diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md
index adb7918..e516311 100644
--- a/src/content/when_to_use_ai.md
+++ b/src/content/when_to_use_ai.md
@@ -27,16 +27,19 @@ Anyways, without further ado, I present to you the first, pipeline written, AI c
 
 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.
 
-## The Spreadsheet Dilemma: Where AI Fails
+## The Spreadsheet Dilemma: Where AI Can help, and where it hurts
 
 **Scenario:** Mapping work types to categories in a spreadsheet with thousands of entries, like distinguishing between "Painting," "Repainting," "Deck Painting," or "Stucco Repainting."
 
-**Why AI Struggles Here:**
+**Where AI Helps:**
 
 *   **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.
+
+**Where AI Struggles:** 
+
 *   **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:**
+**Traditional Methods Are Easier for Deterministic Problems:**
 
 *   **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.
 
@@ -49,6 +52,10 @@ Okay, so I've been getting this question a lot lately: "When should we use AI?"
 *   **Natural Language Processing (NLP):** AI understands context, tone, and intent in unstructured data, making it ideal for tasks like chatbot responses or content analysis.
 *   **Pattern Recognition:** AI identifies trends or anomalies in large datasets that humans might miss, such as predictive maintenance in industrial settings.
 
+**Why Traditional Methods Don't:**
+
+* **There is no easily discernable patter:** If the pattern doesn't exist in a deterministic sense there will be little someone can do without complex regex and 'whack a mole' style programming. 
+
 ## Hybrid Approaches: The Future of Efficiency
 
 While traditional methods remain superior for precise calculations, AI can assist in setting up initial parameters or generating insights. For example: