Choosing Your First AI Application
John Savill’s Technical Training discusses how organizations can select their first AI application, covering business value, technical requirements, feasibility, and best practices for minimizing risk.
Choosing Your First AI Application
Author: John Savill’s Technical Training
This session explores crucial elements for organizations planning to adopt their first AI application. Topics include preparation, evaluating use cases, and the intersecting dimensions of business value, technical feasibility, ethical implications, and risk management.
Key Chapters
- Introduction
- Getting ready: Preparing teams, resources, and expectations
- Example use cases: Reviewing practical AI deployments
- Key factors to consider: Technical, business, and data perspectives
- Business value: Aligning AI with organizational goals
- Data: Assessing data quality and readiness
- Technical feasibility: Determining whether AI is achievable within existing constraints
- Ethical implications: Minimizing negative impacts and ensuring compliance
- Scale/ROI: Considering scalability and financial justification
- Minimizing risk: Strategies for risk mitigation
- Summary and closing thoughts
Supporting Resources
- Whiteboard diagram
- Azure Learning Path
- Certification Content Repository
- Weekly Azure Update
- Azure Master Class
- DevOps Master Class
- PowerShell Master Class
- Certification Cram Videos
- Mentoring Content
- FAQ
Main Takeaways
- Identify the driving business value for AI adoption
- Evaluate your organization’s data readiness
- Assess the technical feasibility of potential AI workloads
- Account for ethical and compliance implications
- Ensure ROI and scalability are considered from the outset
- Proactively develop risk mitigation strategies
For hands-on guidance, refer to the linked resources and track further Azure and AI educational material from John Savill’s Technical Training.