MG213 Notes
Readings summaries 1
Reading 1- Bojinov, I. (2023). Keep Your AI Projects on Track, Harvard Business Review
101(6), 53-59.
The reading from Harvard Business Review (November-December 2023) focuses on AI's
impact on business strategies, AI project management, and the integration of AI into
corporate settings. It discusses the high failure rate of AI projects and offers guidance to
increase success, framed around five critical steps: selection, development, evaluation,
adoption, and management.
Key Points:
Selection: Companies should assess AI projects by considering internal- and external-facing
impacts, strategic alignment, and potential measurable outcomes. Projects need a clear
framework to measure success and should decide whether AI will augment or replace human
processes. AI works best in solving well-defined problems, but ethical concerns, such as bias
and privacy, must be addressed.
Development: AI projects go through a complex development phase, where data cleaning,
model building, and integration with existing systems occur. Standardization is vital to
streamline the development process. Advanced tools, like AI factories, enhance development
efficiency and model quality, ensuring the proper integration of AI into business processes.
Evaluation: Before wide-scale adoption, AI's performance should be evaluated using methods
like A/B testing and user feedback loops. It’s important to identify any performance drop due
to outdated training data, ensuring AI models are continuously retrained.
Adoption: Trust is crucial for AI adoption. AI products need to be user-friendly and reliable,
and developers must address user concerns about transparency and potential job replacement.
Engagement during development helps build trust and increases the likelihood of adoption.
Management: After adoption, ongoing support, monitoring, and improvement of AI models
are necessary. Ethical issues, performance issues, and system bugs need regular checks to
ensure continuous success.
The reading emphasizes that leaders must foster a digital mindset and ensure AI projects are
strategically aligned, continuously evaluated, and responsibly managed.
Reading 2- Leavitt, H. J., and Whisler, T. L. (1958). Management in the 1980's, Harvard
Business Review 36(6), 41-48.
,The 1958 Harvard Business Review article "Management in the 1980s" by Harold J. Leavitt
and Thomas L. Whisler introduced the term "information technology" (IT) and predicted how
it would transform management practices.
Key forecasts included:
Shift in Organizational Structures: IT would decentralize decision-making and alter
management hierarchies.
Managerial Training: Traditional apprenticeship models would diminish, with universities
playing a larger role in leadership development.
New Compensation Methods: IT would drive team-based bonuses and more precise
performance evaluations.
IT's Role: It would move beyond clerical tasks, integrating into strategic management
decisions.
Class 1 questions
1. In 2024, does technology encourage centralization or decentralization of
organizations?
Technology in 2024 often promotes decentralization by enabling remote work, global
collaboration, and the ability to access cloud-based data. However, some technologies, like
AI-driven decision-making and data analytics, can centralize control by concentrating critical
data and decision-making in central systems. The outcome depends on how organizations
deploy technology—whether they prioritize local autonomy or centralized oversight.
2. How are new technologies, including AI, changing the range of “think” jobs in
organizations?
AI is automating routine cognitive tasks, allowing workers to focus on higher-level strategic,
creative, and problem-solving roles. "Think" jobs now emphasize creativity, critical thinking,
and human intuition over repetitive analysis. AI also generates new roles in data science, AI
ethics, and algorithm management. The long-term societal effect on employment includes
potential job displacement in low-skill jobs but growth in specialized, tech-driven positions.
3. What lessons from IT management in the past remain relevant for managing AI
projects?
Strategic Alignment: Like IT, AI must align with business goals.
User-Centric Design: Successful AI integration requires constant feedback and user
engagement, similar to IT rollouts.
Ethical Considerations: The past management of data privacy and security in IT translates
directly to current concerns in AI regarding bias, transparency, and privacy.
4. Bojinov’s five critical steps for AI projects and Leavitt’s Diamond
, Selection: Aligns with Leavitt’s Task component (selecting the right processes to automate
with AI).
Development: Ties to Technology (AI tools for implementation).
Evaluation: Relevant to Structure (how AI fits into organizational frameworks).
Adoption: Reflects People (how employees embrace and use AI).
Management: Involves both Technology and People (ongoing AI support and employee
interaction).
Readings summaries 2
Reading 1- Markus, M. L. (2004). Technochange Management: Using IT to Drive
Organizational Change, Journal of Information Technology 19(1), 4-20.
The document "Technochange Management: Using IT to Drive Organizational Change" by
M. Lynne Markus outlines how using IT strategically to drive organizational change presents
both high risks and potential rewards, referred to as "technochange." Here are the key points
summarized from the reading:
Definition of Technochange: Technochange refers to technology-driven organizational
change, which differs from typical IT projects or traditional organizational change programs.
It requires a different approach due to the integration of both technology and organizational
shifts.
Challenges with Technochange: A major challenge in technochange is user adoption and
effective utilization of the new technology and work practices. IT project management
focuses on costs, schedules, and functionality, while organizational change management
emphasizes people and cultural readiness. Combining these approaches often leads to
incomplete or misaligned solutions.
Need for Complementary Changes: Successful technochange requires both IT and related
organizational changes, such as business process redesign, training, new performance
metrics, and sometimes job restructuring. IT alone is not sufficient; without these
complementary changes, the technology may fail to deliver the expected benefits.
Risks of Failure: The key risks in technochange include non-use of the technology, misuse,
and failure to capture the expected benefits. These risks arise from treating technochange
solely as an IT project or a traditional organizational change program without considering the
integration of both.
Lifecycle of Technochange: The lifecycle of technochange involves multiple phases:
chartering (approval and funding), the IT project (development), shakedown (implementation
and troubleshooting), and benefit capture (realization of business benefits). Problems often
Readings summaries 1
Reading 1- Bojinov, I. (2023). Keep Your AI Projects on Track, Harvard Business Review
101(6), 53-59.
The reading from Harvard Business Review (November-December 2023) focuses on AI's
impact on business strategies, AI project management, and the integration of AI into
corporate settings. It discusses the high failure rate of AI projects and offers guidance to
increase success, framed around five critical steps: selection, development, evaluation,
adoption, and management.
Key Points:
Selection: Companies should assess AI projects by considering internal- and external-facing
impacts, strategic alignment, and potential measurable outcomes. Projects need a clear
framework to measure success and should decide whether AI will augment or replace human
processes. AI works best in solving well-defined problems, but ethical concerns, such as bias
and privacy, must be addressed.
Development: AI projects go through a complex development phase, where data cleaning,
model building, and integration with existing systems occur. Standardization is vital to
streamline the development process. Advanced tools, like AI factories, enhance development
efficiency and model quality, ensuring the proper integration of AI into business processes.
Evaluation: Before wide-scale adoption, AI's performance should be evaluated using methods
like A/B testing and user feedback loops. It’s important to identify any performance drop due
to outdated training data, ensuring AI models are continuously retrained.
Adoption: Trust is crucial for AI adoption. AI products need to be user-friendly and reliable,
and developers must address user concerns about transparency and potential job replacement.
Engagement during development helps build trust and increases the likelihood of adoption.
Management: After adoption, ongoing support, monitoring, and improvement of AI models
are necessary. Ethical issues, performance issues, and system bugs need regular checks to
ensure continuous success.
The reading emphasizes that leaders must foster a digital mindset and ensure AI projects are
strategically aligned, continuously evaluated, and responsibly managed.
Reading 2- Leavitt, H. J., and Whisler, T. L. (1958). Management in the 1980's, Harvard
Business Review 36(6), 41-48.
,The 1958 Harvard Business Review article "Management in the 1980s" by Harold J. Leavitt
and Thomas L. Whisler introduced the term "information technology" (IT) and predicted how
it would transform management practices.
Key forecasts included:
Shift in Organizational Structures: IT would decentralize decision-making and alter
management hierarchies.
Managerial Training: Traditional apprenticeship models would diminish, with universities
playing a larger role in leadership development.
New Compensation Methods: IT would drive team-based bonuses and more precise
performance evaluations.
IT's Role: It would move beyond clerical tasks, integrating into strategic management
decisions.
Class 1 questions
1. In 2024, does technology encourage centralization or decentralization of
organizations?
Technology in 2024 often promotes decentralization by enabling remote work, global
collaboration, and the ability to access cloud-based data. However, some technologies, like
AI-driven decision-making and data analytics, can centralize control by concentrating critical
data and decision-making in central systems. The outcome depends on how organizations
deploy technology—whether they prioritize local autonomy or centralized oversight.
2. How are new technologies, including AI, changing the range of “think” jobs in
organizations?
AI is automating routine cognitive tasks, allowing workers to focus on higher-level strategic,
creative, and problem-solving roles. "Think" jobs now emphasize creativity, critical thinking,
and human intuition over repetitive analysis. AI also generates new roles in data science, AI
ethics, and algorithm management. The long-term societal effect on employment includes
potential job displacement in low-skill jobs but growth in specialized, tech-driven positions.
3. What lessons from IT management in the past remain relevant for managing AI
projects?
Strategic Alignment: Like IT, AI must align with business goals.
User-Centric Design: Successful AI integration requires constant feedback and user
engagement, similar to IT rollouts.
Ethical Considerations: The past management of data privacy and security in IT translates
directly to current concerns in AI regarding bias, transparency, and privacy.
4. Bojinov’s five critical steps for AI projects and Leavitt’s Diamond
, Selection: Aligns with Leavitt’s Task component (selecting the right processes to automate
with AI).
Development: Ties to Technology (AI tools for implementation).
Evaluation: Relevant to Structure (how AI fits into organizational frameworks).
Adoption: Reflects People (how employees embrace and use AI).
Management: Involves both Technology and People (ongoing AI support and employee
interaction).
Readings summaries 2
Reading 1- Markus, M. L. (2004). Technochange Management: Using IT to Drive
Organizational Change, Journal of Information Technology 19(1), 4-20.
The document "Technochange Management: Using IT to Drive Organizational Change" by
M. Lynne Markus outlines how using IT strategically to drive organizational change presents
both high risks and potential rewards, referred to as "technochange." Here are the key points
summarized from the reading:
Definition of Technochange: Technochange refers to technology-driven organizational
change, which differs from typical IT projects or traditional organizational change programs.
It requires a different approach due to the integration of both technology and organizational
shifts.
Challenges with Technochange: A major challenge in technochange is user adoption and
effective utilization of the new technology and work practices. IT project management
focuses on costs, schedules, and functionality, while organizational change management
emphasizes people and cultural readiness. Combining these approaches often leads to
incomplete or misaligned solutions.
Need for Complementary Changes: Successful technochange requires both IT and related
organizational changes, such as business process redesign, training, new performance
metrics, and sometimes job restructuring. IT alone is not sufficient; without these
complementary changes, the technology may fail to deliver the expected benefits.
Risks of Failure: The key risks in technochange include non-use of the technology, misuse,
and failure to capture the expected benefits. These risks arise from treating technochange
solely as an IT project or a traditional organizational change program without considering the
integration of both.
Lifecycle of Technochange: The lifecycle of technochange involves multiple phases:
chartering (approval and funding), the IT project (development), shakedown (implementation
and troubleshooting), and benefit capture (realization of business benefits). Problems often
