AI is reshaping the way you design, develop, and deploy software. Whether you're debugging code, automating repetitive tasks, or ensuring security compliance, AI is here to make your work faster and more efficient — without sacrificing control.
In fact, it can speed up software development by up to 55%, freeing you from tedious tasks and allowing you to focus on innovation.
But what does this mean for your day-to-day work? How can AI truly enhance the way you build software?
Let’s explore the most impactful AI use cases for developers, common challenges, and best practices for integrating AI into your workflow.
Role of AI in software development
AI isn’t here to replace you — it’s here to boost your productivity. By handling tedious tasks like code optimization, testing, and security scanning, AI frees up your time to focus on strategic and creative work.
Will AI replace developers?
The short answer? No. While AI can enhance productivity, it lacks creativity, critical thinking, and problem-solving skills.
In fact, 70% of developers say AI tools improve their productivity, allowing them to focus on high-level decision-making rather than repetitive coding tasks.
So, rather than replacing developers, AI acts as an assistant — helping teams code faster, troubleshoot issues proactively, and streamline the development process. By offloading tedious and repetitive tasks to AI, you can spend more time on strategic work, like designing scalable architectures and optimizing user experience.
That’s why AI-driven tools are becoming essential for modern development teams, significantly reduce development time by helping you:
- Automate repetitive coding tasks
- Improve code quality with AI-assisted reviews
- Enhance testing and debugging with predictive analytics
- Streamline documentation and knowledge management
- Optimize project planning and resource allocation
And the best part? AI works within the tools you already use — so instead of switching between platforms, you can simply ask AI for help in a chat-like conversation and get instant answers.
Key AI use cases for developers
AI is changing the software development lifecycle (SDLC), which is a structured process that is used to design, develop, and maintain software. Some of these use cases were inspired by our recent webinar on how engineering teams accelerate productivity with AI agents.
Here’s where AI is making the biggest impact:
AI-powered coding assistance
If you've ever used AI-driven coding assistants, you know how they can:
- Suggest code snippets and auto-completions to speed up development
- Conduct automated code reviews to help enforce best practices
- Detect and resolve bugs before they escalate
- Optimize and refactor code, making it cleaner and more efficient
AI-powered assistants also help developers quickly track code changes, enhancing version control and collaboration. Instead of spending hours manually reviewing pull requests, you can rely on AI to spot inconsistencies in seconds and help adhere to standards automatically.
AI for testing and debugging
Testing and debugging can slow down development — but AI helps you move faster. With AI, you can:
- Automatically generate test cases based on past patterns
- Identify security vulnerabilities before they become threats
- Use historical data for predictive debugging, spotting issues early
AI-powered search agents can also scan logs, detect patterns, and recommend fixes — so you spend less time hunting down bugs and more time shipping great code.
AI for code documentation and knowledge management
Writing documentation isn’t exactly fun — but AI can take care of that for you. AI-powered tools can:
- Auto-generate function and API documentation based on code changes
- Summarize large codebases into digestible explanations
- Transcribe and organize developer discussions, making knowledge easier to access
AI can even act as an internal knowledge assistant, helping you quickly retrieve information from scattered documentation, past tickets, and knowledge bases.
AI in UI/UX design and prototyping
AI can accelerate the design and prototyping process by:
- Suggesting wireframes and layouts based on best practices
- Conducting automated accessibility checks to ensure compliance
- Predicting user behavior based on historical data
With AI-powered search and automation, engineers can surface critical design patterns and ensure usability best practices are met without manually combing through resources.
AI for project planning and resource allocation
Managing development timelines and team workloads is complex — AI makes it easier.
AI-powered project management can:
- Predict project deadlines and bottlenecks based on historical data
- Automate backlog prioritization by analyzing issue severity
- Highlight high-risk areas that need immediate attention
AI tools can also analyze past sprint cycles and recommend ways to optimize team efficiency.
AI-driven scheduling tools can even optimize meeting times, analyzing team availability in real-time.
AI-powered research and user story generation
Struggling with defining user requirements? AI can help you uncover key insights faster. It can:
- Generate user stories based on product goals and customer feedback.
- Analyze support tickets and surveys to identify common pain points.
- Predict feature impact, helping you prioritize development efforts.
Instead of manually sifting through feedback, AI can process large data sets and surface actionable insights—so you can make informed product decisions faster.
AI for security and compliance in development
Security is a top priority — and AI can help you stay ahead of threats. AI-powered security tools can:
- Run automated security scans to detect vulnerabilities
- Perform risk assessments based on coding patterns
- Identify anomalies in code, flagging potential threats
AI can also help enforce compliance policies, helping to ensure that sensitive data isn’t exposed through insecure API calls or misconfigurations.
For an in-depth look at AI-powered engineering productivity, watch the exclusive webinar.
Challenges and risks of AI in software development
AI is powerful, but it’s not perfect. Here are some key risks to keep in mind:
- AI-generated code can contain security risks: Without proper validation, AI-generated code can introduce vulnerabilities.
- How to overcome: AI-powered automation tools must have built-in guardrails to ensure compliance with security best practices and prevent risky deployments.
- Data privacy and compliance risks: AI models can inadvertently expose sensitive information.
- How to overcome: AI governance policies should include automated checks to prevent data leakage and ensure compliance with regulations like GDPR and CCPA. Implement AI models for internal codebases.
- AI can perpetuate biases: AI models learn from existing code, which means they can replicate existing biases. Human oversight is critical to ensure ethical AI adoption.
- How to overcome: Developers must fine-tune AI models to detect and mitigate biases while maintaining fairness in automation and decision-making processes.
Best practices for implementing AI in development
To get the most out of AI while minimizing risks, follow these best practices:
- Use AI as an assistant, not a replacement – Keep developers in the loop.
- Establish AI governance and security policies – Prevent data leaks and security risks.
- Validate AI-generated code – Ensure it meets best practices before deployment.
- Choose AI tools that complement your workflow – Balance automation with human expertise.
- Monitor AI-driven changes – Regularly review AI-generated outputs for accuracy and compliance.
What is agentic AI and why it’s a game changer for developers
AI-powered tools have already transformed the way developers write, test, and deploy code — but the next evolution is agentic AI.
Unlike traditional non-agentic AI, which only responds to individual prompts (like auto-suggesting code snippets or answering specific questions), agentic AI can take initiative. It can understand goals, tracks progress across tasks, and autonomously executes multi-step workflows — all while keeping you in control.
Why agentic AI is more powerful for developers
Traditional AI tools — like code autocompletion or basic automation — enhance productivity, but they rely on constant guidance from developers.
Agentic AI takes this further by acting like a collaborative team member that works alongside you, proactively handling tasks and anticipating what comes next.
Here’s what makes agentic AI a step change for developers:
- Understands context: Agentic AI is able to track your full development workflow, not just isolated commands. It can know where you are in a project, what your goals are, and what needs to happen next.
- Takes initiative: Instead of waiting for you to ask for help, it is able to actively identify issues, suggest optimizations, and even execute fixes when appropriate — while following your team’s coding standards and best practices.
- Executes multi-step workflows: Agentic AI can string together multiple actions, like generating test cases, running tests, analyzing results, and automatically opening tickets for failed cases — saving hours of manual coordination.
- Learns and adapts: Agentic AI improves over time, learning from your team’s codebase, preferences, and patterns to offer increasingly relevant suggestions and better automation.
How developers benefit from agentic AI
By offloading complex, repetitive workflows to agentic AI, developers gain back valuable time for creative problem-solving, architectural decisions, and innovation.
Here’s how agentic AI transforms development workflows:
- Proactive bug fixes: Agentic AI doesn’t just flag issues — it proposes solutions, applies fixes, and verifies correctness through automated tests.
- Self-optimizing systems: It continuously monitors performance and automatically suggests (or applies) optimizations based on real-time data.
- Hands-free CI/CD management: Agentic AI can autonomously manage deployments, rollbacks, and performance monitoring — ensuring smooth releases without human babysitting.
- Security-first development: Instead of static code scanning, agentic AI actively looks for vulnerabilities throughout development, automatically applying patches and enforcing compliance rules.
- Smarter DevOps automation: By integrating with your infrastructure and observability tools, agentic AI can predict failures, dynamically allocate resources, and troubleshoot incidents — all before they disrupt your users.
Rather than just assisting developers reactively, agentic AI works alongside you as a proactive collaborator, helping teams ship faster, build more resilient code, and spend less time on repetitive maintenance.
As AI capabilities continue to evolve, agentic AI will become an essential co-pilot for every modern development team — accelerating delivery while maintaining high quality and security.
Accelerate development with enterprise AI
AI is a critical tool for modern engineering teams. By integrating AI-driven automation, real-time insights, and enterprise AI search, developers can speed up your work, reduce bottlenecks, and make smarter decisions faster.
With AI-powered tools like Moveworks, your engineering team can:
- Find critical information instantly: No more digging through documentation or past tickets. AI-powered enterprise search surfaces the right answers in seconds, keeping developers focused on coding.
- Resolve issues faster: AI-powered troubleshooting helps identify patterns in system logs, suggest fixes, and even automate routine debugging tasks.
- Improve security and compliance: AI automatically detects vulnerabilities, enforces best practices, and ensures compliance with security policies — before code is deployed.
- Optimize project planning: AI helps predict delays, identify blockers, and ensure that engineering resources are used efficiently.
- Streamline DevOps and IT requests: AI-powered automation eliminates friction in IT support, infrastructure management, and incident resolution.
Want to see how engineering teams are using AI to boost productivity? Watch our on-demand webinar on 10 Ways Engineering Use AI Agents to discover how you can work smarter with AI agents — from speeding up debugging to automating project planning and beyond.
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