On my first week at Leapsome I told the company I would be doing the same, but trying to reach as many of our 150 folks across the first four weeks as possible.

There is a version of this story where I tell you I spent my first week at Leapsome carefully sending bespoke meeting invitations to every single person on the team, researching their calendars, considering their time zones, and handwriting agendas.

That version is fiction.

The real version involves Leapsome as a platform, Gemini, a .ics file, and about forty-five minutes of moving calendar blocks around like a particularly detailed game of Tetris.

And it works brilliantly.

When you are joining a new company as a People leader: everyone expects you to listen, but nobody builds you the infrastructure to actually do it easily. You’re dropped into a Google Calendar full of strangers’ names, access to a HRIS, engagement surveys, and given a laptop to kick off your user research. The only way to form a real view (not the curated town-hall version, but the actual texture of what’s happening) is to talk to people. A lot of people. Individually.

So I book 1:1s with everyone. Not just my team. Everyone. Once a year, roughly 30 minutes, no agenda, no prep required. It sounds like a logistical nightmare. And it kind of is, briefly, and then it isn’t.

The Setup: Don’t Do This Manually

The first mistake most People leaders make is trying to schedule these one by one. You open your calendar, search for someone’s name, check their availability, draft an invite, send it, then move on to the next person. Multiply that by however many people are in your organisation and you’ve just spent two days doing something a moderately capable spreadsheet could do for you.

Here’s what actually works:

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The classic path would be to buy an employee engagement platform. Research vendors. Sit through demos. Build a business case. Wait for budget approval. Maybe launch something in Q3 (if you’re lucky).

Instead, Andrew built it himself. In two weeks. Using Replit, a tool that’s costs are small, but they’re diminished even more by the minimal time investment required to get up and running.

Andrew isn’t a technical leader who accidentally ended up in HR. He studied psychology at university. His first job out of college was selling promotional pens door-to-door in San Francisco. He is not a developer. He’s just someone who got tired of waiting for perfect solutions that didn’t seem to come.

But what really makes Andrew impressive to me is that he stopped asking for permission and started building things.

And that’s really the entire point of this piece I’m writing about today, and it’s kind of the point of almost everything I advocate for in Built for People. So let’s dig in…

The Problem with “Strategic HR”

I’ve spent the last decade obsessing over making HR more strategic. Learn the business model. Understand the P&L. Speak the language of the C-suite. Get that coveted seat at the table.

All good advice. All completely necessary. And yet, entirely insufficient on its own. The word “built” is in “built for people” on purpose. You can know every line item in the budget, attend every board meeting, speak fluent finance, and still be seen as fundamentally administrative if the only thing you do when problems emerge is... schedule meetings about them, or purchase more tools to be implemented.

It’s a troubling cycle that seems to have emerged in the last decade or so where a leader identifies a real problem (and, if I’m frank, this is a HR problem as much as it is a Finance one, Customer Success, or Marketing)... the problem statement is well written, it’s something that’s genuinely costing the business money or making employees miserable. They present it effectively to leadership. Everyone nods. Everyone agrees it’s important and must be solved. Then what happens?

Six months of vendor demos. An RFP process using those god awful procurement spreadsheets (I’m sorry but please). A committee is ‘spun up’ to evaluate solutions. A budget request that gets pushed to next quarter, “once revenue picks up”. Meanwhile, the problem is still there, still costing money, still making people miserable.

Often this is what passes for strategic HR work.

I often get feedback that I’m still very ‘hands on’, because I spend time using Zapier, Sana, or Claude. It’s because I think that strategy also involves knowing how to (not always necessarily being the individual to do the full programme of work), but knowing how to direct a team to build the thing that solves the problem. This month. This week. Today, if you can!

As Andrew puts it: “Stop trying to be a consultant and go get in the business and sit side by side and build things and fix things with people.”

That’s the work. Not the decks. Not the vendor evaluations. Not the perfectly formatted business cases that live in shared drives nobody opens. The actual building of actual solutions to actual problems.

How We Got Here (And Why It’s Changing)

For years, HR teams have been stuck in a specific pattern when it comes to solving problems:

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It’s been a long five years, y’all. We’ve seen a lot, we’ve done a lot. To say, “so much has changed” feels trite and wholly inadequate. To put it lightly, the last half-decade of work has been… eventful. Things weren’t always this way, surely? In my first role at BHP I remember we rolled out SAP over the course of what I am almost sure was 18 months to two years, a process driven by a winding map of systems training and stakeholder management. The idea of taking that kind of time now is baffling; a view I have, no doubt, at least partially adopted due to my move out of enterprise corporate business, and into the foray of the tech/scale-up world.

Regardless of the size of your business, constant change isn’t just the reality of modern work, it’s become the defining feature of leading people over the last five years; the pandemic, working from home, returning to office, rising interest rates, a euro-usd forex parity, layoffs, the AI boom… “unprecedented times” abound. And if you’re a People leader trying to keep your team grounded whilst everything around them shifts like a game of Jumanji, you’re probably feeling a bit, well, tired.

The Problem with Change Management Theatre

Traditional change management was designed for a world where companies announced one big transformation every few years rather than every few weeks, where everyone got a branded swag bag to commemorate the occasion, and our systems and tools remained in place, relatively uninterrupted, for years at a time.

That world is dead and buried. Gone the way of fax machines, landlines, and “reply all” email chains about the coffee machine being down. Now we’re looking at a future with:

• Strategic pivots every quarter (sometimes every month),

• Technology that changes faster than we can metabolise the launch notes,

• Market conditions shifting like British weather (and, frequently, as bleak), and

• Teams spread across time zones who’ve never met in person.

The community listening to Modern People Leader have expressed a desire to consider new methods around how to approach change, without practices rooted in 2019 (or earlier).

What Your Brain Actually Does During Change

In 2018 I read a book that I ended up bandying around to quite a few other folks on the leadership team at McCann Worldgroup, a company going through a significant digital and agile transformation in the wake of social media. Hillary Scarlett’s brilliant work in “Neuroscience for Organizational Change” shows us that when faced with ambiguity, our brains don’t just feel uncomfortable—they actually consume more glucose, making us mentally exhausted before we’ve even started adapting. Your brain is literally wired to resist uncertainty.

Every time you announce a “strategic realignment” or “organisational restructure,” you’re essentially asking people’s brains to work overtime whilst simultaneously expecting them to be creative, collaborative, and enthusiastic about the future. It’s like asking someone to run a marathon whilst wine-tasting…technically possible, but probably not their best performance.

Today, the traditional approaches are proving themselves as fundamentally flawed. Kotter’s 8-Step Process, with its emphasis on creating urgency - woo, just what we need - and communicating the vision first, assumes people are operating from a rational, executive-function mindset when actually, their threat-detection systems are in overdrive.

Applying Maslow’s hierarchy to change mirrors what neuroscience tells us about how our brains prioritise information. We literally cannot focus on the bigger picture and self-actualisation when our threat-detection systems are activated. It’s not a character flaw or lack of ambition, it’s basic human wiring.

Level 1: Physiological Needs - “Am I Going to Be Okay?”

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You've been using AI for your People Ops work, haven't you? Right? Everyone has! If you take a single look at LinkedIn you’ll find a whole array of self-appointed AI-People-Experts sharing their latest wisdom on using LLMs in our work, so you know that the momentum is out there. Maybe it's helping you write that tricky performance review feedback like Thomas at Juro, or perhaps you've been using Sana to help structure your onboarding documentation or User Interviews as we shared a few weeks ago. And every time you do it, there's this little voice in the back of your head whispering, "Is this okay? Am I cheating?”

You're not. But we need to talk about it.

The reality is that AI has fundamentally changed the game for People teams. You may remember when I mentioned in my original "People Ops as a Product" post that HR teams traditionally haven't been particularly well-resourced in analysis and data? Well, as I discussed with Daniel and Stephen on the Metrics Episode of MPL Build, AI has basically handed us a superpower. We can now do complex analysis, generate insights, and create documentation at speeds that would have required entire analytics teams just a few years ago.

But with great power comes great responsibility, and right now, many People teams are flying blind with limited guardrails around AI use, and I’ve heard from multiple people leaders that they’re still trying to work out how to put appropriate guidance in for their team to wield this new power.

The Wild West of AI in HR

I've been watching People teams across the spectrum, from scrappy startups to massive enterprises, and the approaches to AI are all over the map. Some teams are pretending it doesn't exist (good luck with that). Others are going full robot overlord and trying to automate everything in a quest to reduce spend, maximise output, and test the limits of what we can achieve within today’s toolkit. Most are somewhere in the messy middle; using AI tools quietly, inconsistently, and without any real framework for what's appropriate.

This isn't sustainable. And frankly, it's not fair to your team members who are left guessing about what's okay and what isn't.

Why You Need an AI Fair Use Policy

An AI fair use policy isn't just about legal implications or making it clear to your compliance team you've got your act together (though those are nice side effects, I assure you). It's about creating clarity and confidence for your team so they can focus on doing brilliant work instead of worrying whether they're breaking some unspoken rule.

When you give someone a new tool without any instructions, they either don't use it at all (missed opportunity) or they use it wrong (potential burning fires). Neither option serves anyone well.

A proper AI fair use policy does three critical things:

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You've done the work. You've talked to employees, run the user research, mapped the pain points, and written beautiful problem statements that would make any product manager giddy with joy. You've embraced the whole "employees as users" part of the work, and honestly? You're feeling pretty dang good about it.

Then someone—potentially your CEO, maybe your management team—asks the question that makes your stomach drop: "So what are we actually going to build first?"

And suddenly you're staring at a Notion page full of problems spanning across "onboarding process" to "the expense system" to "we desperately need better manager training” but also “we haven’t got a consistent offsite calendar.”

The Paralysis of Infinite Problems

Here's a problem with applying product thinking to People Ops: identifying problems is the easy part. I mean, talk to any colleague for fifteen minutes and they'll give you enough material to keep your roadmap busy until 2030.

The hard part—the part that separates teams who actually ship impactful employee experiences from teams who get stuck in analysis paralysis—is figuring out what to tackle first, how to break it down into something you can actually deliver, and how to convince everyone (including yourself) that you're not just rearranging deck chairs on the Titanic.

This is where some people ops teams really struggle to put pen to paper effectively. They either try to boil the ocean by fixing everything at once, or they default to whatever screams loudest that week. Neither approach ends well.

The Question That Changes Things

One of the most common questions I get isn't "how do I find problems?" (trust me, as I said, your team can happily provide those). It's this: "I've identified the problems, I've got my team bought in on the approach—now how do we actually decide what's most important and what ships first?"

Closely followed by: "How do I break these massive, complicated people problems down into something we can actually deliver without waiting eighteen months and building the Death Star of HR solutions?"

Enter: Minimum Loveable Products for People Ops

Your product team doesn't try to build everything at once. They start with an MVP, except in People Operations we can rarely get away with minimum viable (ethics, tradition, expectations). What we need are Minimum Loveable Products: the smallest version of a solution that employees actually want to use and that solves a real problem.

Think new hire experience: instead of rebuilding your entire onboarding program from scratch (Death Star approach as mentioned previously), maybe you start with fixing the single most painful day-one experience. Or performance reviews: instead of implementing a whole new system, maybe you test a lightweight feedback or calibration tool with one team first.

The frameworks I'm going to share in this blog do a little to help you get there. I’m hoping I can help you take your beautifully identified problems and turn them into a prioritised, actionable roadmap that your team can actually execute.

The PEOPLE Framework

The PEOPLE framework is a scoring system that helps you cut through the noise and figure out what deserves your team's precious time and energy.

Here's how it works:

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Now comes the implementation challenge: building a systematic framework that can handle this complexity while remaining practical and scalable.

The Challenge of Building Systematic Frameworks

The complexity we discussed in Part I creates a real operational challenge. How do you build compensation systems that are sophisticated enough to handle global competition and multi-dimensional talent markets, while still being practical for hiring managers to use and fair for employees to understand?

The best frameworks balance five critical characteristics:

1. Systematic - Consistent logic that reduces bias and increases fairness. This doesn’t mean the entire system is identical and treats all folks the same, but it does mean that each difference, possible exception, and variation us both understood and clearly documented.

2. Scalable - Works whether you're hiring 10 people or 100 people per year, and reflects your company’s budget and hiring goals.

3. Defensible - Based on clear reasoning that stands up to internal and external scrutiny

4. Flexible - Can adapt to market changes without complete rebuilds, and allows for revisitation for strategic purposes

5. Strategic - Aligned with your business goals rather than just administrative convenience

The Multi-Layer Framework Approach

In my experience, the challenge many teams face is that traditional compensation approaches weren't designed for this level of complexity. Building something that handles today's realities requires more sophisticated thinking.

Think of your compensation framework like a well-designed system where each component serves a specific purpose and they work together to handle complexity systematically.

Layer 1: Market Intelligence Foundation

The foundation of any modern compensation framework has to be sophisticated market intelligence. But the challenge now is that "market data" isn't straightforward when your competitive set spans multiple geographies, industries, and company stages.

Building Comprehensive Market Intelligence:

You can buy both compensation data and market intelligence, but if you are wanting to build something in house, you can also do market research yourself using multiple sources and develop your own basis of compensation data. This is useful because you can be more reactive than some market tools allow, as they are retroactively adding in data in many instances (Not all! Many new tools are much more dynamic now, and I encourage you to check them out).

Segmented Analysis: Instead of looking at broad categories like "software engineers," analyze specific segments like "senior full-stack engineers at remote-first SaaS companies with 50-200 employees"

Competitive Set Definition: Identify the 10-15 companies you actually compete with for talent (often different from your business competitors)

Dynamic Tracking: Monitor how compensation in your competitive set evolves, not just static benchmarks

Navigating Data Source Complexity:

As I said, premium data sets (Radford, CandorIQ, ERI) provide comprehensive coverage and industry credibility, but can lag market movements

Real-time sources (levels.fyi, Glassdoor, AngelList) capture current market dynamics but require careful filtering and validation

Direct competitive intelligence through your hiring process gives you the most accurate picture of what you're actually competing against, but requires a lot of work to find the data and collate it in a logical way.

The key insight: treat market intelligence as an ongoing strategic function, not a once-per-year benchmarking exercise.

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While crafting performance measurement tools is largely about design thinking and user empathy, connecting those tools to actual money? That's when things get interesting. That's when your lovely theoretical framework meets the harsh reality of budgets, market forces, and the CFO's raised eyebrow.

The Problem That Keeps Me Up at Night

Due to the fact this is such a company-wide and crucial process, I became obsessed with a new user story: We want a compensation system that takes subjective performance inputs and transforms them into objective, fair, and financially sustainable salary decisions — all while maintaining trust and transparency.

This isn't just a process challenge. It's a product challenge of the highest order. You're building a system that needs to be mathematically sound enough to satisfy Finance, transparent enough to maintain employee trust, and flexible enough to handle the messy realities of human performance.

The User Research That Nobody Talks About

When we started building our compensation engine at Whereby, I did something unusual: I researched failure stories. Not the success stories everyone loves to share at conferences, but the spectacular failures. The systems that created revolt. The processes that led to mass exodus. The frameworks that looked good on paper but created chaos in practice.

What I discovered was illuminating. Most compensation systems fail not because of bad math, but because of bad user experience. Managers didn't trust black-box algorithms. Employees felt disconnected from the process. Finance teams were frustrated by unpredictable outcomes.

The pattern was clear: we needed to build something that worked for three very different user groups — employees, managers, and finance — while maintaining a single source of truth. In product terms, we needed a platform, not just a tool.

The Calibration Product: Our Quality Assurance System

Remember how I mentioned calibration in the last post? Well, here's where it transforms from a nice-to-have check into a critical system component. Think of calibration as your QA process — it's what prevents garbage in, garbage out.

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