This course supports the Mission Statement of Nagoya University of Commerce & Business by developing students who can judge, rather than merely adopt, new technology. Students are placed in the position of executives deciding whether and where artificial intelligence belongs in a firm. They are required to defend those judgments publicly and revise them under challenge. Cases are drawn from firms competing in the United States, Europe, India, Sweden, Singapore, and Japan, so students practice reasoning across the boundary between the new era of Asia and the rest of the world. By requiring students to reach conclusions with incomplete information and take responsibility for them, the course cultivates the Frontier Spirit and the ethical, innovative leadership the mission calls for.

Artificial intelligence is reshaping how firms compete, how industries are structured, and how work is organized. Understanding this shift requires an integrative grasp of three domains that are rarely taught together: AI, strategy, and operations.

By studying this course, students will deepen their ability to reason as decision-makers rather than as technologists. The course assumes no technical background and does not teach students how to build AI systems. It trains them instead to,

- Judge whether AI is the right solution to a given business problem,
- Identify where in a firm's value chain AI can and cannot create value,
- Decide whether to build a capability, buy it, or partner for it,
- Understand what an organization must change to make AI work.

The course is taught entirely through the case method. There are no lectures in the conventional sense. Each session centers on a real company facing a real decision, and students take the position of the executives who had to decide without knowing how it would turn out. Class time is spent building an argument collectively: students take positions, defend them under questioning, and revise them when a classmate's reasoning proves stronger. The instructor directs that inquiry rather than delivering conclusions. Frameworks and concepts emerge from the discussion rather than preceding it.

- Knowledge and Understanding: Explain in plain language what current AI technologies can and cannot do, and why this matters strategically,

- Specialized Skills: Analyze a firm's value chain to locate where AI could create or capture value, using the codifiability of each activity as the criterion; evaluate and prioritize candidate AI projects; decide whether to build, buy, or partner for a capability and explain the consequences for the firm's boundaries,

- General Abilities: Analyze an unstructured business situation, identify the decision that must actually be made, and take a defensible position under incomplete information; argue persuasively in a demanding discussion environment,

- Attitudes: Approach AI not as a one-off tool but as an ongoing search for better solutions, and anticipate the consequences of AI adoption for the people inside an organization.

*本学の教育ミッションを具現化する形で設定されています。

By completing this course, students will gain,

- The ability to assess critically whether AI is the right solution to a business problem, rather than assuming it is,
- A method for analyzing a value chain to locate where AI can realistically create or capture value,
- A structured approach to evaluating and prioritizing candidate AI projects, and judging when to commit,
- The judgment to decide between building, buying, and partnering for an AI capability, and to see what each choice does to the firm's boundaries,
- The ability to redesign work between people and autonomous systems, and to anticipate the consequences for skill and careers,
- An understanding of why AI pilots succeed and scaling fails, and of what a firm must institutionalize to build durable capability,
- Practical experience constructing and defending a complete AI strategy for a real company, and communicating it to a non-technical audience.

教育手法 Teaching Method		% of Course Time
インプット型 Traditional		0 %
参加者中心型 Participant-Centered Learning	ケースメソッド Case Method	100 %
	フィールドメソッド Field Method	0 %
合計 Total		100 %

Pre- and Post-Course Learning, Report, and Feedback Methods

I. Prerequisites for the Course

Students are expected to dedicate 2 to 4 hours of preparation before each case.

Pre-Class Preparation
Every case is distributed in advance. No case materials are handed out during class. Where a case comes in several parts — (A), (B), (C) — all parts are assigned and read together as one package before the session.
Read the assigned case and any pre-class readings in advance, and come prepared to take a position and defend it.
Cold calls are standard and graded in this classroom.
Post-Class Review
Revisit the frameworks that emerged in discussion and apply them to a firm of your own choosing, in preparation for the final report.

Assignment questions are issued for every case in a separate assignment sheet. A laptop is required.

II. Final Report

Case to be used: all cases. Assignment: synthesize what you have learned across the cases discussed in class and build an AI strategy. Detailed instructions will be announced during the course. Individually written and individually graded; students may research and discuss together, but every submission is the student's own work. Submission Deadline: 11:00 pm on the final class day. Submission Method: via Google Classroom (within 20 A4 pages, Times New Roman 12pt, double-spaced, PDF).

III. Feedback

This course has no written preparatory report. Feedback is given in three ways. First, contribution is acknowledged and challenged in class as discussion proceeds — this is the primary feedback mechanism of the case method, and it is continuous. Second, each student receives an individual written comment on the quality of their class contribution at the midpoint of the term, so that there is time to act on it before the final grade. Third, the final report receives written comments through Google Classroom upon submission. No work is due after the course ends.

*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。

講師用内規準拠 Method of Assessment	Weights
コールドコール Cold Call	0 %
授業内での挙手発言 Class Contribution	70 %
クラス貢献度合計 Class Contribution Total	70 %
予習レポート Preparation Report	0 %
小テスト Quizzes / Tests	0 %
シミュレーション成績 Simulation	0 %
ケース試験 Case Exam	0 %
最終レポート Final Report	30 %
期末試験 Final Exam	0 %
参加者による相互評価 Peer Assessment	0 %
合計 Total	100 %

評価の留意事項　Notes on Evaluation Criteria

Students are expected to study the case assigned for each week.

By university policy, active class participation is a fundamental component of this course, accounting for 70% of the final grade. In assessing class participation, emphasis will be placed on the quality of contributions rather than mere frequency. Thoughtful engagement in discussions — demonstrating logical reasoning and deep insights — will be valued over simply sharing facts, paraphrasing, or summarizing previous points.

When contributing to discussions, students are expected to present well-structured arguments with clear, articulate points. Superficial or vague responses will not be considered substantive participation.

Cold calls are a standard and graded part of this classroom. Students who do not participate in discussion cannot learn by this method, and their grade will reflect that.

The final report is graded on the strength of original reasoning, not on summary. It is important to deliver your own thoughts and critical reasoning, built on the cases discussed in class.

Turning in the assignment with imperfect English is fine. Using generative AI to write your report is strictly prohibited; where I judge that a report has been written by AI, it will receive zero points.

All grading is individual. Students may research and discuss together, but every submission is the student's own work.

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