10 Key Ways Software Engineers Are Using AI

By Sarah King

Artificial intelligence (AI) is dramatically transforming nearly every industry, and software engineering is no exception. From automating repetitive tasks to solving complex problems, AI is transforming how software is designed, developed, and deployed. With advancements in machine learning, natural language processing, and predictive analytics, software engineers are leveraging AI to enhance efficiency, improve code quality, and create innovative applications.

The adoption of AI in software engineering is not just about automating workflows—it’s about empowering engineers to tackle challenges that were previously considered insurmountable. AI tools are enabling more accurate predictions, smarter algorithms, and enhanced collaboration between machines and humans. By integrating AI, software engineers are building systems that can learn, adapt, and evolve, making software more resilient and intelligent.

In this article, I explore 10 key ways software engineers are using AI to push the boundaries of what’s possible in technology, with research assistance from ChatGPT. These methods highlight how AI is not just a tool for efficiency but a catalyst for innovation and creativity in software development.

How Software Engineers Can Use AI

1. Code Generation and Completion

AI-powered tools like GitHub Copilot and Tabnine assist software engineers by generating code snippets and completing lines of code, significantly speeding up the development process.

• Why it’s valuable: Reduces the time spent on writing repetitive code.
• Popular tools: GitHub Copilot, Cursor, Tabnine, and Amazon CodeWhisperer.
• Contextual suggestions: AI analyzes existing code to offer relevant completions.
• Use cases: Ideal for boilerplate code, API integrations, and rapid prototyping.
• Limitations: Requires human oversight to ensure code accuracy and security.
• Impact: Enhances productivity and reduces cognitive load for developers.

2. Debugging and Error Detection

AI tools help software engineers identify and fix bugs by analyzing codebases and providing suggestions for resolving errors.

• Why it’s valuable: Speeds up the debugging process and reduces human error.
• Examples: DeepCode, Snyk, and SonarQube.
• How it works: AI scans for vulnerabilities, syntax errors, and logic flaws.
• Benefits: Improves code quality and ensures better security practices.
• Limitations: May miss edge cases or introduce false positives.
• Impact: Reduces time-to-resolution for critical bugs.

3. Predictive Analytics in Software Development

AI enables software engineers to use predictive analytics for forecasting project timelines, identifying potential risks, and allocating resources efficiently.

• Why it’s valuable: Increases the likelihood of project success by identifying risks early.
• Examples: Pluralsight Flow (formerly GitPrime), LinearB, Jellyfish, Azure DevOps Analytics (with custom ML models), IBM’s Engineering Insights (within the ELM Suite)
• Applications: Analyzing project data to predict project outcomes.
• Benefits: Helps in planning sprints, estimating costs, and managing deadlines.
• Limitations: Relies on high-quality historical data for accuracy.
• Impact: Enhances decision-making in software project management.

4. Automated Testing

AI-powered testing tools can automatically generate and execute test cases, ensuring that software meets quality standards without manual intervention.

• Why it’s valuable: Saves time and improves test coverage.
• Popular tools: Testim, Applitools, and Selenium AI integrations.
• Types of testing: Functional, regression, performance, and UI testing.
• Benefits: Reduces human error in test case creation and execution.
• Limitations: May require initial setup and training for custom scenarios.
• Impact: Accelerates release cycles while maintaining software quality.

5. Natural Language Processing (NLP) for Documentation

AI tools powered by NLP can automatically generate, update, and organize documentation, making it easier for teams to maintain clarity and consistency.

• Why it’s valuable: Reduces the manual effort required to write documentation.
• Popular tools: Glean, OpenAI’s GPT for summarizing.
• Applications: API documentation, user manuals, and internal knowledge bases.
• Benefits: Enhances communication and reduces misunderstandings.
• Limitations: May struggle with highly technical or niche content.
• Impact: Improves team productivity by providing clear and accessible documentation.

6. Intelligent DevOps Automation

AI is being integrated into DevOps workflows to automate deployment pipelines, monitor system performance, and predict potential failures.

• Why it’s valuable: Streamlines the software delivery lifecycle.
• Examples: Ansible AI, Harness, and IBM Watson AIOps.
• Applications: Continuous integration/continuous deployment (CI/CD), log analysis.
• Benefits: Reduces downtime and enhances scalability.
• Limitations: Initial setup and AI training can be resource-intensive.
• Impact: Increases the reliability and efficiency of software systems.

7. Enhanced Security with AI

AI-powered security tools analyze patterns to detect vulnerabilities and potential threats in real-time, helping engineers build more secure software.

• Why it’s valuable: Strengthens defenses against cyberattacks.
• Popular tools: Darktrace, CrowdStrike, and Palo Alto Networks AI integrations.
• How it works: AI identifies anomalies in user behavior or system activity.
• Benefits: Proactively mitigates risks before they escalate.
• Limitations: Requires regular updates to counter evolving threats.
• Impact: Enhances software security and protects sensitive data.

8. AI-Driven Design Prototyping

Software engineers use AI to create rapid prototypes of user interfaces, improving collaboration with designers and stakeholders.

• Why it’s valuable: Speeds up the design and feedback process.
• Popular tools: Figma with AI plugins, Adobe Sensei.
• Applications: Generating mockups and wireframes.
• Benefits: Helps teams visualize designs before coding begins.
• Limitations: Prototypes may lack the depth of manually crafted designs.
• Impact: Bridges the gap between design and development.

9. Legacy Code Modernization

AI tools assist engineers in refactoring or migrating legacy codebases to modern frameworks, ensuring they remain efficient and maintainable.

• Why it’s valuable: Extends the lifespan of existing software systems.
• Examples: CodeAI and IBM’s Application Modernization solutions.
• How it works: AI identifies outdated code patterns and suggests optimizations.
• Benefits: Reduces technical debt and improves performance.
• Limitations: Complex migrations may require additional manual effort.
• Impact: Makes legacy systems compatible with modern technologies.

10. AI-Enhanced Collaboration Tools

AI-powered collaboration tools help software teams communicate more effectively by summarizing discussions, identifying action items, and even mediating conflicting ideas.

• Why it’s valuable: Improves team productivity and alignment.
• Examples: Slack AI integrations, Microsoft Teams AI, and Notion AI.
• Applications: Meeting transcription, task assignment, and team analytics.
• Benefits: Reduces miscommunication and ensures accountability.
• Limitations: Privacy concerns may arise with data sharing.
• Impact: Fosters better teamwork in distributed and hybrid work environments.

The Role of AI in Software Engineering Continues to Evolve

AI has transformed software engineering into a field where innovation meets automation. From generating code to enhancing security, AI tools are enabling developers to focus on solving complex problems while automating routine tasks. These advancements not only improve efficiency but also open new possibilities for what software can achieve, pushing the boundaries of creativity and functionality.

As AI continues to evolve, its role in software engineering will only expand. By embracing these technologies, engineers can build smarter, faster, and more secure systems, shaping the future of software development. Whether you’re a seasoned developer or just starting out, leveraging AI is no longer optional—it’s a necessity for staying competitive in a rapidly changing industry.

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About the Author

Sarah King is a software and technology leader, entrepreneur, and AI innovator with over 15 years of experience, including 7 years managing high-performing engineering teams. She has leveraged artificial intelligence and machine learning to drive innovation across diverse verticals—ranging from autonomous vehicles and data platforms to EdTech and FinTech. She has a B.S. in Computer Sciences from Harvey Mudd College. Sarah has guided AI-driven engineering initiatives at leading organizations such as Netflix, Ripple, and Cruise Automation. Connect with her on LinkedIn for collaboration opportunities and insights into transformative AI solutions.