How a Gen Z job seeker used AI to stay organized while relationships, persistence, and professionalism earned her two job offers.
The Conversation that Started It All……
What happens when two Gen Zers and a Gen Xer walk into a wine bar? It sounds like the beginning of a joke, but it turned into the beginning of my AI journey.
During that conversation, I discovered how naturally digital natives incorporate Artificial Intelligence into their everyday lives. One young professional described how she was using AI to organize her job search. As a longtime recruiter at WineTalent, I was fascinated. I wanted to understand exactly how she was doing it—and what lessons the rest of us could learn.
This is her story.
Part 1: AI Didn’t Get Emma the Job
At the wine bar recently, one of the young women, Emma, had just finished her master’s program in Ag Science and was eager to land a job where she could meaningfully apply her knowledge. Emma is very articulate and, like many young professionals, is completely comfortable incorporating AI into her job search. She used her mastery of working with AI to build and manage her job search. Within six weeks, she landed a great job.
So, how did she do this? She managed her job search as a project. This was not surprising, given that university researchers often use AI for multiple projects at any one time. For Emma, creating a Job Search Project in Claude was completely natural. In the project, she gave AI pertinent information that could be used to craft a strategy. She also gave the project a solid goal—getting a job. AI managed the project. Emma managed the search. AI became her project manager, but Emma remained in charge of every decision.
Information Provided to AI:
- Types of jobs she wanted, including job descriptions from similar job postings
- Resume
- LinkedIn profile
- Related research
- Past job history—including jobs that may not have seemed necessary to list on her resume or LinkedIn profile
- Location preferences for jobs—including remote, hybrid, and in-person scenarios
Conference Attendance Was Key: While finishing her master’s degree, she attended a research conference. There, Emma talked to many potential employers and people who would become great network connectors for her. What she did next was a perfect example of making a plan, keeping track of information, and following up with contacts.
- Business cards were saved
- Key information from each interaction was recorded
- Any relevant information was noted that could help in the job search
- Spreadsheet created
Emma had worked with AI tools in the past. She fed this information into the program, and it already knew her work style, writing style, and research style, making it easier to understand this new information and Emma’s goals. As part of her Job Search Project, Emma used AI to create a Network Tracking Widget, allowing her to focus on following up with contacts and proactively connecting with new people she was referred to. The widget prioritized contacts and created a to-do list for her, allowing Emma to keep the lines of communication open and follow up on job leads and referrals.
Emma then started doing the work of following up with key contacts, checking on the status of potential job openings, providing useful information to them, and staying on top of the job hunt. She had daily to-dos that she completed on time. To keep AI useful, she had to continue giving it current information. She provided summaries of conversations, email correspondence, and new contact information so that everything could be captured.
This was very enlightening for me. I learned that AI only knows what you teach it. For this project, every meeting, every email, every phone conversation, and every referral became new information that Emma added back into her system. As her knowledge base improved, so did the quality of the guidance it could provide.
Nudge, Don’t Bother: As the job search started to heat up, she found that it was crucial to gently nudge contacts about past information or referrals they had mentioned, sometimes having to connect with a contact two or three times before receiving the information. The gentle nudge is an art, and I believe Emma has an innate ability to do it tactfully. This made me realize that AI was an important project manager, reminding her when to follow up. But Emma continued to hold the reins, deciding how to follow up.
Keeping AI Informed: I also learned that working with AI required Emma to consistently inform the program that she had followed up on leads, describe the conversations she had, and provide any meeting notes she took—both during and after each meeting. This was crucial to keeping the widget working well. While that was sometimes a burden, the payoff was worth it.
As Emma entered her second month of searching for a job, she started to get real traction with her contacts. She heard about an ideal job that a company had posted, and she was able to put her name in front of the hiring manager quickly. That got her on the interview list as a known entity. That really put her in the catbird seat.
As usual in a job hunt, when it rains, it pours. Another position was described to her by one of the contacts she connected with, and she was quickly put into the interview process. She now had two coveted jobs for which she was interviewing concurrently. One was with a smaller firm that had a casual interview process: a phone interview with the future manager, a small panel interview with key stakeholders in the company, and then the interview with HR. The other job was with a much larger company, and the process was, of course, lengthy. There was an initial phone interview, followed by a Zoom interview with the hiring manager. Then there was a full day of in-person interviews at corporate headquarters. Emma was in the conference room for hours, relying on the goodwill of the administrative assistant to keep her hydrated, informed of next steps, and given appropriate breaks throughout the eight-hour-plus day.
Being a consummate professional, Emma sent a thank-you email to everyone she met within 24 hours of leaving these meetings, including the kind administrative assistant. She also sent thank-yous to the people who had connected her with contacts at the companies. These emails included short tidbits of information or follow-ups when warranted. The emails, contacts, and notes from the interviews were all fed into AI immediately.
What I liked most about this follow-up was that she included the administrative assistant. She told me this without my prompting her. As a recruiter, I immediately smiled because I have always believed that everyone you interact with in a professional setting is equally important. You may not believe how often a secretary or assistant is asked for their thoughts on a job candidate. These staffers often pull a lot of weight in many organizations, and how you treat them matters.
Soon, the first job offer hit her inbox. Emma ultimately received offers from both companies. When weighing them, she consulted her widget to evaluate the pros and cons of each offer. The job she accepted was in line with what she wanted to do and where she wanted to do it, and the salary and benefits were good.
I asked her what she would do differently next time. She said it had been a burden to input all of the supporting communications. Next time, she could potentially connect the widget to her email, databases, and other apps, reducing the amount of manual data entry. But the widget worked well for this search, and she was happy with the results.
What I Learned About AI:
- For Emma’s Job Search: AI managed the project throughout the process. Emma managed the relationships.
- AI Only Knows What You Teach It: Good information in = good information out.
- AI Doesn’t Tell Time: Something that surprised me: AI doesn’t naturally know that two weeks have passed since your last conversation with someone. You have to build reminders and timelines into the system or tell it what has happened since your last chat.
- AI Didn’t Land the Job, Emma Did: While the widget helped her keep track of the job search, many of the things she did were Job Hunting 101. She collected business cards, kept track of important information, followed up with people, paid attention to ideas and referrals from her network connectors, and remained persistent about following up.
- Things That Had No Impact on the Job Search: This story shows that Emma followed up on leads and learned about jobs as the jobs were being posted. AI was not good at finding job listings, and Emma didn’t rely on it for that. If someone hopes a bot can find every job opening to apply to, those functions seem to be best handled within individual job-posting sites. From my experience, you can set up agents within most job sites, including WineJobs, LinkedIn, and Indeed.
Emma’s story has me appreciating the role AI played in her successful job search. More importantly, it taught me what AI can—and cannot—do.
Thinking about it, the fundamentals really haven’t changed.
Business cards don’t get people jobs.
Conversations don’t get people jobs.
Following up on those conversations gets people jobs.
Networking matters.
Preparation matters.
Follow-up matters.
Professionalism matters.
What Emma showed me was that AI doesn’t replace those fundamentals—it reinforces them. Used well, AI becomes an organizational partner, helping you manage dozens of moving parts while keeping your attention on what really matters: building relationships, preparing well, following through, and making good decisions.
That’s what impressed me.
Not the technology.
Emma’s dedication.
