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Practical Strategies for Maximizing Development Team Productivity in the AI Era

I will help you redesign your organization and generate results based on my hands-on experience in addressing and solving the decline in development team productivity following AI adoption.

1 learners are taking this course

Level Intermediate

Course period Unlimited

Business Productivity
Business Productivity
leader
leader
scm
scm
team-build
team-build
data-transformation
data-transformation
Business Productivity
Business Productivity
leader
leader
scm
scm
team-build
team-build
data-transformation
data-transformation

What you will gain after the course

  • Diagnosing the causes of productivity decline in development organizations due to AI adoption and establishing practical solutions

  • Optimizing Development Processes through AI Utilization and Applying a Role Redefinition Framework

  • Acquiring practical know-how for establishing AI-based performance metrics and managing organizational change

Lecture Information

You've introduced AI tools to your team, but why have you become even busier?

You've installed Cursor and Copilot, but productivity hasn't increased as expected, and there are no standards for who should review AI-generated code or how. You're unsure what to look for in hiring interviews, and you're at a loss on how to reflect AI contributions during OKR season. Some team members are resisting the AI transition itself, and as a manager, you're struggling to hold everything together on your own.

This is not a problem of tools. It is because the organizational structure has not changed.

This course diagnoses the actual chaos occurring in development organizations following the introduction of AI. Across 10 modules, it delivers redesign methods that engineering managers can apply to their teams immediately—covering everything from redefining team structures and roles to hiring criteria, growth paths, performance metrics, and managing resistance to change.

We have included only the 26 years of field-proven principles from CEO Jintae Kim, who has direct experience as a Senior Researcher at Samsung Electronics, a professor at Sogang University and KAIST, and a startup CTO. This is not theory; it is practice.


Curriculum

Section 1. Diagnosis — AI Transformation, What is Happening Now? 2 Lectures · 45 min

Without facing reality, there can be no prescription. This section uses data and case studies to pinpoint the structural changes actually occurring in development organizations since the introduction of AI, and provides a dispassionate analysis of which roles within your team are expanding and which are shrinking.

  • M1. AI Before/After: What is happening in development organizations

  • M2. Roles that survive vs. roles that shrink in the AI era (Role Map Analysis)

Section 2. Design — How to Build a New Team Structure Lesson 2 · 45 min

If roles have changed, the team structure must change as well. We will compare three team models suited for Human-AI collaboration and establish criteria for determining the appropriate squad composition and size for your team.

  • M3. Comparison of 3 Human-AI Collaboration Team Structure Models

  • M4. Optimal team size and squad composition methods after AI adoption

Section 3. Talent — Who to Hire and How to Develop Them Lesson 2 · 40 min

The standards for a good developer have changed in the AI era. We cover three updated hiring criteria, practical interview questions, and how to redesign the growth paths for existing team members.

  • M5. Hiring Standards in the AI Era: What Should We Look for When Hiring?

  • M6. Redesigning the Developer Growth Path (Changes in the IC Track)

Section 4. Operations — How to Manage Goals and Performance Lesson 2 · 45 min

The moment you measure AI productivity by lines of code, the team falls apart. This session presents specific ways to design new performance metrics focused on judgment and how to operate OKRs suited for the AI era.

  • M7. Goal Setting for AI Organizations: OKR and Expectation-Based Operations

  • M8. Pitfalls of AI Productivity Measurement and Proper Performance Metrics

Section 5. Transition — How to Lead Change Beyond Resistance Lesson 2 · 45 min

Every change comes with resistance. I will share the psychological stages organizations go through during AI transformation, strategies for responding to different types of resistance, and my raw experience leading organizational transformation as a CTO.

  • M9. Managing Resistance to Change: Psychological Stages Organizations Experience During AI Transformation

  • M10. Real-world Case: Experience Leading an AI Transformation Organization as a CTO

Recommended for
these people

Who is this course right for?

  • Development team leaders and CTOs who are concerned about the decline in development team productivity after introducing AI

  • Engineering managers who must lead practical changes and performance in development organizations to fit the AI era

  • Tech lead who wants to establish a new AI-based productivity and performance measurement system

Need to know before starting?

  • At least 1 year of experience in managing a development team or technical organization

  • Basic understanding of software development processes and organizational operations

  • Development experience or interest in using AI tools (GitHub Copilot, ChatGPT, etc.)

Hello
This is reinvention

Career Verified

After obtaining a PhD in Computer Science from Sogang University, he designed mobile platform SW architecture and re-engineered dozens of legacy systems at Samsung Electronics. Subsequently, he served as an adjunct professor at KAIST and Sogang University, and since 2009, he has founded the Software Engineering Expert Group (SEEG) and has been active as its lead consultant.

I am also working as a CTO for startups and SMEs, with hands-on experience in everything from organizational design to product development. Currently, I regularly lecture at Samsung Electronics and Samsung Display, leading SW architect training programs, and have consulted for the SW organizations of dozens of companies over the past 16 years.

I am neither a scholar who only knows theory nor a practitioner with only field experience. Having gone through doctoral research, hands-on experience at Samsung Electronics, serving as a CTO for SMEs and startups, and 16 years of corporate consulting—I can quickly discern whether an organization's problem stems from its structure, its people, or its technology, regardless of the situation.

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Curriculum

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10 lectures ∙ (2hr 53min)

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