AI in coding enhances quality by automating verification, improving productivity metrics, and fostering trust in AI systems, says Sonar CEO Tariq Shaukat. With real-time analytics and proactive issue detection, developers can focus on creative tasks, knowing that AI is handling the repetitive work efficiently. (Interview with McKinsey, Dec 2025)

Why This Matters Now

As AI reshapes software development in 2026, coding isn’t just about writing lines—it’s about smarter verification, better productivity tracking, and higher levels of trust in the systems we build. Tariq Shaukat, CEO of Sonar, argues that AI’s role in coding has evolved from automation to quality assurance and trust-building. The future is here: automated verification, real-time metrics, and proactive issue detection will enable developers to focus on creative problem-solving, rather than tedious, repetitive tasks.

Key Points

• Enhanced code verification: AI tools now automate complex verification tasks, identifying potential issues early in the process and ensuring higher quality code.

• Productivity measurement: AI provides real-time metrics that help developers track efficiency, identify bottlenecks, and ultimately focus on the creative aspects of coding.

• Fostering trust in AI systems: With AI’s growing role, trust is paramount. The transparency and explainability of AI-driven coding tools give developers confidence in their decision-making.

How It Works

AI-driven platforms like Sonar are revolutionising software development by automating code verification and improving reliability. With advanced algorithms and real-time analytics, AI now helps developers identify potential issues in real time and ensure high-quality code. These tools use metrics to help measure developer productivity and efficiency—freeing developers from repetitive tasks so they can focus on more complex and creative challenges.

Tariq Shaukat emphasises that AI systems must be transparent and explainable to build trust among developers. Sonar’s advanced AI tools offer clear insights into why certain suggestions are made, increasing developer confidence in adopting AI-powered systems for code reviews.

Practical Steps (Playbook for 2026)

1. Automate Code Verification
   Use AI-driven tools like Sonar to conduct real-time code reviews. These tools automatically identify issues, such as security flaws or coding inefficiencies, before they make it into production.
   Measure the speed and accuracy improvements in the review process, and track how often issues are caught early.

2. Implement AI-Powered Metrics
   Set up productivity metrics using AI tools that track the efficiency of your development process. These metrics can highlight where bottlenecks exist, how long tasks take, and how your team’s overall performance is improving.
   Example: Use Sonar’s dashboard to track metrics like lines of code, code quality, and testing success rates in real time.

3. Improve Trust with Explainable AI
   Ensure transparency by using AI systems that not only suggest code changes but also explain the reasoning behind them. Developers should trust AI’s decisions, especially when the system offers detailed explanations of how issues are detected and why they matter.
   Regularly evaluate the explainability of AI suggestions and track how well the team is adopting these tools with confidence.

4. Reduce Developer Workload with AI Automation
   Let AI handle repetitive tasks like bug detection or code formatting, allowing developers to focus on more critical, creative problem-solving.
   Track time saved on repetitive tasks and measure the percentage of tasks automated by AI.

FAQs

Q1: How does AI improve code quality?
AI automates verification, identifies issues early, and offers insights into code performance, helping developers maintain higher standards of quality throughout the development process.

Q2: What are productivity metrics in AI coding?
AI tools track efficiency and output, offering real-time metrics to measure developer productivity and focus on areas for improvement.

Q3: How does AI foster trust in software development?
By making the decision-making process transparent and explainable, AI tools help developers understand why suggestions are made, which builds trust and confidence in AI systems.

Sources:

• McKinsey interview with Sonar CEO Tariq Shaukat (December 2025).

• Sonar’s 2025 whitepaper on AI-enhanced coding (Sonar’s official site).

Next Steps

Want to boost your team’s productivity with AI in coding and improve code quality? Contact Generation Digital to see how Sonar’s tools and other AI-driven platforms can transform your development process.