Human Resources.
25 real jobs AI can take on for a human resources team - most of them inside the licence you already pay for. The colour on each card is the kind of AI; the line at its foot is what it takes to get it.
THE COLOUR IS THE KIND OF AI — AUTONOMY RISES LEFT TO RIGHT
The line at the foot of each card is what it takes to get it - a free chatbot, the licence you already pay for, or a build.
If these words are new, the homepage walks the whole spectrum →
Talent management.
Empower employees and people managers with a digital assistant to explore career opportunities, access skill-building resources, and manage development goals. See how Adecco Group is using AI for carer development here.
Relocating and promoted staff get instant answers on policy, pay and team, so transitions feel supported rather than chaotic.
Try it in your own AI
- Context: I'm moving with [company] from [location] to [location] on [date] as part of [a promotion/relocation], and I'm trying to sort the practical questions before the move eats my first month.
Objective: Using the policy below, tell me what I'm entitled to - temporary housing, moving costs, settling-in support - what I arrange myself, and the deadlines that bite. Flag where the policy is silent so I know what to ask HR directly. [Paste or point at the relocation policy.]
Style: Entitlements as a table with deadlines, questions-for-HR as a short list.
✓ In your licence - assembled once, then it runs. We set these up →
Leaders receive the metrics and proven practices that lift team performance, and can track whether the changes actually land.
Try it in your own AI
- Context: I'm in HR at [company]. The management data below shows real gaps between our strongest and weakest teams, and I want managers to get something they can act on rather than a scorecard that makes them defensive.
Objective: For each manager, draft a short report: what the metrics say, the one or two practices from our strongest teams most likely to close their gap, and how we'd both know it worked in a quarter. [Paste the metrics and any notes on what strong teams do.]
Style: One page per manager - metrics summarised, practices with the reason they fit, a check-in measure.
Tone: A coach, not an inspector.
✓ In your licence - assembled once, then it runs. We set these up →
Every internal move comes with ready-made screening, scheduling, onboarding and review content, cutting weeks of admin to hours.
✓ In your licence - assembled once, then it runs. We set these up →
Learning & development.
Generic training programs may lead to low engagement and misalignment with company goals across regions and functions.
New starters get a clear, personalised onboarding plan built from the best resources across the business, reaching productivity faster.
Try it in your own AI
- Context: I'm bringing a new [role] into [team] at [company] on [date]. Our onboarding is a folder of documents and good intentions; the cost is the months before they're properly useful.
Objective: From the material below, build a first-90-days plan: what they learn each week, who they should meet and why, and how we'd both know at day 30, 60 and 90 that it's working. [Paste the role description and available resources.]
Style: Week-by-week table, milestones flagged.
Audience: Written to the new starter, not about them.
✓ In the licence you already pay for
An agent guides each new hire through tailored steps, answers questions live and tracks progress, so day one runs itself.
✓ In your licence - assembled once, then it runs. We set these up →
Training is matched to each role and skill gap, enrolled automatically and tracked, so development happens without manual chasing.
✓ In your licence - assembled once, then it runs. We set these up →
A reusable onboarding agent pulls the right policies and resources together, giving every new hire a consistent, guided start.
Try it in your own AI
- Context: Every new hire at [company] gets a slightly different start depending on who's busy that week. I want the assembling automated - one consistent, guided first month built from our own policies and resources.
Objective: Design the onboarding assistant: what it should collect for each role, the questions new starters really ask (draft the answers from the policies below), and the checklist it walks each hire through. [Paste the policies and role types.]
Style: A spec in sections - inputs, Q&A bank, checklist - so we can build straight from it.
✓ In your licence - assembled once, then it runs. We set these up →
Compensation & benefits.
Managing compensation and benefits without data-driven insights can lead to challenges to maintain fairness and competitiveness in the job market.
Policy updates ship with fresh FAQs, comms and a rollout plan, so changes are understood and adopted, not ignored.
Try it in your own AI
- Context: We're changing [policy] at [company] from [old] to [new] on [date]. Most policy changes fail quietly: announced once, misunderstood, ignored. I own making this one land.
Objective: From the two versions below, produce the difference table, the FAQ answering the questions people will really have (including the awkward ones), the announcement, and the rollout plan with dates and owners. [Paste the old and new policy.]
Style: Differences as a table; FAQ under ten questions; announcement under 250 words.
Tone: The announcement straight about what's changing and why - people forgive change, not spin.
✓ In the licence you already pay for
Pay and benefits decisions are grounded in live market data and modelled impact, keeping packages competitive and defensible.
Try it in your own AI
- Context: I own reward at [company]. Pay for [role/family] is under pressure - [signal, e.g. two counter-offers this quarter] - and any change must be defensible to the exec and the team alike.
Objective: From the market data below, show where our packages sit against market by role and level, model what moving to [target position, e.g. median] costs, and set out the case for and against moving now. [Paste your ranges and the market data.]
Style: Position as a table, the cost model with assumptions visible, the argument in two short paragraphs.
✓ In the licence you already pay for
Time, schedule and location records are cross-checked automatically, catching payroll errors and fraud before money goes out the door.
✓ In your licence - assembled once, then it runs. We set these up →
Leave requests are verified, approved and adjusted against policy with a full audit trail, removing risk and manual paperwork.
✓ In your licence - assembled once, then it runs. We set these up →
A repeatable agent gathers market pay data, models the cost and drafts the comms, so reward reviews run on rails.
✓ In your licence - assembled once, then it runs. We set these up →
A benefits chatbot answers routine staff questions instantly and flags gaps, freeing HR from repetitive enquiries.
✓ In your licence - assembled once, then it runs. We set these up →
Recruiting.
Enable hiring teams to streamline candidate search and selection with a digital assistant that surfaces ideal matches, automates screening processes, and provides insights for better decision-making. See how Motor Oil Group improved recruiting with your AI assistant here.
Job descriptions, interview questions, salary research and offer letters are drafted in minutes, speeding every stage of hiring.
Try it in your own AI
- Context: I'm hiring a [role] at [company] - [one line on the team and why the role exists]. Speed matters, but so does not sounding like every other advert for this title.
Objective: Draft the job description, five interview questions that test what the job needs rather than what the CV claims, a salary range for [locations] with sources, and the offer letter template.
Style: JD under 400 words with must-haves separated from nice-to-haves; each question with a note on what a good answer covers.
Tone: Ours - [paste a line or two of company copy you like] - not corporate boilerplate.
Works in a free chatbot
Internal and external talent is searched, scored and engaged automatically, surfacing the strongest, most likely-to-accept candidates first.
✓ In your licence - assembled once, then it runs. We set these up →
Market research and role criteria combine to build a credible candidate slate, ready to share and finalise with hiring managers.
Try it in your own AI
- Context: I'm recruiting a [role] at [company] and the hiring manager wants a credible slate by [date], not a keyword dump from a job board.
Objective: From the role criteria and market notes below, define what separates a strong candidate from a plausible one for this role, then assess the candidates provided against that bar - strengths, gaps, and the question I should ask each at screen. [Paste the criteria and candidate summaries.]
Style: One table - candidate, strengths, gaps, screen question - then a two-line steer on the slate overall.
Audience: The hiring manager - they'll skim the table and read the steer.
✓ In the licence you already pay for
Recruitment data is connected, analysed and reported so teams can spot bottlenecks and steadily improve the hiring pipeline.
Try it in your own AI
- Context: Our hiring at [company] feels slow but nobody can say where it slows. The pipeline data below covers [period].
Objective: Analyse time-to-hire and candidate satisfaction by stage, find where we lose the most time and the best people, and suggest the two changes most likely to move the numbers - with how we'd verify it next quarter. [Paste the data.]
Style: Findings as a short table, the two changes as plain recommendations with expected effect.
✓ In your licence - assembled once, then it runs. We set these up →
Day in the life.
See how people can use your AI assistant to perform common tasks throughout their day to save time, generate value, and improve their wellbeing.
See how an HR manager uses AI across a working day to move company initiatives forward faster.
Try it in your own AI
- Context: I'm an HR manager at [company]. Today holds [e.g. two employee meetings, a policy question and an initiative update] and the preparation time doesn't exist.
Objective: Take the three items below and prep each one: the background summarised, what good looks like coming out of it, and the questions I should ask. [Paste the notes for each item.]
Style: One short brief per item - background, outcome, questions.
✓ In the licence you already pay for
Follow an HR manager weaving AI through daily tasks to better support and accelerate organisational priorities.
Try it in your own AI
- Context: I'm in HR at [company] supporting [initiative, e.g. a restructure or values rollout]. My job this week is keeping it moving - updates, nudges, materials.
Objective: From the project notes below, draft this week's package: the status update for sponsors, the nudge to the workstream that's slipping, and the FAQ addition for the questions that came up. [Paste the notes.]
Style: Three short drafts, each ready to send.
Tone: The nudge firm and kind - name the slip, offer the help.
✓ In the licence you already pay for
Watch how an HR service manager uses AI to resolve employee queries and deliver better support each day.
Try it in your own AI
- Context: I manage HR services at [company]. The query queue below is today's mix - policy questions, case updates, the odd grievance signal hiding in polite wording.
Objective: Triage it: group by type, flag anything that smells like a risk case needing a human first, and draft replies for the routine ones from the policy below. [Paste the queue and the policy.]
Style: Triage as a table, replies underneath, risk flags at the top.
Tone: Replies warm and precise - policy quoted, not paraphrased.
✓ In the licence you already pay for
See how a senior HR service leader applies AI to handle complex cases and support employees more effectively.
Try it in your own AI
- Context: I handle escalated HR cases at [company]. The case below has history, strong feelings and an audit trail that will be read later.
Objective: Summarise the case neutrally, map what our policy and precedent say against what's been done so far, list the options with their risks, and draft the next communication. [Paste the case file and the policy.]
Style: Four sections; the options honest about trade-offs.
Tone: The communication careful and human - it will be reread many times.
✓ In the licence you already pay for
Employee engagement.
Provide your employees and partners with a self-service conversation chatbot or digital assistant to resolve issues, complete HR tasks and access relevant knowledge articles, insights, or tools they need in their line of work.
Cases are summarised, researched and worked through collaboratively, then documented, so employee issues get resolved and recorded properly.
Try it in your own AI
- Context: I'm handling an employee case at [company] - [de-identified one-liner]. It needs resolving properly and documenting so the next person doesn't reinvent it.
Objective: From the case notes below, summarise the issue and the positions, check what our policy says against what we did, draft the response to the employee, and write the case note for the file. [Paste the notes and the relevant policy.]
Style: Four short sections - summary, policy check, response, file note.
Tone: The response serious and human - the person on the end of this is a colleague, not a case number.
✓ In the licence you already pay for
Staff find policy answers themselves while the system learns from their questions to keep guidance accurate and up to date.
✓ In your licence - assembled once, then it runs. We set these up →
Nominations are drafted, routed and logged automatically, building a culture where good work is consistently recognised.
✓ In your licence - assembled once, then it runs. We set these up →
Engagement data drives a clear action plan and stakeholder buy-in, building a more connected, motivated and aligned workforce.
Try it in your own AI
- Context: Engagement results are in at [company] and [area] has moved [how]. Reports get filed; I want this one to cause something.
Objective: From the data below, identify the three findings that matter most, build an action plan with owners and timelines, and draft the note that gets leadership to back it. [Paste the engagement data.]
Style: Findings with their evidence, plan as a table, leadership note under a page.
Audience: The note is for execs - lead with the business cost of doing nothing.
✓ In the licence you already pay for
Most of these run on the licence you already pay for.
The gap is that nobody's pointed it at the job yet. That's the work we do, and it starts with a half-hour call - bring the one that looked most like your week and we'll tell you whether it's a quick win or a proper build. No licence yet? We'll help you pick one first.
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