Lets imagine scenario where AI replaces the role of a Engineering Manager, the transition could look like this:
Initial meeting and requirement gathering: A virtual meeting is set up with the involvement of the lead engineers, QA team, product designer, and the product manager. The AI, acting as an intelligent assistant, joins the meeting to understand the requirements and record relevant information.
Requirement analysis and clarification: The AI assistant reviews the product requirements and proactively clarifies any ambiguous points by asking relevant questions to the team members.
Risk assessment and roadblock identification: The AI assistant analyzes the requirements and identifies potential roadblocks, risks, and dependencies.
Effort estimation and resource allocation: The AI assistant uses historical data to estimate the effort required for each task. It then recommends a resource allocation plan, taking into account the skills and availability of the team members.
Task creation and prioritization: The AI assistant generates a list of tasks with detailed descriptions and breaks them down into smaller, manageable sub-tasks. It then assigns priorities to each task based on factors like dependencies, business value, and risk.
Project planning and timeline creation: The AI assistant creates a project plan, including a detailed timeline with milestones and deadlines. This plan is shared with the team for review and adjustments as needed.
Monitoring progress and adjusting scope: During the feature development, the AI assistant monitors the team's progress by analyzing data from various tools like version control systems, issue trackers, and time tracking systems(?). It can detect deviations from the plan and suggest corrective actions, such as re-allocating resources or adjusting the scope.
Communication and reporting: The AI assistant ensures that all team members are kept informed about the project's progress and any changes to the plan. It can generate regular status reports and share them with stakeholders, highlighting any potential risks or issues that need attention.
Post-release analysis and improvement: Once the feature is released, the AI assistant gathers feedback from users, team members, and other stakeholders. It analyzes this feedback and suggests improvements for future projects, such as process optimizations or technology updates.
In this scenario, the AI assistant effectively takes on the responsibilities of a Engineering Manager, allowing the team to focus on their core competencies while ensuring efficient project execution.
Top comments (2)
Ehi, great article dude!
Do you have some software to suggest for the points in the list?
Browsing the web I saw a lot of apps that already cover some of the things you mentioned, it could be nice to have a list with this kind of classification..
I can only suggest check out my game about how to work in top software corporation like Google, Facebook or Apple