AI for software development workshop

The workshop begins by establishing a baseline understanding of how AI tools are used within the software development lifecycle. Your software development teams will learn about the current landscape of AI tools, we’ll evaluate their capabilities and limitations to assess AI for software development readiness. This foundational exercise allows Pythian to transfer knowledge, ensuring that every team member understands the "why" and "how" behind AI adoption.

This workshop is a hands-on learning session, not a lecture. Teams will work through real-world scenarios, using AI tools for practical tasks such as generating boilerplate code, debugging, and identifying bugs faster. Participants will also learn how to leverage AI for code reviews to spot potential issues and enforce best practices, as well as automate the creation of test cases and identify vulnerabilities.

The primary focus of this workshop is on achieving tangible business outcomes. Teams will learn how to use AI to increase efficiency and accelerate development cycles, improve code quality by building more reliable software, and foster innovation by freeing up developers to focus on complex, creative challenges. Finally, they'll learn how to mitigate risk by safely using AI with company data and avoiding security vulnerabilities.

AI-assisted coding, debugging, and testing can reduce project timelines by as much as 56%. Launch products to market faster, giving your business a crucial competitive advantage.

With AI handling repetitive, manual tasks, developers can complete more work in the same amount of time. This can lead to a significant increase in the number of features shipped and bugs fixed.

AI can automatically identify bugs and vulnerabilities earlier in the development process, reducing the amount of time spent on debugging and fixing issues post-release.

Your team can prototype new features faster, launch products sooner, and respond to market demands with agility. This means you can get ahead of competitors and capture market share before they do.

With AI handling the mundane parts of coding, developers are free to focus on more complex, creative, and innovative challenges. This leads to new ideas, better product features, and a more engaged workforce.

AI helps enforce coding standards and best practices, leading to cleaner, more maintainable code bases. This reduces technical debt and makes future development easier and more efficient.

By using AI to automate tasks like code generation and testing, you can minimize the risk of human error, which can lead to costly bugs and vulnerabilities.

AI tools can act as a powerful teaching aid, helping junior developers learn faster and close the skills gap with senior engineers. This allows you to scale your team more efficiently.

Training addresses the "shadow AI" problem, where developers use unvetted public tools. It educates them on the security risks of inputting proprietary code into unapproved AI models, protecting your intellectual property.