Deploying and Administering Workplace AI Tools

• Categories of enterprise AI technologies
• Commercial versus open-source AI
• Large language model platforms
• AI applications versus AI platforms
• Matching tool categories to technical requirements

• Cloud-based AI models and managed services
• AWS AI services and SageMaker
• Microsoft Azure AI services
• Google Cloud AI and Vertex AI
• Text analysis services
• Document and image extraction
• Translation and speech services
• Cloud AI consumption and pricing

Hands-on Lab: Run Pre-Built Cloud AI Services — Use AWS, Microsoft Azure, or Google Cloud to execute multiple pre-built AI services, calculate expected usage cost, configure a budget alert, and remove created resources after testing.

• AI copilots and managed assistants
• AI for documents, meetings, communication, and coding
• Team AI workspace administration
• User roles and permissions
• Data-retention and model-training settings
• Approved connectors
• Workspace instructions and usage reporting

Hands-on Lab: Configure a Team AI Workspace — Configure a managed AI workspace for a defined department, including users, permissions, retention settings, approved connectors, workspace instructions, and usage reporting.

• Building technical evaluation criteria and test cases
• Running comparable test cases across competing tools
• Performing security and privacy reviews using vendor documentation
• Licensing and consumption-based pricing
• Calculating setup and support costs
• Calculating total cost of ownership
• Build-versus-buy technical considerations
• Selecting tools based on documented test evidence

Hands-on Lab: Hands-On Tool Evaluation — Test competing AI tools against the same tasks, build a weighted scoring matrix, complete security and privacy reviews, calculate 12-month total cost, and document the technical basis for the selected platform.

• Implementing technical access and data-handling controls
• Maintaining an authorized technology list
• Assigning technical roles and responsibilities
• Configuring data-handling controls
• Enforcing access requirements
• Defining and applying configuration standards
• Managing configuration changes and reviews

• Designing a controlled AI pilot
• Configuring pilot users
• Defining measurable success criteria
• Deployment requirements
• Moving from pilot to broader use
• Training users to operate configured tools
• Rollback and contingency planning

• IT and operations responsibilities
• AI service troubleshooting
• Supporting connected applications and users
• Usage and cost monitoring
• Infrastructure considerations
• Operational escalation
• Maintaining configured AI services over time

Produce a complete technical rollout package for an AI platform, including configured workspace evidence, pilot design, success measures, onboarding procedure, support process, rollback plan, and usage/cost monitoring controls. The capstone is assessed against a skills-based rubric and must be successfully completed to earn the CERT4TECH Certificate in Choosing and Rolling Out AI Tools.