HR already has more AI ideas than it can use. SHRM recently mapped 138 AI use cases across HR.1 So the real question is what to actually build. Not what to brainstorm or ideate, but what to build and put into use.
To make that decision, my go-to is the classic Impact–Effort Matrix.
Impact is the value an idea could create. Effort is what it takes to make it work: time, cost and complexity.
Plot each idea in one of four quadrants: Quick Wins, Major Projects, Fill-Ins and Thankless Tasks.
For HR, I would add one check before you start: What happens if AI gets it wrong?
This is especially important in HR because the work touches people’s jobs, pay, benefits and legal rights. A weak email draft is easy to fix. An AI-assisted employment decision that ends in a discrimination complaint or lawsuit is not.
Early on, I would start with Quick Wins and a few low-risk Fill-Ins. They give you reps and help the organization learn what actually works before you take on larger projects.
A morning brief is a good example. U.S. presidents have long started the day with a tailored intelligence brief. Now you can have your own much less classified version. Connect an approved AI tool to your calendar and email, and you can give yourself a slightly presidential start to the morning.
The Quick Wins are where AI will provide the most leverage, fastest.
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 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.
So AI changes what is worth testing. If a useful first version takes an hour instead of a development project, many more ideas become worth trying.
HR can 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.
Once the smaller builds are working, move up the matrix. A natural-language HR analytics tool could create much more value, but it also requires clean data, permissions, system access and more careful testing.
That is how I would choose what to build:
Start with impact and effort. Then build the highest-leverage ideas you can test safely with the lowest effort.
Best,
Henry Kaerki
Founder, Wellhana