Project Objectives and Context
This is an English translation of the published reading. Apply previously learned AI concepts to a real business scenario and propose an operational improvement plan that can measure value. Focus on strategic planning and business justification; technical system implementation is not required.
Imagine you are a member of an organization's internal team, and your team is exploring how to use AI to improve operational efficiency. You need to identify a specific challenge, investigate feasible solutions, assess implementation conditions, and prepare a briefing that can be clearly presented within five minutes to support future decision-making.
You may use real organizations or create fictional ones. Use brief descriptions and key points to maintain focus, create the presentation using familiar presentation tools, and export it as a PDF for easy reading. The original task suggested at least seven pages; additional pages may be added if the content remains clear and concise.
Seven-part business plan
"some" | What should be explained? |
|---|---|
Executive Summary | Background of the organization; specific business challenges to be addressed; recommendation to augment human work or automate certain repetitive tasks |
Operation Opportunity Analysis | The current challenges, limitations, and inefficiencies in the field; which departments or operational activities may benefit; at least one clearly identifiable process worth optimizing |
AI Concepts and Applications | How to apply; why it might improve the current situation; expected changes to which time, cost, error, or customer experience metrics |
Current vs. Improved Process | Use flowcharts, swimlane diagrams, and other visual tools to illustrate current steps and pain points, then show how AI has changed specific steps or decision points. |
Evaluation criteria | Key performance indicators aligned with business objectives; how to measure success before and after implementation |
Operational Impact and Risks | How do processes, roles, and day-to-day responsibilities change; what challenges might arise regarding data quality, resistance to adoption, or ethical considerations? |
Ethical considerations | What dilemmas, principles, and procedures need to be addressed; how do employees, customers, and other stakeholders are affected? |
These seven sections should support one another. For example, if the abstract mentions reducing errors, the subsequent process section should explain where errors arise, the metrics should allow for comparison of error changes over time, and the risk section should consider new errors that AI might introduce.
How to check scheme quality
The original task focuses on clear, coherent business justifications, rather than on the originality of the concept. Do not add features that are not aligned with business needs solely to demonstrate technical prowess.
Check that all components are clearly described as required, and that no critical assumptions are missing; ensure that business opportunities, application methods, work processes, evaluation criteria, and risks are mutually aligned. A more comprehensive proposal should provide additional evidence explaining why this approach was selected and why the expected benefits are reasonable.
The submission deadline, grading, and individual submission status of the original task are not part of public knowledge. You may use this structure as a practice tool; there is no need to upload organizational materials to the website.
Related Reading
When selecting a specific issue, go back and review AI Work Applications task analysis method; when comparing automation levels, continue reading AI Workflow .
Reference and Extended Reading
The following is an editorial addition.
- Google: Machine learning problem definition : Chinese, assist in converting business objectives into suitable questions for validation.
- NIST AI Risk Management Framework : English, to help assess risks, evaluations, and responsibilities entering the implementation phase.