data for AI training

How companies license Slack workspace history to AI labs, covering what's valuable, what to exclude, how exports and de-identification work, and key risks.

Two colleagues seen from behind working on laptops at a conference table overlooking a small-town main street

For many companies, Slack is where the real work is discussed. Requests arrive, questions get answered, decisions get made, problems get escalated, and context gets shared. That makes a few years of Slack history one of the more interesting datasets a company can offer an AI lab, provided it's scoped and de-identified carefully.

Why Slack data is valuable

Labs building AI systems that collaborate and coordinate need examples of real collaboration. Slack conversations show:

  • Decision-making in context: people weighing options, citing constraints and agreeing on a path
  • Coordination across roles: sales looping in operations, support pulling in engineering
  • Exceptions and escalations: what happens when the standard process doesn't fit
  • Tacit knowledge: the "here's how we actually do it" that never makes it into documentation

Compared with formal documents, chat captures the reasoning between the lines. Compared with email, it's typically higher volume and more informal, which shows how work actually flows day to day.

Which parts of a workspace matter most

Not all channels are equal. Generally, the most valuable content is in:

  • Project and team channels where substantive work is discussed
  • Cross-functional channels (e.g. deal desks, launch rooms, incident channels)
  • Support and operations channels showing problem → diagnosis → resolution
  • Threads with long back-and-forth rather than one-line acknowledgements

Lower-value content includes bot and integration posts, social channels, announcement-only channels, and very short or emoji-only messages.

What to exclude

Before any export, agree on exclusions. Common ones:

  • HR, people and compensation channels
  • Legal and privileged discussions
  • Board, fundraising and M&A channels
  • Direct messages, which many companies exclude entirely and which Slack's own export rules treat differently (see below)
  • Channels covered by customer confidentiality obligations (for example, shared Slack Connect channels with customers)
  • Personal or social channels

Exclusions should be written into the scope document so both you and the buyer know exactly what's in the dataset.

How Slack exports work

What you can export depends on your Slack plan and settings, and Slack's policies change over time, so confirm current details with Slack or your admin. In general:

  • Workspace owners and admins can export public channel history on most plans.
  • Exporting private channels and direct messages typically requires a higher-tier plan and may require an application to Slack, and it has to be consistent with your own policies and applicable law.
  • Exports usually arrive as JSON files organized by channel and date, plus references to files and attachments.

For most AI data deals, public and selected private channel history is enough. DMs are frequently out of scope.

De-identifying Slack data

Chat is one of the harder data types to de-identify because identifiers hide in free text. A thorough process usually includes:

  1. Replacing user IDs and display names with consistent pseudonyms (e.g. "Person 14"), so you can still follow who's talking without knowing who they are
  2. Scanning message text for names, emails, phone numbers, addresses, account numbers and other identifiers
  3. Handling @mentions, signatures and pasted content, including forwarded emails and copied customer details
  4. Treating attachments separately, because file names and contents can contain identifiers, and images may need to be excluded or reviewed
  5. Reviewing samples by hand to catch what automated tools miss

The goal is to keep the workflow intact ("Person 3 asked Person 7 to approve the revised quote for Customer 22") while removing identity. Read our full de-identification guide for more.

Employee considerations

Your employees wrote these messages, so it's worth thinking about:

  • Your existing policies. Many acceptable-use policies already state that workplace communications belong to the company and may be reviewed, but check what yours actually says.
  • Applicable law. Employee privacy rules vary by state and by country. If you have employees outside the US, additional rules may apply.
  • Communication. Some companies choose to tell employees about the program and how de-identification protects them. Transparency tends to reduce concerns.

This isn't legal advice. Have counsel review your policies and the proposed agreement. See is it legal to sell company data?

What affects the value of Slack data

  • History: several years of continuous activity is generally better than a short window.
  • Activity level: substantive daily discussion across many channels beats sparse usage.
  • Breadth: multiple teams and functions represented.
  • Connection to other systems: Slack paired with docs, tickets or CRM shows complete workflows and can increase value.
  • Exclusions: removing the most active channels reduces value, so scope thoughtfully.

A practical checklist before you start

  • Confirm who in your company has authority to approve the program (often the CEO, with input from legal and IT).
  • List your channels and mark each one as include, exclude or review.
  • Check your Slack plan's export options with your admin.
  • Review your acceptable-use and privacy policies, and any customer contracts that mention shared channels.
  • Decide whether your team or the partner's engineers will run the export and de-identification.
  • Agree on how you'll review a de-identified sample before anything is delivered.

Getting started

To get an offer you only need rough estimates: roughly how many messages (or channels and years of activity), which other systems you'd include, headcount and years in business. You don't need to export anything up front.

DataOffer packages and de-identifies Slack data (or supports your team in doing so), gets competing offers from multiple labs, and brings you the best one. You choose which channels are included, and nothing is shared until you approve the buyer, price and terms.

Ready to see what your data is worth?

Share rough estimates (systems, approximate volume, years of history, headcount) and we'll come back with competing offers from AI labs. No upfront cost, no commitment, and nothing is shared until you approve.

This guide is general information, not legal, tax or financial advice. Figures and ranges are illustrative; talk to qualified advisors about your situation.