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LinkedIn Recruiter  ·  Generative AI  ·  0 to 1

Launching LinkedIn Recruiter's first generative AI product

Recruiters were spending hours researching candidates and rewriting the same outreach. The bet was that AI could do the personalization at scale without sounding generic. I led the design team that took it from first concept to a live pilot.

Role
Sr. Manager, Product Design
Team
8 designers across Recruiter (5) and Enterprise Jobs (3)
Partners
Product, Engineering, Data Science, UX Research, Product Marketing, Legal, Program Management
Scope
0-to-1 launch, piloted with Recruiter Corporate customers

Pilot results

40%

higher acceptance for AI-assisted InMails than standard single messages

11%

faster time to acceptance

39%

more accepted InMails with automated follow-ups

82%

of generated messages rated good in product

Adoption: 75% of pilot contracts and 25% of seats tried the feature, and 16% of single, non-templated InMails from pilot customers used AI-generated content.

Inside the work. These are working artifacts from the build: explorations, review rounds, and iterations on the way to launch. They're shown to make the decisions visible, not just the final pixels. Product screens are illustrative recreations; the originals are confidential.

01  StrategyThe bet

Personalization was the problem, not writing speed.

Recruiters told us the same three things: researching each candidate took too long, staying on brand was hard, and personalizing at scale was nearly impossible. Before designing anything, we ran foundational research to find where AI would actually earn its place.

Where could GenAI make messaging more efficient?In personalizing the standard messages recruiters already send.
What worried recruiters about AI-written outreach?That messages would read as generic and get rejected.
What most limited how many candidates they could reach?The time it took to compose each message.

Data first, then design.

The quality of the output depended on what we fed it. I partnered with Data Science, Product, and Marketing to decide which data should shape a message, starting from what recruiters already trusted: their own saved templates, the candidate's profile, job details, and company information. We validated output quality against sandbox data before committing to the final approach.

Because this was the first generative AI experience in Recruiter, we designed it as a pattern that could scale to later releases, not a one-off feature.

02  ExecutionHow it shipped

An operating rhythm built for a high-visibility launch.

Every design decision had a clear path to a decision-maker. I owned the quarterly design planning cycle with cross-functional partners and set the review cadence for the team.

WeeklyDesign critiques and design reviews with product managers and engineering
OngoingShow-and-tells with the broader Recruiter team
QuarterlyDepartment office hours with the Sr. Director and design jams with the VP and pillar leads
MilestonesExecutive and VP marketplace reviews

Two hard calls.

Risk

A design system in flux

Components we needed were changing during the build. We worked with the design system team to stabilize what the launch depended on, built on mature components, and planned the larger Recruiter update separately so the launch didn't wait on it.

Obstacle

Disclaimers

AI-generated content needed clear disclosure. We designed it to inform recruiters without interrupting their flow, then used post-launch data to simplify the experience and remove the banner.

03  The experienceWhat recruiters do

Five steps from blank message to personalized outreach.

Select a step to walk through the shipped flow.

About the candidateJob detailsCompany info
EditSave as templateSend

The recruiter opens a new message to a candidate from search or a project pipeline.

Illustrative recreation of the shipped experience.

04  IterationAfter the first ramp

Adoption lagged, so we changed the front door.

The first ramp showed recruiters weren't finding the feature. The value was there once they used it; the entry point was the problem. We reworked onboarding before expanding.

Version A  ·  First ramp
  • Recruiters had to click a draft prompt to start onboarding
  • Disclaimer sat on top of the experience
  • No persistent entry point once a message was typed
Version B  ·  Revised
  • Onboarding appears as soon as the composer opens
  • Disclaimer removed after the post-launch review
  • Persistent personalization entry point in the toolbar

Illustrative recreation of internal iterations.

Customer voice

“This helped me send detailed personal emails to candidates and save time. I am so glad you created this!”Pilot customer, in-product feedback
“I really appreciate the AI feature especially for those who may have trouble typing for someone like myself.”Pilot customer, in-product feedback
“It saves time by letting the Recruiter create a customizable message that can be sent to candidates quickly.”Pilot customer, in-product feedback

In the press

My team designed and launched the first phase in 2022. LinkedIn continued to evolve and expand the experience, with public coverage as it grew.

05  ImpactWhat it changed

A pattern for AI in Recruiter, not just a feature.

The launch proved recruiters would adopt AI when it personalized their own work rather than replacing it. The data foundation, the onboarding pattern, and the cross-functional review model laid the groundwork for how the experience expanded.

Karyn Nesby

Contact

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