Where should companies draw the line between AI assistance and AI action?

Hey everyone, I’m Soham, founder of Linkence

We’re building Linkence as an AI workspace layer for companies, something that can retrieve context from tools like Drive, Slack, Jira, Confluence, email, CRMs, ERPs, databases, and internal systems, then help teams take the next step.

One question I keep thinking about is:

Where should AI stop being an assistant and start being allowed to take action?

For example, I think most teams are comfortable with AI doing things like:

• finding the right document, ticket, email, or customer record

• summarizing project context

• drafting a customer reply

• preparing a report

• creating a suggested task or ticket

But it becomes more sensitive when AI starts to:

• update a CRM record

• send an email

• change a ticket status

• trigger an internal workflow

• query business databases

• operate as an internal agent for support, sales, HR, finance, or engineering

Our current view is that enterprise AI needs three things before it can act safely:

1. Strong retrieval : it must find the right context first.

2. Source citations : users should know exactly where the answer came from.

3. Approval and audit trails : sensitive actions should be reviewed, approved, and logged.

We’re also learning that standard SaaS connectors are only the starting point. Real companies often need AI connected to custom CRMs, ERPs, databases, approval flows, and internal tools. That makes the trust model even more important.

Curious to hear from founders, operators, and engineers here.

What would you let an AI agent do inside your company today, and where would you absolutely require human approval?