The Decisions That Define Us: A Practical Guide for Smarter Choices in the Age of Data and AI
- Derrick Yuen, MBA
- Jul 16
- 5 min read
Updated: Jul 18
We all make decisions every day—what to eat, what to wear, which task to start first. Most of these pass quickly, without much reflection. But at work, decision-making becomes something else entirely.
Now, there are outcomes to explain, stakeholders to answer to, and expectations to meet. The stakes go up—and so does the pressure to get it right.
And yet, decision-making remains a largely invisible skill. It’s not formally taught. It doesn’t sit on a performance scorecard. But if you look closely at successful professionals and leaders, one trait stands out:
They consistently make decisions with clarity, intent, and accountability.
So how do they do it?
🔍 Better Insights Don't Automatically Mean Better Decisions
We live in a world where data is everywhere. Dashboards are common. AI tools are growing. But while insights are increasingly available, turning those insights into action remains a challenge.
Insights are only valuable when someone has the judgment to interpret them and the courage to commit to a course of action.
This is the gap between analysis and decision. It’s not about having more data, but about knowing what to do with it. Without that ability, insights just become noise. And too much analysis can paralyze action altogether.
“We are drowning in information, while starving for wisdom. The world henceforth will be run by synthesizers, people able to put together the right information at the right time, think critically about it, and make important choices wisely” – E. O. Wilson
These are the lessons rarely taught in class—but they make all the difference at work.
🤖 What About AI? Can It Make Decisions for Us?
Some now believe AI can make decisions in place of humans. But let’s step back.
AI, at its core, is a statistical engine. It predicts the next word, pixel, or sound based on patterns from past data. It can generate coherent responses—but coherence is not the same as intentionality.
AI doesn’t understand consequences. It doesn’t weigh ethics. It doesn’t hold accountability. What it does well is average across known examples—and in some domains, that average is good enough.
So, where does it fit?
AI is helpful for low-stakes, routine, or well-bounded decisions where efficiency matters more than nuance.
But for strategic, complex, or irreversible decisions—the kind that shape culture, reputation, or lives—humans still matter. Not just any human, but the right one: experienced, context-aware, and accountable.
At FYT, we believe:
Data equips. AI assists. But humans decide—and are ultimately accountable.
✅ The 5D Decision Lens: A Smarter Way to Navigate Decisions
To help teams and leaders make better decisions, FYT uses a simple but powerful tool called the 5D Decision Lens. It helps classify decisions across five dimensions:
1. Reversibility
Reversible: You can test, adjust, or walk it back.
Irreversible: Once done, it’s hard or costly to undo.
Examples:
Reversible: Trying a new email format or changing a meeting time.
Irreversible: Downsizing a department or signing a multi-year contract. Getting a Tattoo.
2. Stakes
Low Stakes: Minor impact; mistakes are recoverable.
High Stakes: Impacts people, money, brand, or strategy.
Examples:
Low: Choosing internal software fonts.
High: Approving layoffs, making a public position statement.
3. Time Sensitivity
Urgent: Must act now (e.g., crisis, deadline).
Negotiable: The clock is ticking, but more time can be created.
Examples:
Urgent: Responding to a security breach.
Negotiable: A “Friday” deadline that’s self-imposed.
4. Complexity
Simple: Clear cause-effect; predictable.
Complex: Multiple variables, feedback loops, evolving dynamics.
Examples:
Simple: Ordering office supplies.
Complex: Reworking an incentive system or changing org culture.
5. Learnability
Learnable: Can test and refine.
One-shot: No do-overs; full impact shows up later.
Examples:
Learnable: Piloting a new workflow or tool.
One-shot: Making a public apology or restructuring a company.
🧩 Real-World, Multi-Dimensional Decisions
Most real-life decisions combine several dimensions. That’s where the 5D Lens becomes powerful.
💼 High-Stakes + Reversible + Time Available
Example: Deciding whether to publicly support a sensitive social issue.🧭 Approach: Use the time to test internally, anticipate responses, and plan reversibility—but don’t rush just because you can undo it.
🔄 Low Stakes + Complex + Learnable (Eventually)
Example: Choosing a new internal knowledge-sharing platform.🧭 Approach: Pilot with a small group. Don’t over-engineer the decision—learn through use.
⏱️ Irreversible + Low Stakes + Time Sensitive
Example: Submitting staff name tags for printing.🧭 Approach: Quick quality check, then ship. Don’t sweat perfection.
⚖️ High-Stakes + Irreversible + Learnable (Too Late)
Example: Eliminating a product line or making a senior hire.🧭 Approach: Use every tool—data, consultation, scenario planning—before acting.
🧠 Multi-Dimensional Decisions Still Require Humans
As you can see, real-world decisions are often messy, layered, and context-specific, touching multiple dimensions at once—reversibility, complexity, urgency, and more.
This is where standard decision-making rubrics break down. You can’t simply plug the situation into a model and expect the “right” answer to emerge.
And while AI can help process data or simulate options, it lacks the contextual awareness, ethical judgment, and situational insight that real decisions often demand. That’s why, even with AI in play, it is humans—with the right skills, mindset, and experience—who remain essential.
When decisions span multiple dimensions, there are no perfect answers. What matters is the ability to weigh trade-offs, navigate ambiguity, and move forward with intent and accountability.
📊 When to Use Data, and When to Use Judgment
Use Data / AI When… | Use Human Judgment When… |
Outcomes are measurable and repeatable | Stakes are high and context or emotion matters |
You can test and iterate safely | Feedback is slow, incomplete, or high-impact |
You’re optimizing something already understood | You’re setting direction or making value-based calls |
🎯 Final Thought: The Goal Is Not to Use Data or AI—It’s to Solve the Right Problem
In this age of digital acceleration, it’s easy to get caught up in buzzwords. Many chase the idea of being “data-driven” or “AI-powered” as if they were goals in themselves.
But they’re not.
Data and AI are means to an end. The end is always the problem you’re trying to solve.
Sometimes that problem is best addressed with insights, automation, or modeling. Other times, it needs dialogue, empathy, or gut instinct.
At FYT, we believe in starting with the problem. Once that’s clear, you can decide:
What decisions need to be made
Who should make them
What tools will support—not distract from—that process
Because in the end, decision-making isn’t about using more tech—it’s about using the right judgment, at the right time, with the right tools, for the right problem.
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