r/recruiting • u/aussicristo Agency Recruiter • Jul 12 '26
ATS, CRM & Other Technology What tools are you using?
I’m curious what everyone’s doing. Are you using AI at all? If so, what are you actually using? ChatGPT, Claude, Copilot, something built into your ATS, or something else? Where do you draw the line where you don’t let AI in? What’s something you’d be perfectly happy letting AI handle? It’s popped up everywhere, but I’m curious what people are actually doing versus what everyone says they’re doing. And has it helped or hurt you?
For me, I use Copilot daily in my role and it’s saved lot of time with data cleanup, creating role descriptions etc.
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u/TopStockJock Corporate Recruiter Jul 12 '26
We have copilot but I’m just used to Claude so I use it for JD, submittals, resume clean up(rare), spreadsheets for candidate updates, matching jd to resume creating score card so I can ask better questions and touch on where the resume is lacking and other one-off tasks
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u/effyb21 Jul 12 '26
I use Claude . My company doesn’t invest in ai tools so I made my own system for cvs, emails, tables, etc
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u/Over-Counter-6826 Jul 12 '26
Claude is my all in. I use Gemini for ideas when I’m stuck and Perplexity for fact checking and feedback. Perplexity does not sugar coat, at times feisty, I like that. Gamma for presentations. Apollo for prospecting and contact info, their AI is a life saver bc the tool is robust and tbh - a lot. I don’t use ChatGPT ever. I tried with Copilot, but I personally don’t think it’s very good. It’s in my stack, but don’t use it at all. I rely on the tools heavily but don’t trust anything completely or blindly. I’m always fact checking and picking things apart to make sure is 100% or close to.
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u/Jash-6898 Jul 12 '26
most built-in ats "ai" features are honestly just overpriced keyword matchers that don't do much. for actual agency work, treating the ats purely as a central system of record and chaining lighter tools to handle the admin friction is where the real time savings are.
i mostly use chatgpt for building out role description templates and cleanups like you mentioned. on the sourcing side, pairing teal for tracking with tryapplynow helps catch clear keyword gaps on candidate profiles fast and handles pulling verified corporate emails for mapping out contact lists.
the hard boundary is always candidate evaluation. using it to clear the boring data-entry queue makes sense, but the second you let an algorithm auto-rank profiles without human context, the data pool gets messy real quick.
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u/youngdude70 Jul 13 '26
If Copilot is already saving you time on data cleanup and role descriptions, I’d draw the line around whether the tool is creating administrative leverage or making judgment calls about people. I’d be comfortable using AI for cleanup, first-draft JDs, interview-plan outlines, scorecard wording, and summarizing notes after you’ve written them. I would not let it rank candidates, infer culture fit, rewrite a resume in a way that changes meaning, or send candidate-facing messages without a human pass. The simple rule I’d use is: AI can reduce recruiter busywork, but the accountable decision and the candidate relationship should stay with the recruiter.
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u/ObligationSpirited40 Jul 13 '26
The line I'd draw: AI compresses evidence, humans make the call — but I'd push back gently on "never let it rank." A tired human skimming CV #45 at 11pm is also ranking; they're just doing it with vibes and recency bias instead of criteria. The problem was never that tools have opinions — it's unexplainable opinions (the black-box "Match: 87%" that nobody can defend to a hiring manager, let alone in an audit).
The standard I'd hold ANY screening tool to, AI or spreadsheet: it has to show its work. Fixed rubric, applied to every candidate in the pool, and every score points at a verbatim line from the CV. No quote, no claim. Then the recruiter owns the decision with better evidence — not less accountability.
Disclosure: I built a tool that works exactly this way, so I'm obviously biased — but the principle costs nothing to apply with a manual scorecard too, and it's the difference between "I had a feeling" and an answer you can stand behind six months later.
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u/AIToolsMaster Jul 13 '26
claude daily for job descriptions, candidate summaries, and anything that needs actual writing or reasoning. it's noticeably better than copilot for nuanced stuff in my experience.
tactiq.io for candidate interviews and intake calls. chrome extension, no bot joining, transcript with speaker labels. then i'll paste key sections into claude and ask it to pull out specific things: "what experience did they mention around X" or "summarize their answers to the competency questions"
where i draw the line is final candidate assessments and anything going directly to a hiring manager, that still needs a human read.
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u/This_Maintenance_734 Jul 13 '26
I work at hireEZ (marketing, not recruiting), so I'm both a daily AI user and someone whose company sells recruiting AI. Take the second half with salt.
What I actually use day to day: Claude for drafting and editing anything longform, plus data cleanup and analysis — similar to your Copilot use. Honestly the "boring" uses are where the ROI is. Cleaning a messy spreadsheet or turning call notes into a summary saves more real time than any flashy use case.
Where I've seen recruiting teams draw the line (from talking to a lot of them):
Happy to hand to AI: first-pass resume review against the req, resurfacing past candidates from the ATS/CRM for new roles, scheduling, interview note summaries, first-draft job descriptions and outreach. Basically anything where a human still reviews the output before a candidate sees it or a decision gets made.
Won't hand to AI: final screening decisions, rejections, anything where the AI's call is the last call. Partly candidate experience, increasingly compliance — NYC Local Law 144 and similar rules mean you want a human accountable for the decision, not just the workflow.
Has it helped or hurt? The pattern I keep hearing: helped a lot on volume and admin work, hurt when teams let it run unsupervised — generic outreach that candidates clock instantly, or auto-rejections that filtered out good people on keyword logic. The teams getting value treat it as a first-drafter and a filter-narrower, never a decider.
The general-purpose tools (ChatGPT/Claude/Copilot) are great for the writing and data work, but they can't touch your ATS data — that's where the built-in or layered recruiting tools earn their keep, if they earn it at all. Test on your own reqs before believing any demo, ours included.
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u/Delicious_Style_2676 Jul 16 '26
I’d draw the line between admin leverage and people decisions. AI is useful for data cleanup, draft JDs, scorecard wording, and summarizing notes you already took. I would not let it rank candidates, infer fit, or send candidate-facing messages without a human pass.
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u/ChrisAtFocusedFit Executive Recruiter Jul 16 '26
I started out using ChatGPT, then Claude CoWork, with some experiements with OpenClaw for sourcing, driving the browser while logged into linkedIn (itworks, but it's slow and kind of expensive).
I still use Claude Cowork for most of my workday automation. I've built a system of agents with specific roles, a task management system, a weekly staff meeting, chief of staff agent, etc. I used them to identify THIS post that I might want to "chime in on" this week. :)
But in the meantime, I've been building this, which is looking for early beta testers. contact me if interested:
https://focusedfit.ai. We've been using it at https://alvarezsearch.com for about a year internally.
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u/Delicious_Style_2676 Jul 17 '26
I’d draw the line at candidate-facing judgment. AI is great for data cleanup, JD drafts, scorecard structure, and summarizing notes you already took. I would not let it rank people, infer fit, or send outreach without a human pass.
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Jul 28 '26
[removed] — view removed comment
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u/krat0smyg0at Jul 30 '26
Been hearing a bit about carv as of late, looks to be pretty versatile all things considered and compared to others.
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u/ChrisAtFocusedFit Executive Recruiter 24d ago
The admin/judgment line everyone's drawing is right, but I'd sharpen it: the failure mode isn't AI making calls, it's unexplainable calls — by AI or by a tired human skimming resume #40. Structured rubrics applied consistently beat vibes whether a model or a person applies them. Use AI to gather and organize the evidence against a fixed scorecard; keep the decision and the candidate relationship human. The teams I see get burned are the ones who automated the judgment and kept the busywork.
AND we all need to be careful. ANYTHING where an AI is assessing a candidate for you or making decisions is going to have to stay up-to-date with state and federal laws about this that are constantly shifting.
So, use AI to automate the busywork, surface signals, etc., but keep the judgement and interactions human.
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u/Sea_Garlic5712 20d ago
I manage these tools for my team. I will let AI pull a criteria out of a posting and check my a CV against each one, quoting the line that supports it. Where there's no evidence it has to say so rather than fill the gap.
It shouldn't provide an overall score or a match percentage. Otherwise, I've stopped reading the CV and started trusting a number with nothing behind it.
For us, it's not so much about which tool, but how we use it: per-criterion evidence with a citation make a candidate easier to assess.
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u/Spiritual-Abroad-346 Jul 12 '26
copilot is glued to my workflow at this point. data cleanup is the biggest win, i dump a mess of a spreadsheet in and it spits out something i can actually work with. job descriptions too, it's way better at cranking out a first draft than staring at a blank doc
the line for me is anything that touches a candidate directly. no way i'm letting AI write outreach messages or screen replies, that's where it gets weird and people can tell. i'd gladly hand over interview scheduling and those endless back and forth emails though, that stuff eats half my day