How Chiefs Of Staff Use AI Browsers for Candidate Sourcing

This guide is for chiefs of staff who run candidate sourcing. It explains how an AI browser like Strawberry runs the workflow given the tools a chief of staff actually uses every day, what the output should look like, and where the workflow fits in the chief of staff's week.

Why this matters for chiefs of staff

A chief of staff spends time on this: shadow the CEO across every meeting, surface what is being lost in the noise, and turn decisions into shipped work. The pain that makes candidate sourcing feel slow is real: context lives in every channel at once; the chief of staff is the only one with cross-functional visibility but no time. The reason an AI browser helps is that chiefs of staff already use multiple surfaces (Notion, Gmail, Slack, Google Workspace, the company CRM and analytics tools) to do this work, and the browser is the only tool that can read across all of them and produce a finished output.

What success looks like

The goal of candidate sourcing is to build a shortlist of 10-30 candidates who match the role and have at least one signal of openness. For a chief of staff, success metric is concrete: 30% reply rate to first outreach, 5+ first-call conversions per 30 sourced. A finished candidate sourcing run should look like this: an exec brief, a meeting recap with owners, or a synthesised view of cross-functional state.

Signals candidate sourcing needs

The workflow needs these signals: current role and tenure; recent role changes (often visible on LinkedIn); GitHub or content output for technical roles; company stage match (someone leaving a Series B is more likely to talk to a seed-stage co). For a chief of staff the practical question is which signals come from the tools already in the stack (Notion, Gmail, Slack, Google Workspace, the company CRM and analytics tools) versus what the browser has to fetch. Strawberry reads the in-stack tools through native integrations and uses the browser for the rest (LinkedIn, news, company websites, search). The chief of staff stays in one surface.

Paste-ready Strawberry prompt

I'm a chief of staff. Run candidate sourcing for me using Notion, Gmail, Slack and the browser, then save the draft.

What a finished candidate sourcing output looks like

Concrete example, not a placeholder:

• Role: Founding Engineer (Stockholm or remote EU)
• Candidate: Marek Novak - Senior Engineer @ Klarna, 4 years
• Fit: 5/5 (worked on payment systems, contributed to Rust open source, recent talk on type-safe APIs)
• Opening line: noticed his RustConf talk on type-safe API contracts and our backend lead's tweet about Marek's library
• Contact: LinkedIn DM + GitHub email

When this works, and when it does not

This workflow is right for chiefs of staff when the work is repeatable and crosses multiple tools. It is wrong when anything that requires the CoS to become the bottleneck on a function that should own itself. In that case, the chief of staff should keep doing the work manually until the pattern is clear enough to automate.

Three mistakes to avoid

• Spray-and-pray DMs that mention nothing specific
• Missing the obvious signals (someone just posted 'thinking about a change')
• No quality bar - putting 200 names on the list to look productive

Caveats

Strawberry holds back on sending email, updating CRM records, or changing shared systems until a human approves the action. Treat the agent as a fast first-draft author, not an autopilot.