Human Neuroplasticity → Mismatched Qualities in the Era of AI Agents
Human Neuroplasticity → Mismatched Qualities in the Era of AI Agents
Great question! The core idea here is that skills that were once valuable (like memorizing complex formulas, building big tables, and crafting bar charts) might now be mismatched qualities—things that AI can do better, faster, and more efficiently.
This connects to neuroplasticity, or how the human brain rewires itself based on what we practice. For decades, people trained their brains to be “human Excel sheets”—great at working with structured data, building dashboards, and interpreting bar charts.
🚨 But now AI can do those tasks instantly. So, are we training ourselves for the wrong skills?
## 1. What Are “Mismatched Qualities” in the AI Era?
A mismatched quality is a skill that was once essential for human intelligence but has become obsolete, inefficient, or even limiting due to technological progress.
### Examples of Mismatched Qualities in Analytics & BI:
✅ Building Large Tables & Pivot Reports
- Before AI: Essential for data analysis in Excel & SQL.
- Now: AI agents generate reports & summaries automatically.
- Mismatched? Yes. Humans should focus on decision-making, not manual data structuring.
✅ Writing Complex Formulas & DAX Expressions
- Before AI: Highly valued in BI tools like Power BI.
- Now: AI (like Copilot & GPT-4) can write formulas instantly based on natural language queries.
- Mismatched? Yes. The value now is in framing the right question, not the formula itself.
✅ Interpreting Bar Charts & Trend Lines
- Before AI: Humans analyzed charts to find insights.
- Now: AI can detect patterns & anomalies faster than humans.
- Mismatched? Partially. Humans still need to validate AI outputs, but the core analytical process is shifting.
📌 New Mindset Shift:
Instead of “How can I structure and analyze data?” → “How can I guide AI to ask the right questions and validate its insights?”
## 2. What Skills Are Becoming More Valuable Instead?
If traditional BI skills are becoming mismatched, what should humans focus on instead?
### A. Prompt Engineering & AI-Augmented Analytics
🔹 Why? AI models are only as good as the prompts they receive.
🔹 New skill: Learn how to ask better, domain-specific questions that AI can interpret correctly.
🔹 Example: Instead of manually writing DAX in Power BI, you ask an AI agent:
- ❌ Old approach: “Write a DAX formula for YOY growth.”
- ✅ New approach: “Analyze our last 12 months of sales data and identify if any seasonal anomalies exist. Provide a YOY trend along with possible explanations.”
🧠 AI augments analysis, but humans must drive the right questions.
### B. Scenario-Based Thinking & Systems Analysis
🔹 Why? AI is good at recognizing patterns but bad at understanding uncertainty & long-term strategy.
🔹 New skill: Humans must master probabilistic thinking, scenario planning, and risk analysis.
🔹 Example: Instead of just forecasting future sales, we should ask:
- “What happens if inflation spikes again?”
- “How will AI automation affect our workforce over the next 5 years?”
📌 AI can generate insights, but humans need to frame “What if?” scenarios and interpret the real-world implications.
### C. Data Storytelling & Cognitive Framing
🔹 Why? AI can generate reports, but it doesn’t know what matters to decision-makers.
🔹 New skill: Humans need to translate AI insights into actionable narratives that executives can use.
🔹 Example:
- Instead of saying: “Sales dropped by 12% last quarter.”
- We should say: “Sales dropped by 12%, largely due to supply chain disruptions in Asia. Our predictive model suggests a potential recovery in Q3 if logistics improve.”
📌 The ability to contextualize AI-driven insights is a uniquely human skill.
## 3. The Future: Adapt or Get Stuck in Mismatched Qualities?
🚀 Key Takeaways:
✅ Old analytical skills (bar charts, pivot tables, DAX formulas) are becoming mismatched.
✅ AI is now a co-pilot for analysis—our role is shifting to guiding, validating, and decision-making.
✅ The most valuable skills now are:
- AI-augmented analysis (prompt engineering & domain expertise)
- Scenario thinking (understanding nonlinearity & disruptions)
- Data storytelling (turning insights into action)
💡 Final Thought:
The AI era doesn’t eliminate human intelligence—it redefines it. The question is: Will we evolve with it, or will we keep training ourselves for outdated tasks?