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edX

AI at Work: Tools, Workflows & Real-World Application

Hewlett-Packard via edX

Overview

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Knowing about AI is not the same as working with it. The HP's course AI at Work: Tools, Workflows & Real-World Application is designed for learners who have built their AI foundations and are ready to put them to work — across industries, roles, and everyday professional contexts.

This course moves beyond concepts into action. Learners explore how to select the right AI tools for the right tasks, design AI-augmented workflows, and evaluate outputs critically. From writing and research to data analysis, communication, and creative problem-solving, the program equips learners with the practical fluency to integrate AI into their professional lives with confidence and judgment.

Developed by HP, the instruction is led by industry experts who bring real-world experience across sectors where AI is already reshaping how work gets done.

Upon completion, learners will be equipped to identify high-value AI use cases in their own contexts, build effective human-AI workflows, and apply prompt engineering with depth and precision. The course is ideal for professionals seeking immediate productivity gains, educators looking to model AI-integrated practice, and anyone ready to move from AI awareness to AI action.

No coding required. Just readiness to move from understanding AI to using it.

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Syllabus

  • Why the shift from AI knowledge to AI practice matters and how to build a practitioner mindset
  • How to evaluate and choose the right AI tool for different tasks
  • Advanced prompt engineering techniques including chain-of-thought and meta-prompting
  • How to use AI effectively for writing, editing, and communication
  • How to leverage AI for research, knowledge synthesis, and fact-checking
  • How to apply AI to data analysis, visualization, and decision support
  • How to use AI in creative and design workflows responsibly
  • How to design efficient human-AI collaboration workflows
  • How to evaluate, audit, and trust AI outputs critically
  • How to build a sustainable personal AI practice

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