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- Learn how to align AI solutions with business goals, scale AI adoption, and use Microsoft AI technologies for enterprise success.
After completing this module, you will be able to:
- Explain the architect's role in driving AI adoption and transformation.
- Identify key responsibilities of an AI architect in business contexts.
- Understand how architects align AI solutions with organizational goals.
- Apply best practices for scaling AI across enterprise environments.
- Identify core Microsoft AI services and tools.
- Explore Microsoft Copilot solutions and their business value.
- Understand how generative AI unlocks productivity in enterprise environments.
- Identify OOB Microsoft AI agent resources available for business solutions.
- Learn to analyze, design, and implement AI-powered business solutions using agents, generative AI, and Microsoft Copilot for productivity.
After completing this module, you will be able to:
- Explain how AI agents support task automation, data analytics, and decision-making.
- Describe the role of Microsoft Copilot and generative AI in enhancing productivity.
- Assess scenarios where AI agents add measurable value to enterprise environments.
- Evaluate grounding data quality across accuracy, relevance, timeliness, cleanliness, and availability.
- Organize business solution data for AI readiness and scalable consumption.
- Learn to design an enterprise AI strategy using Azure's Cloud Adoption Framework. Align AI agent lifecycle with business goals for operational excellence.
After completing this module, you'll be able to:
- Map CAF's AI adoption phases to the AI agent lifecycle.
- Design an enterprise-ready operating model for AI agents.
- Select and apply Microsoft platforms for AI solutions.
- Utilize checklists to transition AI workloads from proof of concept to production.
- Learn to assess costs and benefits of AI solutions. Develop ROI analyses, evaluate TCO, and decide to build, buy, or extend AI components.
After completing this module, you will be able to:
- Define ROI criteria and evaluate total cost of ownership (TCO) for AI solutions.
- Create a comprehensive ROI analysis for AI-powered business processes.
- Analyze and decide whether to build, buy, or extend AI components.
- Implement model routing strategies to optimize AI performance and cost.
- Learn to design AI agents tailored to business needs using Dynamics 365 and Copilot. Explore Responsible AI, custom connectors, and integration strategies.
After completing this module, you will be able to:
- Apply Responsible AI principles to AI agent design.
- Configure business terms and customize Copilot in Dynamics 365.
- Design custom connectors and integrate AI agents with Dynamics 365 Contact Center.
- Build task-focused and autonomous agents using Copilot Studio.
- Implement prompt-driven conversational agents with adaptive responses.
- Learn to design scalable, secure, and customizable AI solutions using Microsoft platforms. Explore extensibility strategies for enterprise AI.
After completing this module, you will be able to:
- Design scalable and secure AI solutions using Microsoft platforms.
- Leverage custom models in Microsoft Foundry.
- Design and operationalize agents in Microsoft 365 Copilot.
- Extend agent capabilities using Copilot Studio and Model Context Protocol (MCP).
- Apply governance and lifecycle management for AI solutions.
- Learn to orchestrate, configure, and extend AI-driven experiences in Dynamics 365 and Microsoft 365 Copilot for finance, supply chain, and customer service.
After completing this module, you will be able to:
- Orchestrate AI features and Copilot agents across Dynamics 365 and Microsoft 365 applications.
- Design secure, compliant, and extensible AI solutions.
- Configure and operationalize prebuilt Copilot agents for finance, supply chain, sales, and service scenarios.
- Apply best practices for responsible AI, workflow integration, and success measurement.
- Learn to monitor, analyze, and tune AI agents for reliability, performance, and continuous improvement in enterprise environments.
After completing this module, you'll be able to:
- Establish monitoring frameworks for AI agents.
- Apply industry tools and processes for observability.
- Analyze backlogs and user feedback for actionable insights.
- Apply AI-based diagnostic and tuning methods.
- Monitor performance and metrics to optimize agent workflows.
- Learn to validate and maintain AI-powered business solutions with structured testing frameworks, metrics, and governance for enterprise reliability.
After completing this module, you'll be able to:
- Design structured testing processes for AI agents, custom models, and multi-application scenarios.
- Define measurable validation criteria for performance, safety, and compliance.
- Implement scalable testing strategies using Copilot for consistency and coverage.
- Ensure AI solutions align with enterprise goals and remain trustworthy throughout their lifecycle.
- Learn to design ALM processes for AI solutions, ensuring governance, security, and consistency across development, testing, and production.
After completing this module, you will be able to:
- Design ALM processes for AI components like datasets, prompts, and models.
- Establish governance, security, and monitoring across environments.
- Ensure consistent and compliant AI behavior from development to production.
- Learn to design secure, governed, and compliant AI systems that align with organizational policies and Responsible AI principles.
After completing this module, you will be able to:
- Design AI systems that balance innovation with accountability.
- Apply identity, access control, and data governance to secure AI agents.
- Implement observability and threat protection for AI solutions.
- Translate compliance requirements into technical controls.
- Mitigate vulnerabilities across prompts, models, and workflows.
