AI in Engineering Management: Unlocking New Levels of Productivity

In today’s fast-paced digital world, AI-driven tools are revolutionizing how we approach tasks in software development, communication, and data management. From GitHub Copilot to Microsoft Copilot and custom solutions like ChatGPT wrappers, these tools are proving to be invaluable for both individual and team productivity. Here’s how I’ve been using them in various aspects of my work.

GitHub Copilot: Your Code Companion

GitHub Copilot has become an essential tool in my development workflow, making tasks like writing and reviewing code faster and more efficient. Its seamless integration with Visual Studio Code and other IDEs simplifies the coding process, offering suggestions for syntax, code comments, and unit tests. What’s more, it helps me comprehend code written by others, enabling smoother collaboration.

Even in programming languages I’m not familiar with, Copilot has been a tremendous help. I’ve used it to write scripts in new programming languages, guiding me through unfamiliar syntax and logic. Recently, I’ve also been leveraging it to suggest code improvements when reviewing pull requests and crafting concise descriptions for my own pull requests.

GitHub Copilot’s code review functionality (available in paid plans and currently in public review at the time of writing) is another game-changer. It streamlines the review process by suggesting code improvements, enhancing overall code quality.

AI-Powered Code Review: The AI Code Reviewer

Another tool I’ve found useful is the AI Code Reviewer GitHub Action, which automates parts of the pull request review process. This action retrieves the diff from pull requests, filters out excluded files, and sends code chunks to the OpenAI API to generate review comments. While the AI-generated comments can sometimes be noisy, using the exclusion feature effectively helps reduce this noise, making the review process more efficient.

Microsoft Copilot: Boosting Productivity Beyond Code

Microsoft Copilot is another powerful tool that has significantly improved my productivity, particularly in areas such as email management, presentations, and data analysis.

Emails

As an engineering manager, I deal with numerous emails daily, often navigating long threads and crafting responses on a variety of topics. Microsoft Copilot helps by summarizing lengthy emails and threads, allowing me to quickly gauge their urgency. When drafting emails, Copilot can generate templates to kickstart my thought process or offer suggestions to refine the content, tailoring it to suit the intended audience.

Presentations

Creating impactful presentations is essential, and Microsoft Copilot has made this process much easier. I’ve used it to generate creative PowerPoint slides, where it suggests image options based on prompts and offers various design templates under the Designer tab.

Excel

In Excel, Copilot is invaluable for quickly generating formulas for complex queries. This functionality has saved me a lot of time and effort, allowing me to focus on higher-level tasks.

Teams Meeting Transcripts

In the era of remote work, not everyone can attend every meeting. Copilot has made it easier to catch up on missed meetings by summarizing meeting transcripts, making it simple to follow up on key points and action items without watching the full recording.

ChatGPT Wrapper: Enhancing Efficiency and Privacy

At my company, we’ve implemented an in-house wrapper for ChatGPT to ensure the privacy and security of proprietary data. As a heavy user, I’ve found it incredibly useful for a variety of day-to-day tasks.

Performance Assessments

For performance assessments, I’ve created custom prompts that help streamline both self-assessments and manager evaluations. My self-assessment prompt takes my notes from the year and applies the company’s performance criteria to generate clear highlights of my work, including examples of impact and leadership.

For the manager’s assessment, I use a similar prompt that incorporates 360-degree feedback and self-assessment reports. It helps generate a comprehensive summary of an employee’s achievements and areas for growth.

Understanding System Failures and Errors

While GitHub Copilot is excellent for diagnosing code-related issues, I’ve also used ChatGPT to troubleshoot system errors, environment failures, and problems related to virtual machines, CI/CD pipelines, and software installations. This flexibility makes it an indispensable tool across various domains.

LinkedIn Posts

Professional social media presence matters. Thanks to LinkedIn, we can share insights that leave a lasting impression on both our network and the organization we represent. I’ve been using ChatGPT to help refine my LinkedIn posts, ensuring they’re clear, concise, and business-appropriate. The feedback has been overwhelmingly positive, validating the value of this AI-driven approach.

Conclusion: Embracing the Future with AI Tools

AI-powered tools like GitHub Copilot, Microsoft Copilot, and ChatGPT are changing the way we work. From improving code quality to optimizing productivity in meetings, emails, and data analysis, these tools are proving to be indispensable in today’s fast-paced, data-driven world. By leveraging their capabilities, I’ve been able to work smarter, not harder, ultimately driving better results in my day-to-day tasks.

 

4 responses to “AI in Engineering Management: Unlocking New Levels of Productivity”

  1.  Avatar
    Anonymous

    Informative!

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  2. Mohsin Hussain Avatar
    Mohsin Hussain

    Loved it!

    Like

  3. Ash Nehmet Avatar

    With regards to performance evaluation. Could you elaborate on how exactly this works?

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    1. Ahsan Iqbal Avatar

      Hi, in my case, there are two stages during end of year performance review where ChatGPT helps. First is self assessment. All my reports maintain notes about their work contributions throughout the year. My ChatGPT prompt takes those notes as input. Additionally it takes definition of performance pillars (defined by my company) as input i.e. impact, leadership, and learnings. Then the prompt generates output summarising self assessment notes into impact, leadership, and learning highlights. For example: The prompt will turn a note like “I noticed a process gap in our release pipeline. I did X to fix the process. The release process improved by Y hours”. ChatGPT prompt would rephrase it into a leadership highlight like “Person X demonstrated ownership by identifying issue in release pipeline and fixing it which improved process efficiency by Y hours”. My reports praised the prompt because it saved them hours of time in writing an effective self assessment.

      Second stage where I use a similar prompt is manager’s assessment for each report. Here the inputs include self assessment of report, company definition of performance pillars, and 360 degree feedback received for a report including manager’s feedback. The output includes impact, leadership, and learning highlights of the report. Additionally, the prompt outputs areas of improvement for further development and growth of the report.

      I hope it answered your question, please feel free to ask further questions, if any.

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