There is a phrase that has existed in technology and systems thinking for decades: GIGO —> Garbage In, Garbage Out. On the surface, it sounds harsh, almost dismissive. But beneath that blunt framing lies one of the most honest truths about project leadership, system implementations, and ultimately, human behavior.
The outcome you get is directly tied to what you put in.
In this installment of Project Leadership Unlocked, I want to expand GIGO beyond its traditional definition and reframe it into three distinct realities that project leaders live with every day:
- Garbage In —> Garbage Out
- Good In —> Good Out
- Great In —> Great Out
This is not just a theory about data or systems. It is a theory about care at the core and how results are directly correlated to the depth of care, intention, and discipline we invest from the beginning.
I May Be Repeating Myself
Apologies, this blog ties so much into what I posted last Friday. It almost feels as though my thoughts were stuck and I promise a different theme in my next number of posts.
Garbage In —> Garbage Out: The Cost of Indifference
Most project failures are not caused by one catastrophic mistake. They are caused by a series of small decisions where care was optional and overlooked.
Garbage in shows up in many familiar forms during implementations:
- Vague requirements
- Rushed customers
- Incomplete discovery sessions
- Assumptions left unchallenged
- Poor data quality accepted “for now”
- Stakeholder alignment treated as a checkbox
- Testing compressed to protect timelines
None of these feel dramatic in the moment. In fact, they often feel pragmatic. But what gets ignored upstream never disappears… it simply resurfaces downstream as defects, rework, missed expectations, and frustrated teams.
When garbage goes in, garbage comes out.
Not because teams are incapable, but because the foundation they were asked to build upon was unstable from the start. As a project leader, allowing garbage in is rarely about lack of knowledge or experience, it is almost always about lack of care under pressure.
Good In —> Good Out: The Plateau of “Good Enough”
Many projects never fail outright. They land in a far more mysterious spot: good enough.
Good in looks responsible:
- Requirements are mostly clear
- Risks are documented, but not deeply explored
- Data is cleansed “enough to go live”
- Testing validates primary paths
- Communication happens, but inconsistently
The result is predictable: Good out
- The system works
- The project closes
- The business adapts
But legacy actions persist like manual workarounds, latent defects, lingering dissatisfaction, and teams who quietly accept limitations as “just the way it is.”
Good in does not create disasters, but it also does not create excellence. It creates mediocrity that slowly taxes organizations over time.
This is where many well-intentioned leaders unintentionally settle and it is not because they don’t care, but because they care within the boundaries of comfort, time, and habit.
Great In —> Great Out: Care as a Leadership Discipline
Great outcomes are never accidental. Great in requires intentionality that goes beyond process compliance:
- Requirements are challenged, not just captured
- Assumptions are surfaced and tested early
- Data quality is treated as a product feature, not a cleanup task
- Testing is designed to reveal truth, not validate optimism
- Risks are discussed openly, without fear or blame
- Stakeholders are partners, not spectators
This level of input demands something more than skill and that is care as a leadership discipline.
- Great project leaders do not ask: “What is the minimum we can do to move forward?”
- They ask: “What does this deserve if we want it to succeed?”
And here is the irony: when great care goes in, teams often get more than they expected out of the result. They get stronger trust, clearer ownership, better adoption, and systems that enable rather than constrain.
The Core Truth: Results Reflect Care
GIGO is often framed as a technical warning, but in project leadership it is a mirror.
- If you rush clarity, you get confusion.
- If you tolerate ambiguity, you get rework.
- If you treat quality as negotiable, you get fragile outcomes.
- If you invest deeply and early, you get resilience later.
The results you see at the end of a project are rarely a surprise. They are an echo of the care, or lack of care, applied throughout the journey.
Other Fun Examples of GIGO
- Gross In —> Gross Out
- Glorious In —> Glorious Out
- Gibberish In —> Gibberish Out
- Goofy In —> Goofy Out
- Grumpy In —> Grumpy Out
- Gossip In —> Gossip Out
- Guesswork In —> Guesswork Out
- Grit In —> Gritty Out