How much faster can AI actually make your team?

As a leader, you’ve probably asked yourself: “With AI becoming better at coding, how much time could my team realistically save? Could we speed up development by three or even ten times?” Let’s explore this thoughtfully, understanding exactly where AI adds value and where human intuition remains important.

The insights shared here are based purely on my personal experience, assuming an ideal scenario where engineers effectively leverage AI tools. It’s not intended as a new standard or expectation to place on your team but rather as a reference to highlight potential areas for improvement.

First, what exactly does a Software Engineer do?

Here’s a simple breakdown of the software development process:

1. Understanding the Problem (15%)
   Talking to people, asking questions, and clearly defining the problem.
2. Choosing the Right Solution (20%)
   Considering current systems, trade-offs, and deciding the best solution that fits with the current context.
3. Planning the Work (10%)
   Writing clear, actionable implementation plans and system design documents.
4. Writing Code (30%)
   Writing code that’s clean, readable, and easy to maintain.
5. Testing and Fixing Bugs (15%)
   Making sure everything works as expected and quickly fixing issues.
6. Deployment and Monitoring (10%)
   Launching the solution safely and closely monitoring its performance.

Where can AI really help?

Let’s look at each part again, this time seeing how AI fits in:

• Choosing the Right Solution (20%):
  Human judgment and intuition still matter greatly. AI can suggest options, but final decisions rely heavily on human context.
• Planning the Work (10%):
  AI can streamline repetitive planning tasks, such as automatically creating and organizing tasks directly in Jira, generating an initial timeline for the project, breaking down larger projects into actionable subtasks based on the implementation plan, and even rapidly drafting initial documentation to align the team quickly. This potentially reduces planning time by about 40%.
• Writing Code (30%):
  AI tools like GitHub Copilot, ChatGPT, or Cursor AI can greatly accelerate the coding process, potentially saving around 50% of coding time. However, engineers remain essential because human judgment is necessary to thoroughly review the AI-generated code, ensuring it’s correct, readable, maintainable, and scalable for long-term use.
• Testing & Bug Fixing (15%):
  AI-driven testing tools can automate roughly 50% of testing tasks. For example, AI can generate detailed test cases directly from API documentation, automatically create comprehensive test suites, and even simulate user interactions. This significantly reduces the time spent manually writing tests and speeds up finding and resolving issues.
• Deployment & Monitoring (10%):
  AI can significantly enhance deployment and monitoring processes by automating routine tasks like log analysis, anomaly detection, and performance tracking. For example, AI-driven monitoring tools can predict potential issues before they become critical, alerting teams proactively. This potentially improves efficiency by about 30%.

Realistically, how much time can AI save?

Here’s an estimated breakdown, based purely on my own experiences, assuming engineers really know how to use AI effectively:

• Planning: 40% of 10% = 4%
• Coding: 50% of 30% = 15%
• Testing & Fixing Bugs: 50% of 15% = 7.5%
• Deployment & Monitoring: 30% of 10% = 3%

Total time saved with AI is around 29.5%.

This 29.5% represents an ideal scenario based on personal experience. It’s not a new baseline you should set for your team, just a useful reference to see potential areas of improvement.

It’s also important to understand that just because AI can help code, it doesn’t mean delivery times will automatically triple or become ten times faster.

Gaining roughly 30% more productivity is fantastic, but it’s important to keep things balanced:

• Use AI for tasks that are repetitive or predictable.
• Rely on human judgment for important decisions.
• Encourage your team to develop good problem-solving skills and intuition.

AI can significantly boost productivity, but thoughtful, skilled engineers remain essential. Think of AI as a powerful assistant rather than a complete solution.

Ultimately, successful software development isn’t about choosing between AI and human expertise; it’s about harmoniously blending both to create something exceptional. Embrace AI’s strengths and nurture human creativity, and you’ll unlock the best of both worlds.

I’m interested in your perspective. How much time could your team save by using AI? How are you finding the right balance between AI and human skills in your team?