Why I Built Guarden Labs (And What It Is Not)
Not a product. Not a promise. A lab. Because experimentation beats assumption.
Executives keep telling me some version of the same story:
“We know we need to change how we staff and run the business.”
“We’re being sold AI, dashboards, and big programs.”
“What we don’t have is a safe way to test what will actually work here.”
On one side, there’s pressure: investors, boards, and customers asking for better margins, faster execution, and clearer workforce strategy. On the other, there are real constraints: lean teams, noisy data, and people who are already carrying too much change.
I kept seeing the same gap: leaders are being pushed to decide faster about their workforce without a structured way to learn faster.
Guarden Labs is my response to that gap.
It isn’t a new platform or a “set it and forget it” tool. It’s a way to run disciplined workforce experiments inside real operations—so you can make better bets with less theater and less risk.
The Pattern I Kept Seeing
Across PE-backed and mid-market companies, the story repeated itself.
1. Big questions, fuzzy evidence
Questions like:
“Can we redesign this function to cut overtime without breaking service?”
“Where should AI or automation actually touch our workforce?”
“How much change can this part of the organization really absorb in the next 12–18 months?”
Smart questions—answered mostly with opinions, not structured tests.
2. Lots of dashboards, little clarity
HR and workforce analytics have matured. Most teams now have:
Reports
Dashboards
Maybe an AI overlay or two
But the hard part wasn’t generating charts. It was translating all of that into clear, testable decisions.
3. High stakes, low tolerance for trial-and-error
Leaders know they should experiment, but they also know failed initiatives carry real costs—financial, political, and cultural. So ideas either:
Get rolled out too fast and too big, or
Sit on slides because no one wants to “take the risk”
4. Complexity outpacing traditional planning
Business plans are still built as if the world moves in straight lines.
Reality doesn’t.
What I saw, over and over, was this:
Leaders didn’t need more frameworks. They needed a contained way to try things and learn quickly—without betting the company.
That’s why I created Guarden Labs.
What Guarden Labs Is
Guarden Labs is a structured experimentation environment for workforce decisions.
It’s how I help leadership teams move from:
“We think this might work…”
to
“We ran the experiment. Here’s what actually happened—and what we’ll do next.”
In practice, a Guarden Lab is:
A clear question
“If we change this staffing model, can we reduce overtime by 15% without hurting quality?”
A defined hypothesis
“We believe that shifting X% of work to a different pattern / role / vendor will reduce rework and shorten cycle time.”
A limited test environment
One function, region, product line, or site—so you’re not gambling the whole business.A small, meaningful set of measures
A blend of operational, financial, and people metrics—not 40 KPIs no one remembers.A time-bound run
Long enough to see signal; short enough to adjust (usually weeks, not years).A sensemaking session at the end
Not “did we hit a magic number?” but “what did we learn, and how does it change what we do next?”
It’s deliberately simple—and deliberately rigorous.
What Guarden Labs Is Not
Just as important as what it is: what it is not.
1. Not a product
There is no “Log in to Guarden Labs” button.
I might tap into your existing systems, models, or tools—but the lab itself is:
A way of working, not a software SKU
A series of questions, experiments, and decisions, not a platform rollout
Designed to sit on top of your current tech stack, not replace it
If you already have HRIS, BI, and analytics tools, a lab helps you use them intelligently instead of buying something new and shiny.
2. Not a promise
I’m careful not to make guarantees.
The point of a lab is to find out what is true in your context, not to confirm a solution I wanted to sell you anyway.
So I’m explicit up front:
I don’t promise that a particular model, tool, org design, or AI approach will “work.”
I do commit to a disciplined test, clear measures, and an honest readout.
Sometimes the most valuable outcome is: “This idea doesn’t hold up here. Let’s not scale it.”
That’s still a win if you learn it before a company-wide rollout.
3. Not an AI black box
Some labs involve analytics, automation, or AI. Many don’t.
What Guarden Labs is not:
An auto-pilot for people decisions
A secret algorithm that scores your workforce
A way to outsource judgment to a model
I treat AI and analytics as instruments, not decision-makers—ways to surface patterns, not verdicts. The judgment stays with you and your leadership team.
4. Not theater
A lot of “pilots” are really theater—everyone expects them to be declared a success no matter what.
Guarden Labs are designed specifically to avoid that:
Small enough that you can afford to be honest
Visible enough that the learning matters
Structured enough that we don’t lose the thread in day-to-day noise
If the evidence cuts against the preferred idea, the lab still did its job.
How a Guarden Lab Actually Runs
Every lab is tailored, but the backbone is consistent.
Step 1: Frame the question
We start with the decision, not the data.
Examples:
“Should we change this staffing model?”
“Where should we focus AI or automation first?”
“Which part of the workforce is most fragile in this integration?”
We sharpen the question until it’s answerable in a quarter.
Step 2: Build the evidence base
Next, I bring together:
Your internal data (workforce, operational, financial)
Insight from the people closest to the work (leaders, managers, frontline)
Relevant external research or benchmarks
The output is a clear hypothesis and a minimal set of metrics that will tell us if we’re directionally right or wrong.
Step 3: Design the experiment
We answer very practical questions:
Where exactly will we run it?
What changes—and what stays constant?
How long do we run before we evaluate?
What do we need from HR, finance, and operations to make this real?
I deliberately bias toward simple designs that can run inside normal operations.
Step 4: Run and sense-make
As the lab runs, we:
Track the agreed metrics
Listen to what people are seeing and feeling
Watch for unintended consequences
The aim is to build and refine a shared “map” of what’s happening—not just to push numbers around.
Step 5: Decide, scale, or shelve
At the end, we answer three questions together:
What did we learn—about the workforce, the work, and our assumptions?
What should we change—in the design, the experiment, or the underlying idea?
What, if anything, should we scale—and with what guardrails?
Sometimes the answer is “yes, scale this.”
Sometimes it’s “yes, but with changes.”
Sometimes it’s “no, this isn’t worth scaling at all.”
All three outcomes are useful—because all three replace assumption with evidence.
Why This Matters Now
Guarden Labs exists because of a simple reality:
Complexity is rising. Workforce, tech, and operating models are changing faster than the old planning cycles.
Expectations are higher. Boards, investors, and employees expect decisions to be grounded in something more than gut feeling.
Big, one-shot bets are expensive. In workforce strategy, the cost of being wrong is measured in money, time, trust, and churn.
You can respond to that by trying to predict more.
Or you can respond by learning faster, in smaller, safer ways.
That’s what the labs are for.
How Guarden Labs Fits Into BloomGuarden
For me, BloomGuarden has always been about two things:
Hands-on execution – payroll, HR ops, integration, workforce design, the unglamorous work that actually makes a strategy real.
Better decision infrastructure – analytics, BloomBots, and eventually workforce simulation that helps leaders see around corners.
Guarden Labs sits right between those:
Close enough to day-to-day operations that results matter
Structured enough to build reusable patterns and insight
Practical enough that operators don’t feel like they’re part of a science project
It’s how I help you bridge from today’s consulting and project work to tomorrow’s more advanced workforce intelligence—without pretending we can skip the messy middle.
Final Thought
I didn’t create Guarden Labs because the world needed another framework.
I created it because leaders kept asking a very reasonable question:
“Can we test this in a way that doesn’t waste time, burn people out, or bet the company?”
Guarden Labs is my answer to that:
Not a product.
Not a promise.
A lab.
If you want to replace a few big assumptions with real evidence—and do it in a way that respects the reality of your business—try a Guarden Lab or email contact@bloomguarden.com and we can talk through what that would look like for your organization.
References:
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Edmondson, A. (2018). The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth. Wiley.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Mintzberg, H. (1994). The rise and fall of strategic planning. Free Press.
Pfeffer, J., & Sutton, R. I. (2006). Evidence-based management. Harvard Business Review, 84(1), 62–74.
Rousseau, D. M. (2006). Is there such a thing as “evidence-based management”? Academy of Management Review, 31(2), 256–269.
Rousseau, D. M., & ten Have, S. (2022). Evidence-based change management: Putting evidence to work in your organization. Oxford University Press.
Thomke, S. (2020). Experimentation works: The surprising power of business experiments. Harvard Business Review Press.
