HR already has more AI ideas than it can use. SHRM recently mapped 138 AI use cases across HR.1 The problem then becomes: what do you actually build in real life? Not just discuss or brainstorm, but build and use.
My go-to for this is the classic Impact–Effort Matrix.
Impact is how much better the work or outcome becomes. For this exercise, I would look at impact across the HR team or organization, not just whether something saves one person ten minutes. Effort includes the time, cost, data, integrations, permissions and maintenance needed to make it work.
That gives you four common places to put an idea: Quick Wins, Big Bets, Fill-Ins and Money Pits.
Before anything goes on the matrix, I would make one HR-specific check: What happens if AI gets it wrong? A weak email draft is easy to fix. A bad AI-assisted employment decision can have much bigger consequences.
The matrix forces an important distinction. A morning brief can be personally useful and easy to build, but most of the impact stays with one person. An HR policy assistant could help an entire workforce, but now you need approved information, permissions, testing and someone to own it.
U.S. presidents have received a tailored daily intelligence brief for decades.2 Now you can have your own less-classified version. Connect an approved AI tool to your calendar and email, and you are ready for your own presidential start to the day.
The Quick Wins are especially interesting because AI has changed what “low effort” means.
I recently built an interactive ROI calculator for Wellhana. I use it in conversations with HR leaders to discuss the value of employee financial wellbeing. We change the assumptions together and make the financial case visible.
A few years ago, I might have spent thousands having something like that designed and developed. With AI, I built it myself from start to finish.
That changes what is worth testing.
HR could build a recruitment ROI calculator before asking for more recruiting budget. Or take workforce data and source material and build a leadership briefing around a problem that needs funding.
The Big Bets may eventually create even more value, but they deserve more work before you start. Clean data, system access, integrations, permissions, testing and ongoing ownership can turn an exciting idea into a real project.
That is how I would choose what to build:
Start with impact and effort. Then take a clear-eyed look at what happens if AI gets it wrong.
Over the next few issues, I am going to pick several of these and actually build them from scratch. Then we can see what deserves to stay.
Best,
Henry Kaerki
Founder, Wellhana