The course examines technology strategy through seven critical lenses:

I. The Innovation Imperative (how organizations identify and commercialize breakthrough technologies),

II. The Ecosystem Advantage (how platforms and partnerships create sustainable moats),

III. The Emerging & Transformative Technologies Revolution

(comprehensive exploration of AI, biotech, quantum, climate tech, blockchain, and their convergence),

IV. Value Creation vs. Value Capture ( Identifying use cases & outcomes)

V. Constraints: Regulation & more (how compliance shapes competitive advantage),

VI. Economics of Technology Strategy (quantifying impact)

VII. The Responsible Future (how leaders navigate ethical dilemmas and societal impact).

This course is distinguished by its practitioner-led approach, emphasizing ecosystem thinking, treating regulatory compliance as a strategic competency rather than constraint.

Students are equipped to operate at the executive and board level through perspectives shared by featured guest speakers from enterprises, startups, scale-ups, regulators, investors, and policy makers.

Course Objectives

By successfully completing this course, students will be able to:

● Develop strategic frameworks for evaluating emerging technologies (e.g. Artificial Intelligence) and their business implications

● Design ecosystem strategies that leverage network effects, platform dynamics, and partnership models for sustainable growth

● Analyze real-world case studies from fintech, healthtech, deep tech, and other high-growth sectors using established industry frameworks

● Synthesize practitioner insights from diverse guest speakers representing multiple stakeholder perspectives

● Create actionable recommendations for organizations facing technology disruption and transformation challenges

● Navigate regulatory complexity as a competitive advantage,

understanding compliance, risk management, and policy engagement strategies

● Balance innovation velocity with responsible governance, ethics, and societal impact considerations

● Communicate technology strategy decisions to executive and board level audiences

Discussion Forums

Discussion forums are an excellent way for students to engage with the course material and with peers. Any discussion forums for this course will be accessible through the Discussions tab in the  NYU Brightspace course site.

Virtual Meetings

In addition to weekly in-person class meetings, the  professor may choose to hold virtual meetings through the Zoom tool on NYU Brightspace, e.g. office hours.

Netiquette

When engaging with others in NYU Brightspace, it is important to interact in an appropriate manner. Always use professional language (no netspeak) in   discussion board posts. Please always be respectful of  classmates even if you disagree with their ideas.

Readings

Required Textbook

Schilling, M. (2020). Strategic Management of Technological Innovation.

McGraw Hill/Irwin.

Required Articles and Cases

All required readings will be available through NYU Brightspace, including:

●   Cook, S., Hagiu, A., & Wright, J. (2024). "Turn Generative AI from an Existential Threat into a Competitive Advantage." Harvard Business Review.

●   Chesbrough, H. (2012). "Why Companies Should Have Open Business Models." MIT Sloan Management Review.

●   Greve, H. R., Rowley, T.J., & Shipilov, A.V. (2013). " How Partners Shape Strategy." Harvard Business Review.

●   McDonald, R. & Krach, K. (2020). " How Would-Be Category Kings Become Commoners." MIT Sloan Management Review.

●   Nagle, F. (2018). "Capturing Value From Free Digital Goods." MIT Sloan Management Review.

●   Sako, M. (2012). " Business Models for Strategy and Innovation." Communications of the ACM.

●   Mims, C. (2024). "What I Got Wrong in a Decade of Predicting the Future of Tech." Wall Street Journal.

Recommended Books

●   Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and Prediction: The Disruptive Economics ofArtificial Intelligence.

●   Chen, A. (2021). The Cold Start Problem: How to Start and Scale Network Effects.

●   Kearns, M. & Roth, A. (2019). The Ethical Algorithm: The Science of Socially Aware Algorithm Design.

● Mollick, E. (2024). Co-Intelligence: Living and Working with AI.

● McAfee, A. & Brynjolfsson, E. (2017). Machine, Platform, Crowd:

Harnessing Our Digital Future.

NYU’s library can be accessed here: http:/library.nyu.edu/

Course Requirements

Participation is paramount to  success in this course. Please log into NYU

Brightspace multiple times a week, read all announcements and course

content, complete all assignments on time, and complete weekly readings.

I. Class Participation & Engagement - Ongoing

15% of Total Grade

The participation grade reflects the frequency and quality of contributions to class discussions, case analyses, and guest speaker sessions.

Students are expected to:

●   Complete all readings before class and come prepared with insights and questions

●   Actively participate in case discussions using analytical frameworks from the course

●   Engage respectfully and thoughtfully with guest speakers (prepare questions in advance)

●   Contribute to peer learning through shared insights, constructive feedback and professional experiences

●   Attend all classes (excessive absences will negatively impact  grade Quality participation includes:

Articulating clear arguments, drawing on course frameworks, connecting concepts across sessions, using data and examples to support points, asking provocative questions, and demonstrating intellectual curiosity.

Students should expect to be called upon during class.

For excused absences, notify the instructor in advance. Missed and unexcused classes will result in a class participation penalty.

II. Applied Assignments

30% of Total Grade

● Two assignments to be completed independently by each student.

Each requires the application of specific course concepts to case study.

Grading is based on the depth and quality of the analysis and/or strategy;  the ability to connect disparate course concepts into a reasoned narrative, and how external tools were used ethically and responsibly, with the final output serving as supporting evidence.

III. Group Projects (Team-Based)

30% of Total Grade

●   Three Projects (10% each): Group analysis, strategy  & presentation with three graded components:

○ Quality and depth of analysis (65%).

○ Process Portfolio (10%): Documentation of how the team

collaborated, including meeting notes, decision rationales,

division of work, evidence of iteration, and how external tools were used ethically and responsibly, presented through a Team

Charter.

○   Presentation Delivery (25%): Focused on clarity, teamwork, and ability to respond to spontaneous questions from peers, faculty and guests.

Grading is based on transparency of the collaborative process and the team's ability to demonstrate real-time, unscripted critical thinking, ethical reasoning, strategic alignment and connecting concepts to practice.

IV. Final Project

25% of Total Grade

● Scenario-Based Exercise:

○   Supervised, timed, in-class assignment to be completed individually.

○   This reflective assignment requires students to take a holistic perspective on the concepts explored throughout the course, applying them thoughtfully to specific scenarios, from the vantage point of a technology strategist.

○   Rather than focusing on the “ right answer,” students will demonstrate how they approach complex business,

technological, ethical and regulatory challenges, integrate multiple skills, tools and frameworks to design solutions to enterprise-level dilemmas.

Grading is based on the depth and quality of the analysis and strategy,  the ability to connect disparate course concepts into a reasoned narrative, and how external tools were used ethically and responsibly, with the final output serving as supporting evidence.