Update README.md - Change Technical key features and Business value sections to bullets

Bug 4983: Bug - README - Change business value section to bullets, so it matches the formatting of the Technical key features section

Author: rturknett

README.md

@@ -14,14 +14,10 @@ This solution accelerator improves contact center operations by summarizing cust
 
 ### Technical key features
 
-#### Data processing
-Microsoft Fabric processes both audio and conversation files at scale, leveraging its benefits for efficient and scalable data handling
-#### Summarization and key phrase extraction​ 
-Azure OpenAI is used to summarize long conversations into concise paragraphs and extract relevant key phrases
-#### Speech transcription and diarization​
-Azure Speech is used to transcribe audio files, including speaker diarization for post-call analytics. Diarization is the process of recognizing and separating individual speakers into mono-channel audio data​
-#### Analytics dashboard​
-Power BI is used to visualize the correlation between operational metrics and AI-generated conversational data​
+- **Data processing:** Microsoft Fabric processes both audio and conversation files at scale, leveraging its benefits for efficient and scalable data handling
+- **Summarization and key phrase extraction​:** Azure OpenAI is used to summarize long conversations into concise paragraphs and extract relevant key phrases
+- **Speech transcription and diarization​:** Azure Speech is used to transcribe audio files, including speaker diarization for post-call analytics. Diarization is the process of recognizing and separating individual speakers into mono-channel audio data​
+- **Analytics dashboard​:** Power BI is used to visualize the correlation between operational metrics and AI-generated conversational data​
 
 \
 ![image](/images/readMe/ckm-v2-ui.png)
@@ -37,16 +33,11 @@ The contact center manager uses their dashboard to identify how LLM-generated co
 Company personnel (employees, executives) looking to gain conversational insights in correlation with operational Contact Center metrics would leverage this accelerator to find what they need quickly.
 
 ### Business value
-#### Conversation analysis​
-Generative AI analyzes call transcripts, summarizes content, identifies and aggregates key phrases for data visualization​
-#### Automated customer satisfaction​
-Generative AI determines the post-call satisfaction rating of a customer’s experience with their agent
-#### Operational clarity​
-Relevant metrics such as call volume, handling time, and call resolution are pulled from the same call logs for operational data visualization​
-#### Unified data​
-Unstructured (call transcripts) and structured (operational metrics) data are both analyzed and visualized within the same application​
-#### Targeted decision enablement​
-Enable agents and managers to achieve glanceable insight recognition, corollary information analysis, and accelerated decision making​
+- **Conversation analysis​:** Generative AI analyzes call transcripts, summarizes content, identifies and aggregates key phrases for data visualization​
+- **Automated customer satisfaction​:** Generative AI determines the post-call satisfaction rating of a customer’s experience with their agent
+- **Operational clarity​:** Relevant metrics such as call volume, handling time, and call resolution are pulled from the same call logs for operational data visualization​
+- **Unified data:** ​Unstructured (call transcripts) and structured (operational metrics) data are both analyzed and visualized within the same application​
+- **Targeted decision enablement:** Enable agents and managers to achieve glanceable insight recognition, corollary information analysis, and accelerated decision making​
 
 ### Products used/licenses required