Artificial intelligence (AI) projects are some of the most exciting and fast-moving initiatives in today’s organizations. But while AI systems can fail because of technical problems, in practice they often fail for another reason: poor execution. Blockers aren’t tracked, responsibilities blur, teams lose alignment, or deliverables don’t meet the quality standards promised to stakeholders.

Lead and Evaluate AI Project Implementations

Lead and Evaluate AI Project Implementations
This course is part of Managing AI Projects That Ship and Scale Specialization

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December 2025
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There is 1 module in this course
Artificial intelligence (AI) projects are some of the most exciting and fast-moving initiatives in today’s organizations. But while AI systems can fail because of technical problems, in practice, they often fail for another reason: poor execution. Blockers aren’t tracked, responsibilities blur, teams lose alignment, or deliverables don’t meet the quality standards promised to stakeholders. This course, AI Project Implementation: Playbooks, QA, and Readiness, is designed to help you avoid those pitfalls. It focuses on two practical skills that every project manager and program lead needs: coordinating project workstreams with implementation playbooks and validating deliverables through quality assurance (QA) and acceptance testing. Together, these skills ensure that AI projects don’t just get built—they get delivered in a way that is reliable, accountable, and ready for real-world deployment.
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6 videos2 readings3 assignments
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Chaitanya A.
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Johns Hopkins University
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