Alpha launched in mid November 2025 with thirty two customers across small, mid market, and enterprise segments. Within three weeks, we saw strong match quality and early hiring outcomes, but also clear friction for users who were not already comfortable with LLM based products.

When Alpha closed, I led the synthesis and translated what we had learned into three design bets that would anchor the Beta experience:

Individually these look like UX improvements. Taken together they formed a trust framework for an agent that sat next to a manual product. We used them to focus design and research investment for Beta and to align PM, engineering, and content around what it would take to earn autonomy rather than simply request it. Beta research later confirmed that these three areas were where we needed to invest, and that weak design in any of them created adoption friction, especially for customers without strong affinity for LLM tools.

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Late in Q1 FY26, a research readout from several enterprise customers surfaced a hard constraint. They would not adopt AI Sourcing if they could not see and approve the candidates the agent wanted to contact on their behalf. They wanted a shortlist inside the workflows they already used, not an agent sending cold outreach in their name without an approval step.

We were one week from GA feature freeze when this landed. Product leadership initially wanted to ship without addressing it to protect scope. The UX team argued that ignoring it would put GA metrics at risk, and we were given twenty four hours to find a direction that could ship.

In that window, I pulled the lead UX team into a working session and reviewed their first idea, which added a new candidate review paradigm on top of the three we already had. That would have fragmented the experience further. I rejected that route and redirected the team to solve for shortlist inside the existing Projects surface, which was already the primary candidate management space in the enterprise product.

That afternoon we produced three Projects based concepts and put them in front of users. Feedback was unanimous in favor of the Projects direction. After the sessions, engineering flagged a feasibility concern. I approved a small pivot that preserved the core concept and handled the constraint. At the end of the US work day, our lead designer handed the work to a lead in Tokyo, who fleshed out interaction details and edge cases overnight. We woke up to a complete, validated feature that engineering could pick up before freeze.

Shortlist shipped as part of Projects in GA. Enterprise customers got the approval control they had asked for in a surface they already trusted, and we avoided introducing another review paradigm into an already complex system.

Within the first month of Alpha, it was obvious that the existing research cadence could not keep pace with the product. Studies often took weeks from question to readout. The AI Sourcing surface could change multiple times in a week, and we were shipping new flows before we had meaningful feedback on the old ones.

I asked my UXR manager counterpart to help me build a Rapid Discovery process that compressed this into a forty eight to seventy two hour loop. We staffed a small pod with a designer and a researcher, then coached them on ways to move faster without compromising objectivity.

With the introduction of AI tools into the process, we were able to test concepts very quickly. In addition to rapidly creating wireframes and prototypes, we were able to create interactive interfaces that focused users on specific elements that we were testing. For example, we created a prototype that tested an activity feed to judge the level of detail and cadence that felt trustworthy and useful to employers.

Rapid Discovery filled the gap between traditional research and the pace of an AI product. It kept the agent tightly coupled to real behavior instead of assumptions and helped us avoid large, unvalidated bets. Over time, other teams adopted the same pattern for their own fast paced work.