AI for Software Development
As AI becomes embedded across the development cycle, software professionals need the skills to use AI tools effectively and unlock their full potential. Our courses help developers, engineers and technical teams to build their AI expertise and gain the skills to boost productivity, enhance delivery and future-proof capability.
The best AI skills courses for software developers
Recent data from Accenture has shown that engineering and DevOps job adverts are on the rise, increasing by 11% compared to last year. This points to AI fundamentally shifting the roles and responsibility of developers, rather than replacing them.
With organisations across the globe building, testing and deploying AI products at scale, the needed for skilled developers is stronger than ever. Here are some of the best courses for developers that want to build their AI skillset and learn how to effectively put AI to work across the software development lifecycle.
AI Assisted Secure Software Development
Duration: 3 days
Who is it for?: Software developers, technical leads, DevOps engineers, security professionals and teams looking to adopt AI across the software development lifecycle.
This hands-on course explores how developers can use tools such as GitHub Copilot, Amazon Q and Tabnine to improve productivity, accelerate delivery and build smarter applications using AI.
Through practical labs, you'll learn how to apply prompt engineering techniques, develop AI-powered applications, deploy machine learning models, and understand the security risks and responsibilities that come with AI-assisted development. It's perfect for teams looking to build the skills needed to thrive in an AI-enabled software.
Certified Tester AI Testing (CT-AI) Certification
Duration: 3 days
Who is it for?: Software testers, QA professionals, test analysts, developers and anyone involved in testing or working with AI-powered systems.
As AI becomes more and more integrated into systems and solutions, testing them requires new skills and approaches. This course provides a practical introduction to artificial intelligence, machine learning and the unique challenges of assuring quality in systems that can learn, adapt and evolve over time.
You'll explore how to test AI-specific characteristics such as bias, non-deterministic behaviour and model drift, while gaining insight into techniques including adversarial testing, metamorphic testing and A/B testing. Ideal for testing professionals looking to build the skills needed to evaluate, validate and improve the reliability of AI-enabled systems.
Ensuring Code Quality and Security in AI-Assisted Software Engineering
Duration: 1 day
Who is it for?: Software developers, technical leads, DevOps engineers and teams using AI-assisted development tools.
AI coding tools can significantly accelerate software delivery. But they can also introduce governance and quality risks. This course helps developers understand the limitations of AI-generated code and apply the engineering practices needed to build reliable, secure and maintainable software.
Through practical exercises, you'll learn how to identify common issues such as hallucinated code, hidden vulnerabilities and missed edge cases, while strengthening outputs with testing, static analysis and secure coding techniques. Ideal for teams looking to adopt AI-assisted development with confidence. Without compromising on quality, compliance or security.
Implementing AI in Java and C#
Who is it for? Software engineers, front-end developers and full-stack developers looking to integrate AI into their development workflows.
These hands-on courses show developers how to use GitHub Copilot effectively within Java and C# environments, helping teams accelerate development, improve documentation and streamline everyday coding tasks.
Through practical exercises and real-world development scenarios, you'll learn how to incorporate AI into your workflow, write more effective prompts, and get the most value from AI-assisted development tools. Whether you're building applications in Java or .NET, these courses provide a practical introduction to applying AI in day-to-day software engineering.
Testing Generative AI Systems
Duration: 1 day
Who is it for?: QA engineers, test leads, developers, product managers and professionals responsible for deploying, governing or evaluating generative AI systems.
Testing generative AI requires a different approach to traditional software testing. As AI systems produce probabilistic and evolving outputs, organisations need new techniques to assess quality, reliability, safety and risk throughout the lifecycle of an AI-powered product.
This practical course introduces proven frameworks for testing and evaluating generative AI systems, including red teaming, risk assessment and non-deterministic testing techniques. You'll learn how to identify failures, design effective testing strategies and build confidence in AI-enabled products before and after deployment.
GitHub Copilot for Developers
Duration: 1 day
Who is it for? Experienced developers looking to boost productivity and get more value from AI-assisted development tools.
GitHub Copilot is transforming how developers write, review and maintain code. This hands-on course shows you how to use Copilot effectively within Visual Studio Code, helping you accelerate development, improve code quality and streamline everyday engineering tasks.
You'll learn how to use Copilot for code generation, analysis, documentation, refactoring, unit testing and application development, while exploring emerging techniques such as vibe coding. Ideal for developers who want to integrate AI into their workflow and work more efficiently without compromising quality.
Build the AI capability to shape the future of software development
Whether you're getting started with AI-assisted development, integrating AI into applications, strengthening software quality and security, or testing AI-powered systems, our AI software development courses provide hands-on, practical training designed for modern software teams.
Related articles
AI is shifting the role of software engineers from writing code to defining intent, evaluating outcomes, and making sound engineering decisions. As AI takes on more implementation work, human judgement, accountability, and architectural thinking become even more valuable.
The AI-Enabled Software Engineer. the evolution of the role, not its replacement.
15 October 2026
Effective ethical hacking relies on understanding systems before attempting to exploit them. This article explores why enumeration remains a core cyber security skill, how AI is changing the way ethical hackers work, and why human judgement remains essential for identifying genuine risks and improving organisational security.
Inside the mind of an ethical hacker
14 October 2026
AI is transforming cyber attacks, enabling criminals to create highly convincing phishing emails and scale social engineering efforts faster than ever before. This article explores why human judgement remains one of the strongest defences against cyber threats, and why organisations must update training and processes to prepare employees for an AI-driven threat landscape.
The human factor in AI-driven cyber security: turning awareness into stronger defence
8 October 2026
New research reveals that 39% of employees have received no training on AI-powered cyber threats in the last 12 months, despite growing concern about the sophistication and scale of modern attacks.
New survey reveals 39% of employees have received no training against AI-powered cyber threats
2 October 2026
Daniel Ives explores how AI may be creating a new era of cloud computing, transforming the cloud from a software platform into an intelligence platform.
Cloud 3.0: why AI is creating a new era of cloud computing
2 October 2026
Can AI really be slowed down? The challenge is not the technology itself, but getting competing nations to agree.
AI Slowdown? Only if the world agrees, and how to keep up if it doesn’t
16 September 2026
Browse all AI-enabled software courses
AI for developers FAQs
What is AI-enabled software development?
AI-enabled software engineering involves integrating AI tools and technologies throughout the software development lifecycle. This can include AI-assisted coding, automated testing, intelligent code reviews, documentation generation and the development of AI-powered applications.
Why do software developers need AI skills?
AI is becoming a core part of the software development lifecycle, with organisations increasingly adopting AI-powered tools and workflows. Developing AI skills helps software professionals improve productivity, build AI-enabled applications and stay competitive as technology and job roles evolve.
What AI tools are commonly used by software developers?
Popular AI development tools include GitHub Copilot, Amazon Q, ChatGPT, Claude and other AI coding assistants. These tools can support tasks such as code generation, debugging, testing, documentation and application development.
Can AI replace software developers?
AI can automate certain development tasks, but it does not replace the need for skilled software professionals. Developers remain responsible for software design, architecture, security, quality assurance and ensuring AI-generated outputs are accurate, secure and fit for purpose.
How can GitHub Copilot improve developer productivity?
GitHub Copilot can help developers generate code, create documentation, suggest solutions and automate routine coding tasks. Used effectively, it can reduce development time and allow teams to focus on higher-value engineering work.
Which AI course is right for software developers?
The right course depends on your goals. Developers looking to improve productivity may benefit from AI-assisted development and GitHub Copilot training, while teams adopting AI at scale may focus on AI-enabled software engineering, secure AI development or AI governance.
Can I develop AI software development skills through an apprenticeship?
Apprenticeships like the Software Engineer Level 4 and Junior Develper 3 can help developers to build practical AI, software engineering and development skills while applying learning in the workplace. They can be an effective way for organisations to develop AI capability at scale and prepare teams for the future of software development.