AI Software Development: How Teams Can Improve Productivity and Quality

If you're looking to boost your team's productivity and improve software quality, AI might be the key you’ve been missing. So, in this article, we’ll show you how integrating AI solutions into your software engineering processes can tackle bottlenecks, streamline workflows, and drive measurable results.

Ready to transform how your team works? Let’s get started.

What Is AI Software Development?

AI software development involves using artificial intelligence tools, models, and agents for designing, coding, testing, shipping, and operating software. The goal is to automate tasks, increase efficiency, and enhance product quality.

But it’s important to differentiate between using AI to build software and building AI-powered products like machine learning models or generative AI apps. The first focuses on how teams work and how software is developed, while the latter involves creating AI systems themselves.

As the industry moves toward AI integration, it's clear that developers are leveraging these tools more than ever. In fact, the 2025 Stack Overflow Developer Survey report notes that around 84% of developers report using or planning to use AI tools in their development process.

Basically, more and more engineering leaders try to integrate AI in strategies aimed to increase productivity and quality within development teams.

Check out this video to see how AI enhances every step of the software development process:

Key AI Technologies Transforming Software Development

AI software development relies on several key technologies that drive automation and improve productivity.

These include machine learning (ML) for predictive models, Large Language Models (LLMs) for natural language processing, and code generation tools like AI-driven assistants that suggest code in real time. For instance, code assistants may streamline the development process if you use them wisely, which reduces the time spent on repetitive tasks.

If you're interested in exploring how AI coding tools specifically impact developer workflows, check out our article on the best AI coding assistants. It dives deeper into how these tools can integrate into your existing processes, which can save you time and improve code quality.

Now, moving on, let’s talk about how AI can directly benefit software development.

Benefits of AI in Software Development

AI adoption brings several key benefits to the software development process, many of which were noted in the 2025 State of AI-Assisted Software Development report. These benefits include improving productivity, reducing manual work, and enhancing code quality.

Here are some of the key advantages that AI offers if your team already has solid engineering foundations and well-defined delivery processes in place:

• Faster coding: AI tools can speed up the development process by providing code suggestions in real-time. This may enable software developers to write more efficient code faster.

What to Read Next: Do AI Coding Assistants Really Save Time to Developers?

• Better developer experience and higher throughput: As AI assists with repetitive tasks, your developers can focus on more strategic work, which leads to higher output and smoother workflows. According to the 2025 State of AI-Assisted Software Development report, this integration drives significant developer productivity and increases team throughput.
• Reduced security vulnerabilities: AI can help identify potential security issues early in the development process. This makes it easier to address vulnerabilities before they affect the final product.
• Improved software testing with automation: AI accelerates the testing phase by automating many testing processes, which reduces human error and increases efficiency.
• Enhanced quality assurance: AI tools continuously monitor code quality and they offer suggestions for optimization and catching issues before they impact performance.
• Reduced friction and burnout: It automates repetitive tasks. So, AI reduces friction within teams and helps prevent burnout, which allows developers to focus on more valuable work.

Source: 2025 State of AI-Assisted Software Development

Now, let’s look at how AI integrates across the entire software development lifecycle.

How to Use AI Across the Software Development Lifecycle

Integrating AI into the software development lifecycle can streamline every stage from design to deployment (again, if your workflow is already solid). Here are the key areas where AI can make a significant impact.

Requirements and Discovery

AI can help accelerate the requirements and discovery phase by supporting the following use cases:

• Summarize customer feedback, tickets, and interviews: AI can process large volumes of input to identify key themes and insights.
• Generate draft user stories, acceptance criteria, and scenarios: AI tools can help create initial drafts for your team to refine.
• Highlight risks, dependencies, and edge cases: AI can quickly identify potential issues to enable early problem-solving.

Remember: Humans still make critical decisions like prioritization, trade-offs, and domain nuance. AI can assist in the process, but your expertise is important for understanding the specific needs and context.

In one study, 54.4% of AI adoption use cases involved human-AI collaboration (HAIC) rather than full automation. This shows the importance of human judgment in areas where AI can’t fully grasp the nuances.

The pitfalls to watch for include hallucinated requirements or over-generalization. AI-generated outputs may miss important details or make assumptions that don’t align with your specific project needs.

When you recognize these strengths and limitations, you can leverage AI to improve the requirements and discovery process while maintaining control over critical decisions.

Design and Architecture

AI tools can assist with design and architecture by providing support for key decisions:

• Suggesting architecture patterns and integration options: AI can analyze your current system and suggest optimal patterns based on industry standards.
• Producing sequence diagrams, data models, and API contracts: AI tools automate the creation of essential design documents to save time and ensure consistency.
• Comparing trade-offs: AI can help compare design choices like monolith vs microservices or sync vs async to offer data-backed insights.

However, it's important for you to validate factors such as cost, latency, security, and compliance. AI provides useful guidance, but the final judgment on these aspects remains in your hands.

Coding and Implementation

AI plays a significant role in the coding and implementation phase. As we explained above, it can help you boost efficiency and reduce time spent on repetitive tasks. That’s why, according to the 2025 State of AI Code Quality report, 65% of developers say AI touches at least a quarter of their codebase. 

Here are some practical uses of AI in this stage:

• Code completion and snippet generation: AI can assist by generating code snippets to reduce the time spent on boilerplate code.
• Boilerplate, config, and scaffolding: It automates the creation of repetitive components, which allows you to focus on more complex logic.
• Legacy code migration and refactoring: AI can aid in migrating or refactoring legacy code to make the process faster and more accurate.

However, from our experience, it’s crucial to know when to accept, edit, or reject AI-generated code. Using AI-generated suggestions may not always align with your project’s standards, so it’s important to review them carefully.

Hidden bugs, style drift, and security vulnerabilities are common limitations of AI tools that require your attention before incorporating the code into production.

Testing and QA

AI-driven tools can: 

• Improve your software testing and QA processes. They can automatically generate unit, integration, and end-to-end test cases based on your code to streamline the creation of test coverage.
• Function as virtual QA engineers. AI agents can help with exploratory testing and offering regression hints to ensure that changes don’t inadvertently break functionality. 
• Prioritize tests based on risk and change impact. The goal is to ensure that the most critical areas are tested first.

Despite these advancements, human oversight remains essential on this level as well. AI can struggle with flaky tests and may not fully capture the nuances of critical business flows.

So, you still need to validate that AI-generated tests align with real-world scenarios and are good enough to handle your system's complexities. AI can improve efficiency, but it's important to maintain control over key aspects of the testing process to prevent issues from slipping through the cracks.

Security, Compliance, and Code Quality

AI can help you maintain code quality, enhance security, and ensure compliance throughout development. It can assist with static analysis, dependency scanning, and secret detection. This can help identify vulnerabilities early in the process.

AI tools also support policy checks, such as validating licensing requirements. This can ensure data residency rules are followed and confirm adherence to coding standards.

However, while AI tools are powerful, they are not infallible. 

One study reported that 45% of AI-generated code contained security flaws, even when the models appeared to produce production-ready solutions. This shows the importance of manual review and security ownership.

So, you can’t fully outsource these responsibilities to AI. 

You need to identify subtle flaws, address complex compliance requirements, and ensure that security practices align with your organizational standards.

When you integrate AI into your workflow, you can speed up the detection of vulnerabilities and streamline compliance tasks. But maintaining a vigilant, human-driven review process is still important to safeguard your codebase and systems.

DevOps, CI/CD, and Operations

AI tools can optimize your DevOps and CI/CD pipelines by predicting potential build and deployment failures. With it, you can anticipate problems before they disrupt the workflow.

This provides actionable suggestions for roll-back/ roll-forward strategies that minimize downtime. Plus, AI can intelligently group incidents and propose fixes to help you address issues quickly without overwhelming your team with redundant alerts.

At the same time, AI supports capacity planning by analyzing usage patterns and recommending cost optimization strategies. It ensures that your infrastructure is right-sized, so you can avoid wasteful over-provisioning or underutilization that could impact performance and costs.

Just by integrating AI into your operations, you gain better visibility into your development and deployment processes. AI enables smoother and more predictable release cycles, which reduces the risk of delays and ensures that resources are allocated effectively.

This empowers your team to focus on critical tasks while AI takes care of the repetitive operational work.

Maintenance, Documentation, and Knowledge Sharing

AI can streamline maintenance and documentation by automating tasks that traditionally take a lot of time. It can auto-generate documentation from your code, APIs, and change history to keep everything up to date with minimal effort.

In fact, according to the 2025 Stack Overflow Developer Survey report, nearly a quarter (24.8%) of developers use AI for creating or maintaining documentation.

Source: 2025 Stack Overflow Developer Survey

AI-driven knowledge bots can also answer common "how does this service work?" queries. This can provide your team with instant access to essential information. That’s why over 54% of developers use AI to search for answers, according to the 2025 Stack Overflow Developer Survey mentioned above.

Additionally, AI suggests refactors, identifies deprecations, and points out cleanup tasks. It can help you maintain code quality and ensure that technical debt doesn’t build up over time.

Risks and Limitations of AI in Software Development

While AI offers tremendous benefits, there are significant risks and limitations in software development. As mentioned earlier, AI can amplify both strengths and weaknesses in your processes, and understanding this reality is important for effective integration.

Our team at Axify believes that these are some of the key risks:

• Hallucinations and subtle bugs: As we mentioned above, AI can generate outputs that seem correct but contain hidden issues, especially in edge cases. These bugs may not be immediately visible but can lead to failures down the line, and that can impact code quality.
• Security & IP risks: AI models may inadvertently expose sensitive code through prompts or create dependencies that are not secure, which poses security vulnerabilities.
• Bias and unfair behavior: AI models may reflect biases, particularly in user-facing features. In a 2025 DevOps report, 37% of tech leaders identified AI model bias as a top risk, which is why you need preventive strategies to mitigate AI bias in development.
• Over-reliance on AI and skill atrophy: Relying too heavily on AI can lead to skill degradation in your team, as your developers rely less on firsthand problem-solving and critical thinking.
• Cost creep: High-end models and extensive agent usage can drive up costs, which can impact your budget and ROI.
• Governance gaps: Defining ownership over AI algorithms, approvals, and policies is critical for maintaining control and ensuring compliance within your team.

Getting Started: How to Use AI in Software Development

Integrating AI into your development process requires a structured approach to ensure success. Here are the steps to get you on the right track:

• Stabilize your core workflows first: Make sure your delivery pipelines, review processes, and ownership boundaries are reasonably stable before introducing AI. AI performs best when it builds on consistent inputs and predictable workflows. When it’s used to compensate for broken processes, it surfaces your existing issues.
• Clarify goals and use cases: Define clear objectives such as faster feature delivery, better test coverage, or reducing on-call toil.
• Start small with one or two high-leverage workflows: Begin with AI tools like code completion or test generation for a single team to prove the value.
• Choose tools that fit your stack and risk profile: Select AI tools, such as IDE assistants, AI test tools, or AI doc generators, based on your team's needs and risk management strategies.
• Set guardrails and policies: Implement policies for data handling, IP rules, and AI-generated code approvals to maintain security and compliance.
• Train the team: Focus on prompt engineering. This means teaching your team how to write effective prompts, review AI output, and log feedback for continuous improvement.
• Define success metrics and a timeline: Commit to a 4-8 week pilot with clear KPIs to measure AI’s impact on productivity.
• Iterate and scale what works: Expand AI capabilities once proven effective by rolling them out to more teams for broader impact. While over 90% of enterprises have launched AI pilots, only about 5% have successfully scaled them enterprise-wide. This makes a measured, data-driven approach essential so you can focus on what works and replicate it across teams.

How to Measure the Impact of AI on Software Delivery

Measuring the impact of AI on software delivery is essential to ensure it’s driving tangible improvements. Here are the core delivery metrics you should track before and after implementing AI:

• Cycle time: Track how long it takes from code commit to production. AI can help identify bottlenecks.
• Lead time for changes: It measures how long it takes for a code change to move from commit to running in the production environment. According to the 2025 DORA Report, 9.4% of teams achieve lead time for changes in less than one hour.
• Deployment frequency: It measures how often code is deployed to production. The DORA 2025 Report reveals that 16.2% of teams deploy on demand.
• Change failure rate: It tracks the percentage of deployments that fail and require remediation. The DORA 2025 Report found that 8.5% of teams have a change failure rate between 0% and 2%.
• MTTR (Failed Deployment Recovery Time): It measures the average time it takes to restore service after a failure. The DORA 2025 Report shows that 21.3% of teams recover from failures in less than one hour.
• Defect rate and escaped bugs: AI can reduce defects by improving test coverage and bug detection.
• Review time and wait states: AI tools help speed up code reviews by suggesting improvements and identifying issues before human review.

In addition to these core metrics, there are AI-specific metrics to track:

• AI adoption/ usage: Monitor AI-assisted commits and suggestion acceptance rates to understand how AI is being integrated into daily workflows.
• Time saved per task or per engineer: Quantify the time AI tools save on tasks like code review or automated testing to provide a clear ROI.

Axify can centralize these signals through its AI Performance Comparison tool. This feature measures the real impact of AI adoption by comparing your team’s performance with and without AI tools. It clearly shows its impact on delivery time, DORA metrics, and other key indicators.

Maximize AI’s Potential in Your Software Development Process

Today, we’ve seen how AI tools can enhance developer productivity, improve code quality, and streamline CI/CD pipelines. When you measure key metrics like cycle time, deployment frequency, and others, you can assess AI’s true impact. But to ensure maximum benefit, start small, scale wisely, and keep your team’s workflow optimized.

If you're ready to take the next step, contact Axify to learn more on how to integrate AI into your development process and measure its impact effectively.

FAQs

Will AI replace software engineers?

No, AI will not replace software engineers. AI will automate repetitive tasks and assist with coding, but human expertise is still essential for software architecture, design decisions, and critical problem-solving.

What skills do developers need to stay relevant in an AI-driven SDLC?

To stay relevant, developers must understand how to guide AI tools, such as code review assistants or automated testing platforms. Skills in deep learning, prompt engineering, and evaluating AI-generated outputs will be critical for continued success.

How do I choose the right AI tools for my team?

Choose AI tools that align with your team's existing workflow and technology stack. Look for tools that integrate well with your CI/CD pipelines and can enhance code optimization or support pair programming for maximum impact.

Is it safe to paste proprietary code into AI assistants?

No, it's not always safe to paste proprietary code into AI assistants unless strict data privacy measures are in place. Many AI tools store prompts, which could expose sensitive data or intellectual property.

How long does it take to see results from AI in software development?

Typically, you can expect to see measurable improvements in developer productivity within 4-8 weeks. Metrics like deployment frequency and change failure rate should show the impact of AI tools in this timeframe.

How do I avoid AI from making our processes slower?

To avoid slowdowns, start small and focus on high-leverage tasks, such as code completion or test generation for a single team. Ensure that AI-generated outputs are always reviewed and validated to prevent errors that could slow your process.