Engineering ManagersvsComputer Programmers

until most of the change has landed

High adoption

until most of the change has landed

Very High adoption

AI accelerates design, code and documentation across engineering teams, changing what managers plan, review and staff for.

AI profoundly augmenting coding, debugging, and testing, shifting focus to design and complex problem-solving.

1. AI coding and design assistants
2. Office copilots for management work
3. Generative design and simulation optimisation
4. Quality and governance demands
5. High sector adoption
6. Low observed usage by managers

1. Advancements in Large Language Models (LLMs) for Code
2. Demand for Faster Software Development Cycles
3. Increasing Complexity of Software Systems
4. Need to Manage and Understand Large Existing Codebases
5. Shortage of Skilled Software Engineers (AI as a force multiplier)
6. Growth of Open Source & Availability of Training Data for AI
7. Pressure for Improved Code Quality and Fewer Bugs
8. Integration of AI into Developer Tools & IDEs
9. Desire for Increased Developer Productivity
10. Automation of Repetitive Coding Tasks

1. Technical judgement
2. Quality assurance and review design
3. People development
4. AI tool governance
5. Stakeholder communication
6. Outcome-based measurement

1. Proficiency with AI Coding Assistants
2. Strong Problem-Solving & Algorithmic Thinking
3. System Design & Architecture
4. Code Review & Quality Assurance (for AI-generated code)
5. Debugging & Testing (including AI-assisted)
6. Knowledge of Specific Programming Languages & Frameworks
7. Understanding of Software Security Principles
8. Prompt Engineering for Code

1. GitHub Copilot
2. Jira
3. Microsoft 365 Copilot
4. Autodesk Fusion
5. Ansys

1. GitHub Copilot
2. Amazon CodeWhisperer
3. Tabnine
4. Cursor (AI-first code editor)
5. Snyk / SonarQube (with AI for security/quality)