AI Technical Implementation Considerations
Microsoft Copilot Readiness
To successfully implement Microsoft Copilot, organizations should prepare their Microsoft 365 environment:
1. Licensing and Prerequisites
Ensure appropriate Microsoft 365 licensing (Business Standard/Premium or E3/E5) plus Copilot licenses. Microsoft has removed the previous 300-seat minimum requirement, making Copilot more accessible to SMBs.
2. Semantic Index Setup
Configure the Semantic Index for Copilot, which maps your organizational data for AI retrieval. This index respects existing permissions and security boundaries.
3. Data Governance
Review and organize your Microsoft 365 content. Clean up outdated information, apply appropriate sensitivity labels, and ensure permissions are correctly configured.
4. Access Controls
Verify that your permission structure accurately reflects who should access what information, as Copilot will respect these settings when retrieving data.
Private LLM Architecture and Deployment
A typical private LLM implementation relies on Retrieval Augmented Generation (RAG) architecture:
1. Select Model Approach
Choose between hosting an open-source model like Falcon or Meta’s Llama 3, which offer affordable, customizable AI that SMBs can deploy locally or on cloud platforms, or using managed services like Azure OpenAI or AWS Bedrock.
2. Data Preparation
Prepare and index your business data in a vector database, which allows efficient semantic search and retrieval.
3. Orchestration Layer
Implement the logic that takes user queries, retrieves relevant information from your data sources, and constructs prompts for the LLM.
4. User Interface
Create appropriate interfaces (web applications, chatbots, API endpoints) for users to interact with the LLM.
5. Monitoring and Feedback
Implement logging, performance monitoring, and feedback mechanisms to continuously improve results.
Security and Compliance Considerations
For both solutions, prioritize:
Implement strong authentication and authorization to ensure users only access appropriate information.
Use encryption for data at rest and in transit, and implement appropriate retention policies.
Maintain comprehensive logs of AI system usage for compliance and troubleshooting.
Implement guardrails to prevent inappropriate outputs or data leakage.
Develop clear guidelines for AI usage, including review processes for AI-generated content.
