AI in software development: How to use it, benefits, and key trends

Software development teams are under pressure to ship faster, debug smarter, and stay relevant, as using AI in software development is no longer optional: 84% of developers are already using or planning to use AI tools in their workflow, with 51% relying on them daily.

Discover how teams are using generative AI for software development, the real benefits it delivers, and the key trends shaping the future of coding.

How AI is changing software development

Artificial intelligence in software development is transforming how we plan, build, and maintain software. 

Instead of spending hours writing boilerplate code or testing for bugs, developers are now using AI tools like GitHub Copilot and Tabnine to automate repetitive work, predict issues early, and improve code quality. 

This leads to faster releases, fewer errors, and more time for creative problem-solving.

At a broader level, AI is also reshaping how we measure success. Software engineering metrics like cycle time and deployment frequency — once focused purely on speed and output — are evolving to track collaboration, efficiency, and the impact of AI-assisted workflows. 

In short, AI is empowering developers to work smarter, backed by data and intelligent automation.

Benefits of AI in software development 

The benefits of AI in software development go far beyond simple automation. By integrating AI-assisted software development into everyday workflows, you can code faster, make smarter decisions, and spend more time on innovation instead of maintenance. 

AI helps you work more efficiently and confidently by handling:

• Repeated tasks: AI can automate testing, debugging, and documentation so you can focus on higher-value work.
• First drafts of code: With AI, you can generate boilerplate or framework code in seconds to speed up development.
• Small code updates: AI can suggest quick fixes or minor improvements directly in your IDE.
• Reducing cycle times: AI helps you identify bottlenecks and streamline release pipelines with predictive insights.
• Data-driven coaching opportunities: You can use AI analytics to surface performance trends and guide developers toward continuous improvement.

Examples of AI in action 

AI-assisted software development is already reshaping how we design, test, and optimize our products. 

It’s no longer about developing AI software for the sake of novelty — it’s about embedding AI for development into everyday workflows to improve speed, accuracy, and collaboration. 

And the results are measurable, often reflected in stronger software development KPIs like improved cycle time or higher code review throughput.

AI is driving change through:

• AI-assisted code generation: Tools like GitHub Copilot and Replit Ghostwriter can draft complex functions or boilerplate code within seconds, giving developers a massive productivity boost.
• Automated testing pipelines: AI-powered tools predict high-risk areas in codebases, enabling smarter test coverage and fewer regressions.
• Review pattern detection: Intelligent systems identify recurring review bottlenecks or missed quality checks across teams.
• Bottleneck identification and mitigation: AI analyzes workflow data to flag inefficiencies, reducing delays in development and deployment.

Even large enterprises are seeing tangible gains. ANZ Bank ran a six-week study using GitHub Copilot with nearly 1,000 engineers, and the results were hard to ignore. 

Productivity went up, job satisfaction improved, and developers reported feeling more confident tackling complex tasks. It’s proof that when used intentionally, AI doesn’t replace human skill but supercharges it.

“The transformation is not about AI replacing engineers. It is about engineers expanding what they can tackle. I’m seeing frontend developers confidently solve backend tickets because AI helps them navigate unfamiliar territory... Think of AI as a brilliant junior engineer. It is really good at coding, but it needs you to provide the intent, the context, and the why. The engineers drive the value. AI amplifies what they already know.”

— Surya Mereddy, Software Engineering Manager at Appfire

Key metrics to follow when implementing AI in software development

Tracking the right metrics helps you see whether AI is truly improving how you work or just adding noise.

“We used Flow to measure our transformation, tracking standard engineering metrics: commit frequency, time to merge, review response time, and impact scores. The insight comes from watching patterns, not just numbers... Do not just measure productivity. Measure whether your engineers are learning and adapting.”

— Surya Mereddy, Software Engineering Manager at Appfire

In other words, the value of AI isn’t in raw output — it’s in how it changes developer behavior over time. The following metrics, when paired with a solid code review checklist, offer clear visibility into that shift.

Review responsiveness

AI tools can help surface relevant feedback faster, cutting down on the time between pull requests (PRs) and approvals. Tracking how quickly reviews are opened, commented on, and merged helps you understand how generative AI in software development is influencing collaboration.

• What it tells you: How efficiently reviewers are engaging with code changes and whether feedback loops are tightening
• Why it matters: Shorter review times often mean tighter team alignment and fewer blockers in the delivery cycle.
• If it’s lagging: Slow response times may suggest reviewers are overwhelmed or unclear on expectations. Review automation or clearer ownership could help.

PR size and frequency

AI often encourages smaller, more frequent pull requests by generating or suggesting focused snippets of code. This reduces risk and improves traceability.

• What it tells you: How AI is helping you break down large tasks into manageable chunks
• Why it matters: Frequent, smaller PRs lead to faster merges and lower defect rates — key indicators of improved efficiency through AI in software engineering.
• If it’s lagging: Oversized or infrequent PRs can signal a need for better task scoping or coaching on how to use AI-generated code effectively.

Review depth and feedback volume 

AI can handle routine checks, freeing you to focus on deeper insights and strategic feedback. Monitoring feedback volume and comment depth shows whether this balance is working.

• What it tells you: The quality and relevance of review discussions
• Why it matters: Meaningful feedback drives better code quality and team learning — something AI can’t replace but can amplify.
• If it’s lagging: Low comment volume may indicate that reviewers are overly reliant on AI suggestions or skipping peer collaboration.

Contribution spread

As AI tools lower barriers, more engineers, including those outside traditional roles, can contribute meaningfully. Tracking contribution spread reveals whether that’s actually happening.

• What it tells you: How evenly work is distributed across your team
• Why it matters: Balanced contribution shows that AI is enabling — not concentrating — productivity among a few individuals.
• If it’s lagging: A narrow contribution spread may suggest uneven AI adoption or knowledge gaps in how to apply it.

Cycle time consistency

Cycle time, or how long it takes for a change to move from commit to production, is a crucial indicator of team health. AI-driven automation can reduce variability and boost predictability.

• What it tells you: The steadiness of your development process and the impact of AI-assisted automation
• Why it matters: Consistent cycle times mean your AI integrations are helping, not hindering, the flow of delivery.
• If it’s lagging: Fluctuations might point to process bottlenecks, uneven AI usage, or overreliance on manual interventions.

“The biggest challenge is not technical. It is cultural. I learned this the hard way when I lost six hours of work to AI hallucination because I was not committing regularly... AI will confidently give wrong answers, so you need verification. Vibe coding without discipline is a recipe for pain... [AI] is not magic. It is only as good as the engineer using it.”

Surya Mereddy, Software Engineering Manager at Appfire

In short, AI doesn’t replace good engineering — it raises the bar for it. Clear processes, consistent reviews, and code review best practices

are what keep innovation sustainable. Here are a few ways your team can manage AI’s risks and stay grounded while scaling up its use.

Research use cases 

Before adding another AI tool, make sure it actually fits a need. Research where automation will drive measurable impact, like accelerating testing or improving documentation, before committing to a full rollout.

Invest in risk management

Treat AI like any other major dependency: test, verify, and validate. Establish guardrails for reviewing AI-generated code and create fallback processes to prevent data leaks or corrupted output.

Plan for skill shifts

AI changes what core skills look like in engineering. Encourage developers to adapt — learning how to prompt effectively, validate AI output, and debug with new tools. This isn’t about replacing expertise; it’s about evolving it.

Provide AI training

Train engineers on both the capabilities and the limits of AI. A clear understanding of how these tools work (biases included) can help your team use them responsibly and get reliable results.

Capture your baseline before AI blurs it

Before integrating AI, benchmark key metrics like cycle time, review responsiveness, and PR size. This helps you measure true improvement later rather than relying on vague impressions.

Define what “good” looks like and show your work

Establish a shared definition of success. Whether that’s smaller PRs, faster merges, or fewer post-release bugs, transparency in measurement builds trust and accountability across the team.

Share trendlines, not just snapshots

Track progress over time rather than relying on single data points. Trendlines reveal whether AI adoption is genuinely improving consistency and collaboration or simply creating short-term bursts of activity.

Stay ahead in AI-powered software development with Appfire

Integrating AI into development workflows can be transformative, but only if you can see what’s really happening. Workflow diagnosis software like Flow provides objective visibility into how AI affects your team. By tracking patterns in commits, PR behavior, and cycle times, Flow highlights where AI is making a meaningful impact and where your team may need support.

This data-driven approach removes the guesswork. Engineers can learn from each other’s successes, adjust their practices, and continuously improve without hype or assumptions. Flow empowers your team to adopt AI responsibly and confidently while maximizing real improvements in their software development process.

Book a demo today and discover how Flow can transform your development workflow.