Most AI in HR content falls into one of two categories. There's the vendor demo where everything works perfectly on a clean dataset with zero edge cases. And there's the thought leadership post that says "AI will transform HR" without showing a single screenshot.
I wanted something different. Over the past few months, I've been collecting real examples from real people leaders who are actually using AI in their day-to-day work. Not prototypes. Not pilot programs that got approved but never launched. Actual workflows that are running right now, built by people who also have to answer Slack messages, sit through calibration meetings, and remember to submit their own expense reports.
What I found surprised me. The most interesting AI use cases in HR aren't the ones making headlines. They're not "we replaced our recruiting team with a robot." They're more like "I stopped spending two hours redacting salary data from PDFs" and "my AI agent says a polite no to sales emails so I don't have to." Small, specific, and genuinely useful.
Here are 12 of my favorites, organized from simple to sophisticated, from four practitioners who shared exactly what they built and how it's working.
Real AI in HR examples
These aren't anonymous case studies. These are named people leaders who shared their workflows publicly.
- Melissa Theiss is the Head of People Ops at Kit. She documented four levels of AI use cases, from basic drafting all the way to multi-tool automations involving Slack, Google Sheets, and Notion.
- Sarah Sheikh Jauhar is a Fractional People Leader who builds AI-powered people operations for startups. She built an AI recruiting agent for a Series A client and shared six weeks of performance data publicly.
- Alice Browne is Managing Partner of People at Brainlabs. She documented a week of using Claude Cowork for people operations tasks, from simple drafts to building an interactive org design tool from scratch.
- Emily Mabie is a Senior Learning Designer at Zapier. She live-built AI workflows and agents, showing how non-technical HR professionals can automate repetitive tasks.
12 real examples of AI in HR
1. Drafting a company announcement in your brand's voice
Who: Alice Browne, Managing Partner of People at Brainlabs
What she built: Brainlabs was rolling out Claude company-wide and needed a post for their #companyannouncements Slack channel. Alice gave AI the context, the date, a rough sense of tone from their tone guide (saved as a Notion skill), and the content doc. Claude drafted the whole announcement, decided what to lead with, what to save for the channel itself, and linked to the project-specific Slack channel by ID so it would render as clickable.
What works: This is the lowest-friction entry point for AI in HR. You're not replacing a system or rebuilding a workflow. You're taking something you were going to write anyway and cutting the drafting time. Alice said she edited maybe two sentences. The AI handled the structural decisions (what to include, what to leave out, what order to present information) that normally eat up most of the drafting time.
Key takeaway: Start with a task you already do manually that doesn't involve sensitive employee data. Internal announcements, onboarding welcome messages, and team updates are all safe first experiments. Give AI a tone reference and a source doc. See how close it gets.
2. Automating email responses with AI
Who: Emily Mabie, Senior Learning Designer at Zapier
What she built: A Zapier workflow that triggers every time a new email hits her Gmail inbox. An AI step reads the email body, analyzes it, and drafts a reply in Emily's voice using a sample of her real writing. The draft gets attached to the original email thread. Nothing sends without Emily reviewing it first.
What works: Emily built this live in front of 400 HR professionals in under 10 minutes. The key design choice: AI creates a draft, not a sent email. She retains full control. The version she uses daily is tuned to identify sales pitch emails and write a firm but polite no. She said it easily saves her an hour a week. But more than the time savings, she called out the cognitive load reduction. Each small email decision takes energy, and offloading the first draft of those decisions frees her up for work that actually requires her brain.
Key takeaway: The biggest productivity gains from AI often aren't dramatic. They're small, repetitive decisions that drain your focus over a full day. Email is the most universal example.
3. Redacting sensitive documents without design tools
Who: Melissa Theiss, Head of People Ops at Kit
What she built: HR teams regularly need to share documents that contain sensitive sections. Compensation expectations on an otherwise-shareable job description. Salary bands on a planning doc. Historically, Melissa's approach was the same as most people ops teams: open the PDF in Preview, drag white or black shape boxes over the sensitive parts, and hope nothing bleeds through. With Claude Cowork, she describes what needs to be removed in plain English. AI runs a Python script to redact the specified sections from the PDF.
What works: You don't need to know Python. You don't need to open a design tool. You describe what to remove in the same way you'd tell a colleague. "Remove the compensation expectations section from page 2." That's it. The output is a clean, properly redacted PDF.
Key takeaway: AI in HR doesn't always look like a chatbot or a dashboard. Sometimes it looks like removing one manual, annoying step from a process you do every month.
4. Running compensation analysis with an AI co-pilot
Who: Melissa Theiss, Head of People Ops at Kit
What she built: Melissa uses Claude Cowork with an HR plugin that comes pre-loaded with slash commands like /offer-letter, /onboarding-plan, and /comp-analysis. She configured it with Kit's actual benefits, equity structure, and comp philosophy so the outputs aren't generic. For comp work specifically, she uses it alongside their existing job architecture and salary tier system to calculate formulas for new sub-tiers, apply their quartile spread formula to new rows, and sanity-check data pulled from their compensation tool.
What works: This is the use case that makes comp & benefits people sit up. AI isn't replacing the compensation philosophy or the decision-making. It's doing the spreadsheet math faster and more reliably. Melissa's example of deriving the quartile spread formula from a sheet with values only (no formulas) and then applying it to new data is the kind of task that normally takes an hour of careful work. AI handles it in seconds, and she verifies the output.
Key takeaway: AI works well as a calculation partner for comp analysis because the formulas are logical and verifiable. You can check the output against known values. That makes it lower-risk than using AI for subjective decisions.
5. Creating interactive compliance training scenarios
Who: Melanie Naranjo, CPO at Ethena
What she built: Ethena's training platform uses AI to generate interactive scenario-based compliance training. Instead of static slides with bullet points, trainers create dynamic conversations. In the demo, Melanie built a scenario about workplace AI misuse: two coworkers discuss using ChatGPT to age someone's photo as a birthday joke, then the learner answers whether this is appropriate. The training renders as a Slack-style or text-message-style conversation, making it feel like something employees would actually encounter.
What works: The insight here is about where AI adds value in L&D. The hard part of compliance training has never been formatting slides. It's designing scenarios that feel realistic enough that employees actually engage with them. By handling the visual layout and interactivity, AI frees the training designer to focus entirely on the scenario design and the learning objectives. Melanie also pointed out that you can use ChatGPT to brainstorm scenarios (asking for examples of how AI could go wrong in the workplace), then plug those into the training builder.
Key takeaway: AI in L&D works best when it handles production (layout, formatting, interactivity) and the human handles strategy (what scenarios matter, what the right answer is, what the learning objective should be).
6. Building an anonymous policy chatbot with usage analytics
Who: Melanie Naranjo, CPO at Ethena
What she built: A chatbot that only pulls answers from policies the company uploads. It never searches the internet. It never hallucinates. Employees ask questions like "What is our PTO policy?" and get answers sourced directly from their company's uploaded documents, with links to the original policy.
What works: The chatbot itself is useful. But the real value is the analytics layer. Because employee queries are anonymous, people feel safe asking questions they might not ask a human. "How many times can I ask someone out before it's against policy?" is a real question someone might need answered but would never walk into HR and ask out loud. Ethena captures this data in aggregate: what topics are trending, what questions have no policy to match against (a signal you need to create one), and which policies are generating the most confusion. Melanie shared an example where a spike in AI-related questions, with no matching policy, was a clear indicator that the company needed an AI use policy.
Key takeaway: The gap analysis is the killer feature. A chatbot that answers questions saves employee time. A chatbot that reveals which questions employees are afraid to ask in person, and which policies don't exist yet, saves the entire people team's strategic planning.
7. Designing a hiring assessment from scratch
Who: Alice Browne, Managing Partner of People at Brainlabs
What she built: Alice wanted to test for AI readiness in hiring but couldn't find an existing assessment software. She used Claude to design one from scratch: the scenario, the scoring rubric, the assessor instructions, and guidance for interviewers. There was significant back and forth ("lots of tweaking the details"), but the result is a full Notion doc with a live brief, four scoring dimensions, and interviewer guidance. She was piloting it on internal team members at the time of her post.
What works: Most people think of AI as a content generation tool. This example uses it as a design thinking partner. Alice wasn't asking AI to write a job description. She was asking it to help her think through what competencies matter, how to measure them, and how to structure the evaluation so different interviewers score consistently. That's a fundamentally different (and more valuable) use case.
Key takeaway: AI is a surprisingly effective partner for building rubrics, scorecards, and evaluation frameworks. The back-and-forth iteration process (not the first draft) is where the value lives.
8. Sending bulk personalized manager notifications via Slack
Who: Melissa Theiss, Head of People Ops at Kit
What she built: After confirming outstanding performance grant awardees, Melissa needed to individually notify each manager with details about their direct report's grant, a script for communicating it, and a deadline. She gave Claude Cowork the Google Sheet with confirmed awardees and grant amounts, the Notion SOP from the process, and an example message from the previous cycle. Claude drafted and sent all the necessary Slack DMs.
What works: This is a modern mail merge, but smarter. Each message was personalized based on the manager, the employee, and the grant details. Melissa did extra verification (checking Slack IDs matched the right managers, sending a test message first) because the information was sensitive. That's exactly the right approach. AI handles the drafting and distribution. The human handles the verification of sensitive data.
Key takeaway: For any HR process that involves sending personalized messages to multiple people based on spreadsheet data (comp notifications, onboarding assignments, review reminders), AI plus connected tools turns a multi-hour manual process into a 15-minute review.
9. Building a Slack task management agent
Who: Emily Mabie, Senior Learning Designer at Zapier
What she built: A Zapier agent triggered by a specific Slack emoji reaction. When Emily reacts to a Slack message with her company's "will do" emoji, the agent reads the message, determines urgency and estimated time, checks her Google calendar for availability, and books a focused work block linked back to the original Slack message. Built live in under 10 minutes during a webinar.
What works: Emily drew a critical distinction during the demo. A workflow runs the same way every time (email comes in → AI analyzes → draft gets created). An agent can make inferences. If someone reacts to a message that doesn't actually contain a task, the agent can decide not to schedule anything. If a task says "don't start until next month," the agent can adjust accordingly. That flexibility is what makes agents different from workflows, and it's why they need guardrails. Emily's agent could only access Google Calendar, only triggered on her reactions, and only scheduled during her working hours.
Key takeaway: The workflow vs. agent distinction matters. Workflows are predictable and locked down. Agents are flexible but need boundaries. For sensitive HR data, workflows are usually the safer choice. For personal productivity tasks, agents shine.
10. Automating relationship-building across a distributed team
Who: Alice Browne, Managing Partner of People at Brainlabs
What she built: Being new to the EMEA leadership team, Alice wanted to connect with people across the region, not just whoever happened to be in her calendar already. She asked Claude to pull from Brainlabs' internal team list on Notion and recommend four people each month for coffee chats. The criteria: people she's unlikely to encounter in her day-to-day. It now sends her a list on the first Monday of each month for approval, then schedules the meetings automatically.
What works: This is a people leadership use case, not a people operations one. The value isn't efficiency (it doesn't take that long to book four coffees). The value is intentionality. Left to her own habits, Alice would meet with the same people she already knows. AI removes the friction of identifying who she should be connecting with and handles the calendar logistics, so the only thing left is the actual conversation.
Key takeaway: AI in HR isn't always about saving time. Sometimes it's about doing things you wouldn't have done otherwise. Intentional relationship-building across a distributed team is one of those things.
11. Building an AI candidate screening agent that learns from recruiter decisions
Who: Sarah Sheikh Jauhar, Fractional People Leader
What she built: For a Series A client with two recruiters and 400+ inbound applications backed up across 14 open roles, Sarah built an AI recruiting agent using Claude that lives inside their ATS workflow. When a new application comes in, the agent pulls the job description, scores the candidate against a structured rubric (skills match, experience level, role-specific signals), writes a summary with its reasoning, and drops the candidate into a ranked shortlist. The recruiter opens their queue in the morning and sees a prioritized list with notes instead of a pile of 400 resumes.
What works: The rubric isn't static. It iterates every week based on which candidates the team actually moves forward and which they don't. The agent gets better at matching the team's judgment over time. Six weeks of performance data: screening time dropped from 10 minutes per candidate to under 90 seconds. Each recruiter freed up roughly 12 hours per week. Time from application to first interview dropped from 11 days to 3. Qualified candidates reaching phone screen increased 40%.
But Sarah flagged the human impact as the most important result: "The thing that changed most wasn't just the speed. It was that the recruiters stopped doing the part of their job they hated, and started spending more time on the part that actually matters. Conversations with applicants."
Key takeaway: AI candidate screening works best when it learns from recruiter decisions rather than replacing them. The recruiter still decides who moves forward. The AI compresses the time it takes to get to that decision. If you want this capability without building from scratch, tools like Truffle offer multi-signal candidate screening (video interviews, assessments, and AI match scoring) with explainable scores your team can override, starting at $99/month.
12. Building an org design tool from scratch
Who: Alice Browne, Managing Partner of People at Brainlabs
What she built: Alice has always struggled to find an affordable tool for modeling the impact of reporting line changes. The usual approach: slides and spreadsheets, all painfully manual. So she used Claude Cowork to build a drag-and-drop org design tool that runs in the browser using HTML. You can model different versions of an org structure, save each one, and compare options side by side. She went into an org planning session with an interactive tool instead of a slide deck.
What works: This is the most advanced example on this list, and it illustrates something important about where AI in HR is heading. Alice didn't install software. She didn't submit a request to engineering. She described what she needed in plain language, iterated with AI on the design, and had a working tool in her browser. For people operations leaders who have always been constrained by whatever their IT team has time to build, this is a fundamental shift.
Key takeaway: If a tool you need doesn't exist, or costs more than your team can justify, AI can build a functional version. It won't be enterprise software, but it might be exactly enough for the decision you need to make this afternoon.
What actually makes AI work in HR
Across all 12 examples, a few patterns kept showing up.
- The best use cases start with a specific frustration, not a technology. Nobody on this list said "I want to use AI" and then went looking for a problem. Melissa was annoyed about redacting PDFs. Emily was drowning in email. Alice couldn't find an org design tool she could afford. The AI came second.
- Guardrails matter more than capabilities. Emily's agent can only access her calendar and only triggers on her emoji. Melissa verified every Slack ID before sending comp notifications. Sarah's screening agent produces ranked lists and summaries, but the recruiter still decides who moves forward. Every example that works well has a human checkpoint built in.
- The cognitive load savings are bigger than the time savings. Emily called this out explicitly. Drafting a single email reply takes two minutes. But the mental weight of 40 unread emails, each requiring a decision, eats your focus for the whole day. AI handles the decision scaffolding so you can spend your energy on the decisions that actually need you.
- Start simple, then layer. Melissa documented her use cases in order of complexity for a reason. She started with drafting a Slack announcement (Level 1) before building multi-tool automations across Slack, Google Sheets, and Notion (Level 4). Alice's first task was a company announcement. Her fourth was building a tool from scratch. The progression is intentional.
- Talk to legal before connecting sensitive data. Emily, Melanie, and multiple webinar attendees all flagged this. The answer to "is this safe?" is always "talk to your legal team and your IT team." Enterprise agreements with AI providers determine how your data is handled. Those agreements are company-specific. No blog post (including this one) can tell you what's safe for your organization.
The wrap on AI in HR
Emily Mabie closed the webinar with advice that I think applies to everyone reading this: "Go make yourself an expert in something, and stop thinking you're not technical. Because you are."
HR professionals are sometimes the last people to think of themselves as technically capable. But every example on this list was built by a people leader, not an engineer. Melissa runs people ops. Alice runs people strategy. Emily designs learning programs. Sarah advises startups on hiring. None of them waited for their IT team to build it for them.
The pattern across all five categories of AI in HR (resume screening, candidate communication, interview analysis, talent assessments, and workforce planning) is the same one that shows up in these 12 examples. AI handles the mechanical work. Screening, sorting, scheduling, scoring, formatting, calculating. Humans handle the decisions that require judgment, empathy, and context.
The examples that fail are the ones that try to remove the human entirely. The examples that work are the ones that give humans better information to work with and more time to act on it.
If you're still at the "I mostly use ChatGPT" stage, pick one task from this list that matches a frustration you already have. Build it. Get it working. Then build the next one.
If your biggest frustration is candidate screening, Truffle's 7-day free trial lets you test multi-signal screening on a live role in under an hour. No credit card, no implementation project.
FAQs about AI in HR
What are the most common uses of AI in HR right now?
The five most common applications are resume screening, candidate communication (chatbots for FAQs and scheduling), interview analysis (transcription, scoring, and summarization), talent assessments (personality and situational judgment tests scored against employer criteria), and workflow automation (email drafting, task management, document processing). Workforce planning is emerging but still mostly limited to enterprise.
Is AI in HR safe for handling sensitive employee data?
It depends on your setup. Enterprise agreements with AI providers (OpenAI, Anthropic, Google) define how your data is processed and stored. Always consult your legal and IT teams before connecting AI tools to systems containing employee data, compensation information, or protected health information. Many practitioners keep a human review step for any workflow involving sensitive data.
Do I need to be technical to use AI in HR?
No. Every example in this article was built by a people leader, not an engineer. Tools like Claude Cowork, Zapier, and Athena are designed for non-technical users. The core skill is the same one HR professionals already have: thinking clearly about processes, identifying bottlenecks, and communicating what you need in plain language.
What's the difference between an AI workflow and an AI agent?
A workflow runs the same way every time. When X happens, do Y, then Z. An agent can make inferences and adjust its behavior based on context. Workflows are more predictable and easier to control, making them better for sensitive processes. Agents are more flexible, making them better for tasks that require judgment calls like scheduling and prioritization.
How should I get started with AI in HR if I've only used ChatGPT?
Pick one specific, recurring frustration. Not "I want to use AI more." Something like "I waste 45 minutes every week redacting salary data from planning docs." Start there. Build one workflow to solve it. Get comfortable. Then expand. The practitioners in this article all started with simple use cases before building complex ones.
