A practical mindset for digital transformation in the age of AI
For small and mid-sized businesses, artificial intelligence can feel like a paradox.
On one hand, tools like ChatGPT promise speed, leverage, and cost savings. On the other, many owners quickly discover that AI outputs can feel confident but wrong, useful one moment and unreliable the next. This tension is not a flaw—it’s a signal.
Before deploying AI into your operations, marketing, or customer workflows, there’s a far more effective first step:
Use AI on yourself first.
Not for business.
For thinking.
This post explains why that matters—and how understanding AI’s limitations at a human level leads to far better business outcomes.
The Meaning We Make: A Personal Framework for Modern Wisdom
Small business owners are already overloaded with information. Dashboards, metrics, notifications, and now AI-generated suggestions arrive faster than any team can realistically evaluate.
AI doesn’t remove this problem—it amplifies it.
The real opportunity lies in learning how meaning is formed before automating decisions.
One mindfulness-aligned framework I use personally—and recommend before any business AI implementation—is what I call the 3P Method:
Possible → Probable → Plausible
It’s a simple way to convert raw information into insight, and it mirrors both human cognition and how modern AI systems operate.
Why AI Feels “Off” Sometimes (and Why That’s Normal)
At a technical level, AI systems work by collapsing many inputs into a single output—much like the human brain.
A helpful analogy is the classic “beans in a jar” exercise.
Most people don’t calculate volume and density. They glance, anchor to a visual estimate, and adjust intuitively. Multiple heuristics collapse into one conclusion.
AI does something similar.
But here’s the catch: small sample sizes exaggerate variance.
Think of a coin flip:
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Over infinite flips → 50/50 balance
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Over three flips → chaos feels amplified
AI outputs operate in this same probabilistic space. In short interactions, randomness feels like error—even when the system is behaving correctly.
For a business owner expecting certainty, this can be jarring.
Impermanence, Variance, and the Myth of “Hallucination”
There’s a common phrase now: “ChatGPT hallucinated.”
But let’s slow that down.
AI systems are bound by electronics.
Electronics are bound by matter.
Matter is bound by physical variance.
No system—human or machine—operates in perfect repetition.
In Eastern philosophy, this is known as impermanence: no moment is ever identical to the last. Every observation carries context, drift, and subtle change.
When AI produces an unexpected answer, it’s often exposing something deeper:
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our assumptions
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our memory gaps
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or our expectation that “objective” tools should be infallible
That expectation is the real risk for businesses.
A Real Example: Memory vs. Machine
I once tried to recall the winner of The Apprentice. The memory felt vivid—emotionally anchored. When ChatGPT couldn’t confirm it, two possibilities emerged:
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My memory was wrong
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The model was wrong
What mattered wasn’t which was correct—it was how quickly subjective memory felt like objective truth.
This is exactly how AI errors enter business workflows.
When teams don’t understand how meaning is formed, they treat AI output as fact instead of context. That’s when automation becomes liability.
The Human Mind (and AI) Think in Rhythms, Not Checklists
Humans don’t reason linearly. We reason rhythmically.
Ideas surface, fade, reappear, and recombine. AI mirrors this pattern through probabilistic generation.
This is why forcing AI into rigid business logic before understanding its behavior leads to frustration.
Instead of asking:
“Why did AI get this wrong?”
A better question is:
“What interpretation path did this answer follow?”
That’s a skill best learned personally—before applying it to revenue-critical systems.
The 3P Method: A Practical Bridge Between AI and Wisdom
Here’s the framework in action.
The Three P’s
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Possible
What are all the interpretations or responses that could exist? -
Probable
Which ones are statistically or logically most likely? -
Plausible
After reflection and context, which interpretation aligns with lived experience?
This mirrors both neuroscience and modern AI architectures:
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free association
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executive filtering
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contextual salience
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pattern memory
When business leaders use this method personally with AI—journaling, planning goals, or stress-testing ideas—they develop intuition that no prompt library can replace.
Why This Matters for SMB Digital Transformation
For companies with 1–50 employees, AI mistakes are expensive:
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wrong messaging
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misinterpreted customer intent
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brittle automations
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misplaced trust in outputs
Teams that experiment personally first:
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understand uncertainty
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design better guardrails
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build AI systems that support humans instead of replacing judgment
This is the difference between using AI and leading with AI.
Final Thought: AI Is a Mirror Before It’s a Tool
AI doesn’t just generate content—it reflects how we think.
When used consciously, it becomes a form of structured reflection. When used blindly, it amplifies noise.
The businesses that succeed with AI are the ones whose leaders first learned how to regulate meaning—inside their own thinking—before scaling it into operations.
Ready to Give AI a Trial Run
If you’re an SMB owner who wants to apply AI responsibly, strategically, and at the programming level—not just via prompts, I’d be glad to help.
👉 If you want to work with someone who understands artificial intelligence from the systems and engineering layer—not just the interface—reach out.
The future of digital transformation isn’t about automation alone.
It’s about wisdom applied at scale.