HR teams get AI to stick by starting with one repeated, text-heavy task that has clear review criteria, such as a generic job ad, and expanding only after checking quality and time spent. Keep a person deciding on every output and put the AI step inside the existing workflow. Before testing, check that the tool and data are approved.
By Johannes Sundlo, AI & Future of Work Advisor. I help leaders and HR teams turn AI into adoption that sticks, through keynotes, workshops and change programs.
Start with an HR task you can test and review. This guide covers useful starting points, a five-step rollout and ways to assess time, quality and continued use.
Where HR teams start with AI
Choose a repeated, text-heavy task with clear review criteria. Assess the data and consequences before using AI; a routine HR task can still involve sensitive information. Drafting a generic job ad is a simpler starting point than evaluating a person.
- Drafting: job ads, interview guides, policy first drafts, onboarding emails, performance-review summaries.
- Summarising: long policy documents, survey free-text, exit-interview notes, meeting transcripts.
- Structuring: turning messy notes into competency frameworks, comparing candidates against criteria, tidying spreadsheets.
- Answering: a private assistant for “how does our parental-leave policy work?” type questions, grounded in your own documents.
A 5-step playbook to roll out AI in HR
- Pick one painful, repeated task. Choose a task someone in the team does each week, with a clear owner and review criteria.
- Write the prompt as a process, not a question. Give the AI context (your tone, your policy, an example of good output) and constraints. A reusable prompt is an asset. Save it.
- Put a human in the loop. AI drafts, a person decides. Make that rule explicit so people trust the output and own the result.
- Make it the default, not an extra. Adoption happens when the AI step lives inside the existing workflow (the template, the checklist, the shared doc), not in a separate tool people forget to open.
- Capture the win and tell the story. Record the time and quality before and after the trial, including human review. Share what improved and what still needs work.
High-value AI use cases in HR
- Recruiting: first-draft job ads and interview guides checked against the role requirements. Keep candidate assessment and hiring decisions with accountable people.
- Onboarding: personalised onboarding plans, an always-on assistant that answers new-hire questions from your handbook.
- People analytics: grouping suitably anonymised survey comments into draft themes, then checking them against the source material.
- L&D: generating role-specific learning paths, drafting workshop content, summarising what “good” looks like for a competency.
- Capturing hidden knowledge: structured interviews with senior staff, turned into reusable documentation before that expertise walks out the door.
The mistakes that kill HR AI projects
- Trying to do everything at once. A 12-month “AI transformation” with no win in the first month loses the room. Ship something small and real first.
- No data guardrails. Decide early what can and can’t go into which tool. Sensitive employee data needs a clear, simple rule everyone understands.
- Tool-first thinking. Buying a platform before a single workflow works in practice. Get one workflow right first, then choose the tool that fits it.
- Treating it as IT’s job. HR and IT need to work together on data, tools, skills and changes to daily work.
How to know it’s working
Licence counts tell you little. I track three things: time saved on the target task, how many people use it without being told to, and quality (does the human editing get lighter over time?). When usage spreads on its own and edits shrink, you’ve got real adoption, not a pilot that quietly died.
Work on this with your HR team
In my workshops, HR teams practise with selected tasks, save useful instructions and decide how to review the output. A keynote can introduce the choices before the team starts testing.
Bring practical AI to your HR team
Keynotes and workshops for HR teams that want to go from curiosity to everyday use. Tell me what your team is working on and we can find a format that fits.
Want to talk it through first? Get in touch and tell me where your team is today.
Common questions about AI in HR
How do I start using AI in HR?
Start with one repeated task and clear review criteria, such as a generic job ad or a summary of an approved policy. Check whether the tool and data are permitted, test the result and save a useful prompt. Expand only after reviewing quality and time spent.
Is it safe to use AI with employee data?
Use only tools and data approved for the task by your organisation. An enterprise agreement or a promise not to train on inputs is not, by itself, enough to establish that employee data can be used safely or lawfully. Check access, retention, confidentiality and the purpose with your privacy, legal and IT teams before uploading personal data. Use fictional examples for early practice. In my 2025 HR survey, 44% of respondents to the policy question reported no AI policy; this does not establish the status of every Swedish employer. See the State of AI in HR data.
Which HR tasks should I automate first?
Consider drafts of generic job ads, interview guides, onboarding messages and summaries of approved policy documents. Agree what information can be used and how a person will check the output. Do not assume that survey comments or interview notes are low risk simply because they are text.
These studies cover different groups and dates. Gallup reported in June 2025 that 40% of U.S. employees used AI in their role at least a few times a year. Separately, Randstad reported in September 2023 that 13% of more than 7,000 respondents in an international study had been offered AI training in the previous year. The percentages are not a matched comparison of access and training.
How do I get my HR team to adopt AI?
Choose a useful task, give people time to practise and put review into the workflow. Follow time, quality and continued use. In my 2025 survey, 25% reported AI-agent use and 33% formal AI training. Technical skills and time were the most frequently selected barriers, each at 41%. These are different measures and do not prove that training alone resolves the barriers. My recommendation is to test a few tasks with the team and adjust the support based on what happens. See the State of AI in HR data.
Related guide: Leading the change from the top? Read AI for Leaders: a practical guide to driving adoption.
Keep reading: AI adoption that sticks · AI in recruiting · ChatGPT prompts for HR · running an AI pilot.
Where should HR start with AI?
| Task type | Examples | Why start here |
|---|---|---|
| Drafting | Job ads, policy drafts, onboarding emails | Repeated work with explicit review |
| Summarising | Survey free-text, long policies, notes | Text-heavy work; measure any time saving |
| Answering | Policy Q&A grounded in your own docs | Daily and repetitive |
| Structuring | Notes → scorecards, competency frameworks | Clear input, human decides |
The data: see State of AI in HR: Sweden 2024–2025, with results on tools, training and AI policies.
See also the free AI course for HR and the comparison of AI courses for HR professionals.
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