The Extra Time Challenge

The HRBP Lab · Issue 001 · April 25, 2026

Hello from the Lab.

Welcome — and an especially warm one to everyone who just subscribed. I’m glad you’re here.

You’re growth-minded, curious, and probably a builder at heart. I learn in public and share what I find, so please send me what’s on your mind — a question, a snag you’re hitting, a spark from your week. Anything we can explore together.

I’m using #buildtogether to tag this kind of work. Tag yours too if you want to join the thread.

I’ve been thinking about a question that came up twice in the last month on the Beyond the Prompt podcast: what do people actually do with the time AI gives them back?

It sounds like it should have an obvious answer. You automate something tedious, you free up a few hours, you use those hours for higher-value work. That’s the whole pitch.

Except it’s not always what happens.

Greg Shove, CEO of Section, someone who’s watched a lot of enterprise AI rollouts up close, calls it “leaking.” The time savings are real. But they don’t flow up to the organization. They get absorbed by the individual: work a bit faster, leave a bit earlier, fill the space with whatever was waiting. The ROI is there. You just can’t find it on a spreadsheet.

Melissa Cheals, CEO of Smartly, did something different. She asked her team what they won’t do anymore. Not “what will we do with the extra time” — the version everyone asks, vaguely. But: what work are we ruling out? That’s the decision that makes the time real.

The difference between those two outcomes isn’t the AI. It’s the intention before the automation.

Why this matters if you’re an HRBP

We sit in the middle of this whether we like it or not.

We’re the ones partnering with leaders on workforce plans. We’re the ones sitting in headcount conversations where someone says “AI will make this team X% more productive.” We’re the ones who have to translate productivity claims into actual org decisions.

And right now, a lot of those claims are fiction.

Not because AI doesn’t work. It does. But because the gains leak at the individual level and never show up at the team or org level. If you’ve ever sat in a planning meeting where someone modeled out 20% capacity gains from AI and you thought “where exactly is that going to show up?” — that’s the leak.

Five questions to ask before any automation initiative

None of these are clever. They’re obvious. But nobody asks them, because the momentum around AI right now is “just start, figure the rest out later.”

1. What specifically do we want people to do with the time this saves?
2. How will we know the time is going there and not somewhere else?
3. Are we reinvesting this capacity, or are we planning to reduce it? (Be honest.)
4. If we’re reducing it, what’s our threshold before we lose institutional knowledge we actually need?
5. Who owns the answer to these questions six months from now?

Skipping these is how you end up with a spreadsheet that shows 2,000 hours of capacity gains and an org that feels exactly as stretched as it did last year.

My own version of this

I’ve been testing this on my own work. When I built an AI-assisted workflow for spans-and-layers modeling last quarter, it cut a week of prep down to about a day. Some of that time went into harder, higher-value problems. A lot of it went into the rest of my list — the table-stakes work that still has to get done.

Which got me thinking: how do I optimize this better?

I’m building a framework for exactly that. More on it soon.

The question

Before you automate the next thing, ask your leadership team: what’s the plan for the time? Not “what will people do” in the vague sense. What’s the specific work you want to happen in those hours? If you can’t answer that, the ROI isn’t going where you think it is.

And if you’re an HRBP, this is your question to bring into the room. Nobody else will.

What does “reinvesting the time” look like on your team right now? Hit reply — I read everything that comes in.

— Josh

The prompt I’m using this week

Give this to Claude (or your tool of choice) the next time you start — or return to — a project you’ll keep coming back to:

Help me build an instruction file for [project name]. Ask me questions one at a time — about the goal, the context, the key people, what’s already been decided, and how I like to work on this — until you have enough to draft a complete markdown file. The output should be something I can save, refer back to, update over time, and drop into any future chat so I never have to re-explain the whole project from scratch.

Why it works: The time you save isn’t in the one prompt — it’s in the next twenty. Every time you come back to the project, the AI already knows what you’re building, who’s involved, and how you think about it. You stop starting from scratch.

I’ve started doing this for every ongoing project and every “super-task” with lots of moving parts underneath it. One of the highest-leverage adds to my workflow this quarter.

If you try it, tag it #buildtogether and show me what you built — I’ll feature the best ones in a future issue.

If you want to go to the source:

• Why Most Companies Are Not Seeing ROI on AI Yet — Greg Shove, CEO of Section
• AI-Native or Not: The Defining Choice for Companies Right Now — Melissa Cheals, CEO of Smartly

Both episodes of Beyond the Prompt.

If someone you know is navigating the same question, forward this their way. New here? Subscribe at thehrbplab.ai to get the next one.