シラバス Syllabus

授業名 Disruption by Big Data/Artificial Intelligence
Course Title Disruption by Big Data/Artificial Intelligence
担当教員 Instructor Name Giulio Toscani
コード Couse Code NUC445_N23B
授業形態 Class Type 講義 Regular course
授業形式 Class Format On Campus
単位 Credits 2
言語 Language EN
科目区分 Course Category 専門教育科目 / Specialized Subject
学位 Degree BBA
開講情報 Terms / Location 2023 UG Nisshin Term4

授業の概要 Course Overview

Misson Statementとの関係性 / Connection to our Mission Statement

This course aims to equip aspiring and established leaders from various industries with the knowledge and skills to leverage Big Data and AI effectively, gaining a competitive advantage over their competitors. Participants will learn how to harness their data and employ cutting-edge technologies to establish stronger connections with their customers. By applying the principles of Disruptive innovation methodology, they will not only analyze data and utilize technology but also comprehend and expand their capabilities, seamlessly integrating them into their organization's strategic framework. Ultimately, this course empowers participants to become adept leaders who not only understand data and technology but also leverage them strategically to drive their organizations towards success.

授業の目的(意義) / Importance of this course

This course is instrumental because it will prepare leaders to harness the power of disruptive innovation before their competitors do. Participants will gain a deeper understanding of what disruptive innovation is and will learn how to spot potential threats and opportunities in their own business.

到達目標 / Achievement Goal


By studying this course, students will be able to:
Understand the forces of disruption that could impact their business and see how to spot them early
Uncover a new approach for customer centricity to spot and shape new opportunities by applying the jobs to be done framework
Learn, through practical examples, how to leverage new business models and potential application for their companies
Get hands-on experience using the disruptive innovation toolkit

本授業の該当ラーニングゴール Learning Goals

*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。

LG1 Critical Thinking
LG4 Effective Communication
LG5 Business Perspectives (BSc)
LG6 Managerial Perspectives (BBA)
LG7 International Perspectives (BA)

受講後得られる具体的スキルや知識 Learning Outcomes


Understand the Role of Big Data and AI: Participants will gain a comprehensive understanding of how Big Data and AI technologies can be harnessed strategically to gain a significant competitive edge over competitors.
Explore Cutting-Edge Technologies: Participants will be exposed to the latest advancements in technology, enabling them to identify and leverage innovative tools that enhance customer connections and overall business performance.
Enhance Customer Understanding: By applying Disruptive innovation methodology, learners will be able to develop a deeper understanding of customer expectations, leading to more tailored products and services.
Become Dynamic and Strategic Leaders: Upon completion of the course, participants will have the skills and knowledge to go beyond traditional data analysis and technology utilization, positioning themselves as forward-thinking leaders who can drive their organizations towards success in a rapidly evolving business landscape.

SDGsとの関連性 Relevance to Sustainable Development Goals

Goal 4 質の高い教育をみんなに(Quality Education)

教育手法 Teaching Method

教育手法 Teaching Method % of Course Time
インプット型 Traditional 50 %
参加者中心型 Participant-Centered Learning ケースメソッド Case Method 30 %
フィールドメソッド Field Method 20 %
合計 Total 100 %

事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods

Course Approach: Case and workshop methodology, with a final report delivery.
Report: Individual case exam
Feedback: Written, on final report
Required amount of preparation: there will be 7 cases and a final individual report. The cases require at least 2 hours each, Minimum 14 hours.The final report is approximately 10 hours
No prerequisite Knowledge/Experience

授業スケジュール Course Schedule

第1日(Day1)

Introduction to Disruption and Big Data AI
In this session, we will explore the concept of disruption and its impact on various industries. We will delve into the fundamentals of Big Data AI and how it is revolutionizing traditional business models. You will gain insights into the potential of data-driven innovation and understand the key components of a disruptive ecosystem.

Disruptive Innovation Workshop


●使用するケース
Case 1 ByteDance Beyond China:Leveraging Consumer Artificial Intelligence (AI) from Toutiao to Musical.ly and TikTok 819-0040-1

第2日(Day2)

Understanding Big Data and AI Technologies
This session will provide an in-depth understanding of Big Data and AI technologies. We will cover the principles of data collection, storage, and processing at scale, as well as the core concepts and algorithms of artificial intelligence. You will learn about the interplay between Big Data and AI and how they enable disruptive applications and solutions.



●使用するケース
Case 2 Mcdonald’s can a behemoth lead in the era of artificial intelligence?

第3日(Day3)

Leveraging Big Data for Disruption In this session, we will explore how organizations can leverage Big Data to drive disruption in their respective industries. We will discuss real-world examples of companies that have successfully utilized Big Data to transform their business models and gain a competitive edge. You will also discover various data-driven strategies and techniques for identifying disruptive opportunities.



●使用するケース
Case 3 Sensetime group limited: business model and expansion

第4日(Day4)

Unleashing the Power of AI for Disruption
Building upon the previous session, we will focus on the transformative potential of AI in driving disruption. We will examine the role of machine learning, deep learning, and other AI techniques in enabling groundbreaking applications. Through case studies, you will understand how AI-powered solutions are reshaping industries and creating new market dynamics.



●使用するケース
Case 4 Julio Weiss: An NFT Opportunity ASN-98-E https://www.iesepublishing.com/julio-wais-an-nft-opportunity-english.html

第5日(Day5)

Overcoming Challenges in Disruptive Data-driven Innovation Disruption through Big Data AI is not without its challenges. In this session, we will discuss the obstacles and risks associated with data-driven innovation. We will address issues such as data privacy, ethical considerations, and regulatory frameworks. You will gain insights into best practices for mitigating risks and navigating the complex landscape of disruptive technologies.



●使用するケース
Case 5 Kathy Fish at Procter & Gamble: Navigating Industry Disruption by Disrupting from Within

第6日(Day6)

Design Thinking and Disruption Design thinking plays a crucial role in the process of disruption. In this session, we will explore the principles of design thinking and its application in driving data-driven innovation. You will learn how to adopt a user-centric approach to identify unmet needs, prototype disruptive solutions, and create meaningful experiences for customers in the digital era.






●使用するケース
Case 6 Rock Pharmacy: Rocked? W25507-PDF-ENG

第7日(Day7)

Embracing Disruption: Strategies for Success

In the final session, we will delve into strategies for embracing disruption and capitalizing on the opportunities presented by Big Data AI. We will explore organizational agility, talent acquisition, and cultural transformation as key drivers for success in disruptive environments. You will leave with actionable insights and a roadmap for harnessing the power of data-driven innovation to shape the future of your industry.


●使用するケース
Case 7 Choosy by Jeffrey J. Bussgang and Julia Kelley

成績評価方法 Evaluation Criteria

*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 40 %
クラス貢献度合計 Class Contribution Total 40 %
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 60 %
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

Assessment reflects the quality of a student’s active participation in class discussions. Much of a manager’s success depends on communication; therefore, effective oral communication will constitute the student’s grade. Written work should be clear, logical, grammatically correct, spell-checked, persuasive, supported by examples, and backed up by citations for any data, ideas or other content used. It should represent the student’s best effort. To do well on the writing reports, you will need to incorporate and apply the course readings.

A note on Class participation:
Grading class participation is necessarily subjective. However, I try to make it as “objective as possible”. Some of the criteria for evaluating effective class participation include:

1 Is the participant prepared? Do comments show evidence of analysis of the case? Do comments add to our understanding of the situation? Does the participant go beyond simple repetition of case facts without analysis and conclusions? Do comments show an understanding of theories, concepts, and analytical devices presented in class lectures or reading materials?
2 Is the participant a good listener? Are the points made relevant to the discussion? Are they linked to the comments of others? Is the participant willing to interact with other class members?
3 Is the participant an effective communicator? Are concepts presented in a concise and convincing way?


Class Participation 40%

This course covers a significant amount of content and much of the learning comes from in-class exercises and discussion. Therefore, students are expected to attend all class sessions, complete all assigned readings and come prepared and ready to participate. Attendance will be taken and participation will be evaluated at each class session. Participation in all on-campus sessions in their entirety is mandatory, and students may not be late or leave early for any of these sessions. Failure to be in attendance for the entirety of the session will result in removal from the class.

Individual final project and cases 60%
The Individual final project is meant to be a culmination of all the learnings in the class, that is reinforced by the cases; to do well on this report it is imperative to draw upon the readings and discussions in presenting your analysis.

Please kindly explain in your report the solution you propose for the problem and deeper need you have identified.
• Write a word document (with no upper or lower word-count limits) where you demonstrate what you have learnt in class, by answering in details, giving at least an example for each questions, all the points below.

1. What is the problem? (Distinguish problem from pain points)
2. Who has the problem? (Define your user by age, gender, social status, what type or product or service is purchasing/receiving, the level of satisfaction, the pain points, the frequency of use…)
3. What is your proposed solution? (state clearly in a sentence and repeat it at the end of the report A [label] that allows [users] to [benefit] by [method])
4. How did you reach this solution? (Explain in detail your disruptive innovation process across the 4 dimension of expansion, reductio…)
5. What is the value of solving that problem with your solution? (Show why this solution has value, why it is useful to that user, based on the need/deeper need you identified by observing/thinking of the user)
6. What is the technology you propose for your solution? Why? (Define what technical solution you have found, by searching what is available in the market)
7. What are the human skills required to solve the problem? Why? (Define what human skills and/or training are necessary, by searching what could be the technology limitations that need to be compensated by the human)
8. What is your most important learning for this course? (What is different now, compared to before you started the course)


Please take into account that I reward critical thinking and specifically
• RELEVANCE is the solution addressing the right problem?
• COHERENCE how well does the solution fit?
• EFFECTIVENESS is the solution achieving its objectives?
• EFFICIENCY how well are resources being used?
• IMPACT what difference does the solution make?
• SUSTAINABILITY will the benefits last?

So, jot down what questions you have asked yourself, to reach the conclusions.
Do your best to show that you have been critical, so what you propose makes sense from a business, technology and ethical/legal point of view.

Try not to just propose a solution that is neither impossible to produce, advertise and monetise, nor propose to use technology as a magic wand.

Think instead of a problem first, without considering technology at this stage.

Think of a possible ecosystem, a possible alliance, a different business model or a new model for revenues (the analogic vs. digital toothbrush example), the data you need to have and why these data.

Then think how technology could help you, by looking at what is available on the market.

I do not expect you to propose a detailed technical solution, but, yes a business sound solution using a technology that may have been used by someone else and you could use too.

Remember to list briefly how you are going to integrate the solution, to overcome skepticism from investors and solution users. Just show that you understand the final goal of using these technologies to solve your problem. And of course, explain what are the business and ethical implications.

The assessment criteria are:

Coherence: the smooth and logical flow of writing

Consistency: the uniformity of style and content

Originality: the ability to think independently and creatively

Accuracy: explaining in detail what is your solution about

使用ケース一覧 List of Cases

    ケースは使用しません。

教科書 Textbook

  • Williams, L. 「Disrupt: Think the unthinkable to spark transformation in your business. 」FT press(2015)

参考文献・資料 Additional Readings and Resource

How Incumbents Survive and Thrive By: Julian Birkinshaw
Persuade Your Company to Change Before It's Too Late By: Pontus M.A. Siren; Scott D. Anthony; Utsav Bhatt
Adapting to Digital Disruption By: Julian Birkinshaw; Thomas W. Malnight; Ivy Buche; Pontus M.A. Siren; Scott D. Anthony; Utsav Bhatt; Jonathan Knee; Alison Beard
Can Big Tech Be Disrupted? By: Jonathan Knee; Alison Beard

授業調査に対するコメント Comment on Course Evaluation

Previous comments on this course have addressed a further need to provide more examples and less cases, The more examples have been implemented

担当教員のプロフィール About the Instructor 


An expert on the subjects of Leadership and Disruptive Innovation by Big Data and Artificial Intelligence, Prof. Toscani caters to the board of companies, academic and not-for-profit institutions, like CCL (Belgium), SDA Bocconi (Italy), Telefonica (Spain, Argentina, Brazil, Guatemala), Deppon Logistics (China), Navozyme (Singapore), Nike and Megafon (Russia), Parexcelence (India).
Prof. Toscani started his career as a patent attorney for chemical plants in Lugano, Switzerland. He also has an MBA, and a PhD in Management from the Royal Institute of Technology, Stockholm, and is involved in research projects on Machine Learning and CEO mindset with IESE Business School. He has also published in leading publications like Marketing Intelligence and Planning, Journal of Non-Profit and Public Sector Marketing etc.
In addition to holding the extensive management and teaching experience, Prof. Toscani also presided over the Global Management Programme at Universitas Telefonica, co-managed and taught with IESE Business School. He is currently the Academic Director and professor for the Programme in Artificial Intelligence for Business Strategy in ESADE Business School, Barcelona and Madrid, Spain.
His vast experience on Big Data/AI springs from his work on location-system behavioural analysis and time perception project at Telefonica. He has also participated in the programme on AI for business at the Massachusetts Institute of Technology, and is currently setting a start-up delivering Artificial Intelligence services.
He plays the side flute in a classical music orchestra, is a yoga teacher, a vipassana meditator and an ultra-trail runner with personal record of 120 km. Based out of Barcelona, he has worked, visited or lived in over 100 countries, most recently having cycled solo halfway across the Black Sea and Caucasus, published in his youtube channel.

(実務経験 Work experience)


Professor and Advisor
• ESADE Business and Law School, Barcelona, Spain. Adjunct Professor
Law School degree: Digital Technologies Impact
MIBA (Master in Business Analytics): Human + Machine strategy
Executive education: Remote teams; Digital Entrepreneurship; Leadership in AI.
• Pacifico Business School, Lima, Perú. Adjunct Professor
• University of Bath, Bath, UK. Visiting Professor. MBA Programme: Contemporary issues at the time of Big Data/ Artificial Intelligence
• NUCB Nagoya University of Commerce and Business, Nagoya, Japan. Visiting Professor. MBA programme: Disruption by Big Data|Artificial Intelligence
• Ranepa Business School, Moscow, Russia. Visiting Professor. Global MBA: Digital Entrepreneurship
• Politecnico, Milan, Italy. Visiting Professor. Master in Strategic Design: Design Thinking in AI
• Navozyme, Singapore. Advisory Board.
• Programme Director Universitas Telefónica. Barcelona, Spain.
Direction and Teaching of the Programmes for Telefonica Global Executives

Refereed Articles

  • (2023) The effects of the COVID-19 pandemic for AI practitioners: the decrease in tacit knowledge sharing. Journal of Knowledge Management
  • (2023) Cómo el trabajo en remoto está reduciendo el conocimiento implícito. Harvard Deusto Business Review
  • (2022) Los mundos virtuales, un nuevo reto para la propiedad industrial de las marcas. Harvard Deusto Business Review
  • (2022) The role of reciprocity and reputation in service relationships with Arts organisations. Journal of Services Marketing
  • (2018) Arts Sponsorship Versus Sports Sponsorship: Which Is Better for Marketing Strategy?. Journal of Nonprofit & Public Sector Marketing






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