src/content/when_to_use_ai.md

@@ -23,53 +23,43 @@ Anyways, without further ado, I present to you the first, pipeline written, AI c
 
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-# When to Use AI: Navigating the Right Scenarios
+# When to use AI 😄
+*A journalist, software developer, and DevOps expert’s take on when AI is overkill and when it’s just the right tool*
 
-Okay, so I've been getting this question a lot lately: "When should we use AI?" or even more frustratingly, "Why can't AI do this?" It's like asking when to use a hammer versus a screwdriver. Sometimes AI is the perfect tool, other times it's better left in the toolbox. Let me break down some scenarios where AI shines and where it might not be the best bet.
+When I was building a spreadsheet called “shudders,” I was trying to figure out how to automate the process of mapping work types to work requests. The dataset was full of messy, unstructured text, and the goal was to find the best matches. At first, I thought, “This is a perfect use case for AI!” But then I realized: *this is the kind of problem where AI is basically a human’s worst nightmare*.
 
-## The Spreadsheet Dilemma: Where AI Fails
+So, let’s break it down.
 
-**Scenario:** Mapping work types to categories in a spreadsheet with thousands of entries, like distinguishing between "Painting," "Repainting," "Deck Painting," or "Stucco Repainting."
+### 🧠 When AI is *not* the answer
 
-**Why AI Struggles Here:**
+AI is great at pattern recognition, but it’s not great at *understanding context*. For example, if I had a list of work types like “customer service,” “technical support,” or “maintenance,” and I needed to map them to work requests that had vague descriptions like “this task took 3 days,” AI would struggle. It’s like trying to find a needle in a haystack—*but the haystack is made of human language*.
 
-*   **Fuzzy Matching & Contextual Understanding:** AI excels at interpreting relationships between words (e.g., recognizing "Deck Painting" as a subset of "Painting"). However, traditional methods with regex or string manipulation fail here because they lack the nuanced judgment needed to handle ambiguity.
+The problem with AI in this scenario is that it’s *not good at interpreting ambiguity*. If the work types are vague, the AI might mislabel them, leading to errors. Plus, when the data is messy, AI can’t keep up. I remember one time I tried to use a chatbot to classify work requests. It was so confused, it thought “customer service” was a type of “technical support.” 😅 The result? A spreadsheet full of “unknown” entries.
-*   **Precision Over Ambiguity:** Calculations requiring exact values (e.g., average durations) are better handled by deterministic algorithms rather than AI’s probabilistic approach.
 
-**Traditional Methods Win Here:**
+### 🧮 When AI *is* the answer
 
-*   **Formula-Based Logic:** Building precise formulas for workload analysis relies on clear, unambiguous rules. AI can’t replace the need for human oversight in such cases.
+There are some scenarios where AI is *definitely* the way to go. For example, when you need to automate repetitive tasks, like calculating workloads or generating reports. These tasks are math-heavy and don’t require creative thinking. Let’s say you have a list of work orders, each with a start time, end time, and duration. You want to calculate the average time per task. AI can do that with precision. It’s like a calculator, but with a personality.
 
-## When AI Shines: Contextual and Unstructured Tasks
+Another example: if you need to generate a report that summarizes key metrics, AI can handle that. It’s not about creativity, it’s about logic. And that’s where traditional programming shines.
 
-**Scenario:** Automating customer support with chatbots or analyzing social media sentiment.
+### 🧪 The balance between AI and human oversight
 
-**Why AI Works Here:**
+AI is a tool, not a replacement for human judgment. While it can handle the *analyzing* part, the *decisions* still need to be made by humans. For instance, if you’re trying to decide which work type to assign to a request, AI might suggest “customer service” based on keywords, but the final decision depends on context.
 
-*   **Natural Language Processing (NLP):** AI understands context, tone, and intent in unstructured data, making it ideal for tasks like chatbot responses or content analysis.
+So, in the end, AI is a *helper*, not a *replacement*. It’s great for the parts that are repetitive, but the parts that require nuance, creativity, or deep understanding? That’s where humans step in.
-*   **Pattern Recognition:** AI identifies trends or anomalies in large datasets that humans might miss, such as predictive maintenance in industrial settings.
 
-## Hybrid Approaches: The Future of Efficiency
+### 🧩 Final thoughts
 
-While traditional methods remain superior for precise calculations, AI can assist in setting up initial parameters or generating insights. For example:
+AI is like a superpower—great at certain things, not so great at others. It’s not a magic wand, but it’s a tool that can save time and reduce errors when used right.
 
-*   **AI Proposes Formulas:** An LLM suggests a workload calculation formula based on historical data.
+So, when is it time to say “AI, nope”? When the data is messy, the tasks are ambiguous, or the results need to be human-approved. And when is it time to say “AI, yes”? When you need to automate calculations, generate reports, or handle repetitive tasks that don’t require creativity.
-*   **Human Checks Validity:** A human ensures the formula’s accuracy before deployment.
 
-## Key Takeaways
+### 🧩 Summary
 
-1.  **Use AI** for tasks involving:
+| Scenario | AI? | Reason |
-    *   Unstructured data (e.g., text, images).
+|---|---|---|
-    *   Contextual understanding and interpretation.
+| Ambiguous data | ❌ | AI struggles with context |
-    *   Pattern recognition and trend analysis.
+| Repetitive tasks | ✅ | AI handles math and logic |
-2.  **Stick to Traditional Methods** for:
+| Creative decisions | ❌ | AI lacks the ability to think creatively |
-    *   Precise calculations with deterministic logic.
-    *   Tasks requiring error-free accuracy (e.g., financial modeling).
 
-## Conclusion
+In the end, AI is just another tool. Use it when it works, and don’t let it define your workflow. 😄 *And if you ever feel like AI is overstepping, remember: it’s just trying to be helpful. Sometimes it’s not the best choice. Sometimes it’s the only choice.*
-
-AI is a powerful tool but isn’t a one-size-fits-all solution. Match the right approach to the task at hand—whether it’s interpreting natural language or crunching numbers. The key is knowing when AI complements human expertise rather than replaces it.
-
-**Final Tip:** Always consider the trade-offs between precision and context. For tasks where nuance matters, AI is your ally; for rigid logic, trust traditional methods.
-
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