🏆 Community Challenge: What's working in hiring right now?

Hello everyone,

I can share, what I consider a basic tip, that usually saves some time from the initial search work.
Before jumping into any type of search and loosing valuable time, I suggest using an LLM like Claude, ChatGPT or Gemini. Get the job description for the role you will be sourcing for, attach it and ask the LLM to analyze it. 

Ask it to provide 10 different names for the same role, similar industries/sectors and a few boolean search strings ready to be copy pasted into Linkedin Recruiter keyword search. 

Personally, I’ve created an AI Agent for the company. Consultants/Recruiters/Sourcers just need to pick the document with the job description or the meeting notes that the client provided for the role and ask to create the boolean search strings. 
What usually took them between 30minutes to 1hour, they take just a few minutes to try different variations of the role and start searching right away.

Hope it helps and best regards,

Eduardo Alves - Sourcing & Talent Attraction Manager

Hello community!

On Building Trust - A brief, real conversation early in the process creates a human connection that automated emails simply can’t replicate, helping candidates feel seen and understood from the start. That initial trust carries through the rest of the process, so even when assessments, scheduling, and structured interviews are automated, the experience still feels thoughtful and personal.

Best,

Sandra Lief

FairchildHR

Hi ​@Eduardo Alves - Smart move starting here before ever opening a sourcer tool!

Decoding the JD first, pulling alternative titles, adjacent industries, and ready-to-paste Boolean strings, is exactly the kind of upstream thinking that compounds over time. And building it into an Agent so your team doesn't even have to prompt it manually? That's the real unlock.

Thanks for kicking us off with something immediately actionable. This is what peer learning is all about. 🙌

👉 Quick starting prompt for anyone wanting to try it today: "Analyze this JD. Give me 10 alternative titles, 3 adjacent industries, and 5 Boolean strings optimized for LinkedIn Recruiter."

Spot on ​@Sandra Rebecca Lief - This is such an important counterbalance to the automation conversation.

Everyone is talking about efficiency, but you're pointing at something that actually drives outcomes: candidates who feel seen early are more likely to stay engaged, show up authentically, and ultimately say yes. That early human moment acts as an anchor for everything that follows.

The sequencing insight here is key. Lead with a real conversation, then let automation handle the logistics. The process feels personal because it started personal.

Thanks for bringing the human element into this thread. Easy to overlook, hard to replace. 🙌

We’ve been experimenting with using AI for something similar. We’ve been taking traditional job descriptions and using AI to turn them into a “Day in the Life” narrative. It shifts the focus from a list of requirements to a more human, relatable view of the role.

Candidates get a clearer picture of:

• what their mornings look like
• who they’re working with
• what success actually feels like in the role

It’s been especially helpful for surfacing overlooked talent. When people can see themselves in the work, they’re more likely to raise their hand, even if they don’t feel like they check every box on paper.

Yay another great challenge! What works for me is a Success Criteria Reset at intake.
Before sourcing, I align with the HM on 3-4 non‑negotiable success criteria (observable outcomes, not skills or years), plus explicit “acceptable gaps’: what the hire can realistically learn in 3–6 months.

Each criteron is mapped to a sourcing clue and an interview question, so we assess consistently end‑to‑end.
As a result, I get faster screening, fewer late‑stage vetoes, and more “non‑obvious” candidates who actually perform.

Today’s candidates are tomorrow’s talent.
Every interaction is an opportunity to build our future workforce.
Ensure thoughtful documentation—each conversation counts.

I’m all about building trust with candidates. I like to use AI for scheduling, reminders, and administrative tasks so I can spend more time where it matters most—building real relationships.

In a world of AI, automation, and speed, trust has become the true competitive advantage in recruiting. Candidates don’t expect perfection—they expect honesty, communication, and respect. That’s what makes this work so rewarding—knowing my candidates trust me throughout the process.

I’ve also recently started using AI to create screening questions aligned to each job description, then refining them as needed. It’s helping me focus on true must-haves while also recognizing trainable skills and potential that might otherwise be overlooked.
 

​@StevenAmrhein I am going to use your strategy on future job postings. Thanks for sharing! 

🌟 What a thread this is becoming!

So many great perspectives coming in and some real patterns emerging across all of them.

​@StevenAmrhein Reframing the JD as a "Day in the Life" narrative is a brilliant way to widen the top of the funnel by making the role feel real and relatable rather than a checklist to qualify against. Paired with ​@Najat Andreozzi Success Criteria Reset, which anchors the whole process to observable outcomes rather than proxies like titles or years, and you have a sourcing and screening foundation that's genuinely built for finding potential over pedigree.

And threading through all of it is what ​@Tracie_Bouye Tracie and others keep coming back to: trust is the competitive advantage. Whether it's saving automation for scheduling so you can show up fully in conversations, or making sure every candidate interaction is documented thoughtfully as a long-term talent investment, the message is consistent. Speed and efficiency matter, but the human moments are what candidates remember.

Thanks for sharing also ​@Talent Architect_Cybersecurity!

Keep it coming, this thread is turning into a real playbook. 👏

Most recruiters are drowning in applications and still missing the right person. The problem isn’t volume, it’s signal.

What’s working for me right now is flipping the funnel:

Instead of filtering in, I’m deliberately filtering out early and then going deeper on a smaller group.

Practical example:

• I ignore 80% of CV noise and focus on evidence of outcomes (revenue generated, problems solved, improvements made)
• I headhunt adjacent industries where the skill transfers, not just identical job titles
• I use AI to summarise and compare candidates quickly, but the decision still comes from a human conversation

The real edge though is this:
I call people others won’t.

Candidates who are:

• Slightly off brief
• Not actively looking
• Overlooked because they don’t tick every box

That’s where the “hidden gem” actually lives. Not in the 200 applicants, but in the 20 people no one else bothered to speak to.

Hiring hasn’t gotten harder. It’s just louder.

So many applicants do not read job descriptions, but they often apply to job titles that include “keywords” in their resume, or have some AI services apply to position for them.  Am example from my Civil Engineering world is the phase “Project Manager”, For any Civil Engineer Project Manager position that I post, I receive applications from people that are “project managers” and have project managers skills but for industries completely not related to Civil Engineering.  These are not the people that I want for these jobs and the jobs are not usually the positions that the candidates want or will enjoy either.  There is a case to be made for people with “project manager” skills who would like to change industries and it is position for companies to hire someone and refocus their transferable skills, but that is a different initiative.   

I had an agent created that is able to scan a person’s resume and compare it all of my posted jobs and tell me if there are other positions that I have posted where the person might be a good fit.  

I think for sourcing candidates, I would like to create the agent that will allow me to find resumes and profiles that are a fit for the key requirements in my job descriptions that is a little stronger and more specific than the standard Boolean search, and can rate and grade my results by strength of match..

What’s actually working for us right now: treating applications as data, not decisions.

Most hiring funnels still assume the best candidates are in the top 10% of resumes. In reality, a lot of strong people are buried under noise- especially with AI-assisted applications.

So we’ve changed how we use the top of the funnel:

Instead of trying to perfectly “screen” resumes, we use AI to cluster and surface patterns across applicants:

• Who’s showing similar career pivots?
• Who has built things outside of work?
• Who’s progressed faster than typical in their context?

That’s helped us spot candidates who don’t look obvious in isolation, but stand out in comparison.

The second shift: we optimize for response, not just selection.

We actively reach out to candidates who are:

• Slightly underqualified on paper
• From adjacent or unconventional backgrounds
• Not actively applying

Because the best conversations we’ve had recently didn’t come from inbound-they came from people who weren’t even sure they were a fit.

AI helps us find signal faster. But the real advantage has been changing what we consider “signal” in the first place.

In a market full of noise, better filters help.
But better questions help more.

Just to add to “human-touch” area, I believe being honest and genuine and not too pushy is generally appreciated by candidates. Also, being responsive to candidate questions and request for status creates positive candidate experience. 

Also, preparing screening questions for each job, including requiring candidates provide examples of proficiency and success in different areas of competence required in jobs. This leads to better insights on candidate's qualifications.

A theme is emerging loud and clear in this thread: the filter is broken, not the funnel.

Whether it's ignoring CV noise in favor of evidence of outcomes, building agents to cut through keyword-matched misfits, or using AI to cluster patterns rather than rank resumes, everyone is pointing at the same root problem - optimizing for the wrong signals for a long time.

And the fix isn't more automation. It's better questions about what actually predicts success, and then letting AI help surface who answers those questions best.

In a market full of noise, better filters help. But better questions help more.

Hard to say it better than that ​@Tanvi Agarwal. 👏

Keep these coming, this thread is building something really useful. Thanks ​@Will B ​and welcome to the Talent Community @Tanvi Agarwal ​@Don Anderson & ​@jason sutton!

Strong contribution all around. I think there’s a common alignment on keeping the human touch in the loop. And I’m glad that we still see this and not just full throttle to automation and tools, rushing the process and losing the human feel.

I've seen recruiting teams save time and gain clarity when they use tools to filter resumes and organize applications, but what really makes a difference is combining that help with well defined human criteria. By automating repetitive tasks you can focus on conversations that reveal potential and cultural fit, as long as there is human review to prevent bias and ensure the candidate experience is respectful. Try small changes, measure the results and adjust; sometimes a simple tweak to the job posting or screening script greatly improves the quality of interviews. In the end, technology should expand people's perspective, not replace them.

Current applicants represent the human capital that will drive our future growth.
Every interaction is strategic to developing our future talent pipeline.
Ensure complete documentation: every discussion adds value to the process.

One thing that’s been working really well for me is using pipeline data and system visibility to drive better hiring decisions.

In a high-volume environment, it’s easy to assume there are “more options out there.” But when I break down the actual pipeline across systems like the ATS and sourcing platforms, the reality is often:

• Large volumes that don’t meet minimum qualifications
• Candidates stuck in earlier stages or not flowing between systems
• Strong candidates being passed over due to one perceived gap

What’s been effective is bringing that full picture into the conversation and reframing evaluation to focus on:

• What is truly required vs. what is trainable
• Where strong candidates may be getting overlooked
• How we can move forward with viable talent instead of waiting for perfect

When the challenge isn’t volume but lack of traction, we pivot—going back to grassroots recruiting, activating local networks, re-engaging warm candidates, and repositioning roles to better align with what the market is responding to. We’re also intentionally bringing back more high-touch tools like one-pagers, day-in-the-life content, candidate fact sheets, and quick guides to give candidates a clearer picture of the role and improve engagement.

That approach has helped improve alignment with hiring teams, maintain momentum, and uncover “hidden gem” candidates who may have otherwise been overlooked—especially in challenging markets.

These are all great! Challenger badges have been awarded to those who have contributed to date - keep them coming!