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

Why This Matters Now

As AI transforms software development in 2026, coding isn't solely about writing lines—it's increasingly focused on smarter verification, enhanced productivity tracking, and stronger trust in the systems we build. Tariq Shaukat, CEO of Sonar, emphasizes that AI’s role in coding has progressed from automation to quality assurance and trust-building. The future is here: automated verification, real-time metrics, and proactive issue detection will allow developers to concentrate on creative problem-solving rather than monotonous tasks.

Key Points

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

• Productivity measurement: AI delivers real-time metrics that aid developers in tracking efficiency, identifying bottlenecks, and ultimately focusing on the creative aspects of coding.

• Building trust in AI systems: With AI’s expanding role, trust is crucial. The transparency and explainability of AI-driven coding tools provide developers with confidence in their decision-making.

How It Works

AI-driven platforms like Sonar are revolutionizing software development by automating code verification and enhancing reliability. With sophisticated algorithms and real-time analytics, AI now aids developers in identifying potential issues as they arise, ensuring high-quality code. These tools utilize metrics to help gauge developer productivity and efficiency—freeing developers from repetitive tasks so they can tackle more complex and creative problems.

Tariq Shaukat stresses that AI systems need to be transparent and explainable to establish trust among developers. Sonar’s advanced AI tools provide clear insights into why specific suggestions are made, boosting developer confidence in adopting AI-powered systems for code reviews.

Practical Steps (Playbook for 2026)

1. Automate Code Verification
   Utilize AI-driven tools like Sonar to perform real-time code reviews. These tools automatically pinpoint issues, such as security vulnerabilities or coding inefficiencies, before they reach production.
   Assess improvements in speed and accuracy of the review process, and monitor how often issues are intercepted early.

2. Implement AI-Powered Metrics
   Introduce productivity metrics using AI tools that track the efficiency of your development operations. These metrics can illuminate bottlenecks, task durations, and enhance your team’s overall performance.
   Example: Use Sonar’s dashboard to monitor metrics like lines of code, code quality, and testing success rates in real-time.

3. Boost Trust with Explainable AI
   Ensure transparency by employing AI systems that offer not only code change suggestions but also explain the rationale behind them. Developers should have confidence in AI’s recommendations, especially when the system provides detailed explanations of issue detection and significance.
   Regularly assess the clarity of AI suggestions and observe the team's confidence in adopting these tools.

4. Ease Developer Workload with AI Automation
   Allow AI to manage repetitive tasks like bug detection or code formatting, enabling developers to focus on more crucial, creative problem-solving.
   Track time saved on repetitive tasks and quantify the proportion of tasks automated by AI.

FAQs

Q1: How does AI enhance code quality?
AI automates verification, uncovers issues promptly, and provides insights into code performance, aiding developers in maintaining elevated standards of quality throughout the development process.

Q2: What are productivity metrics in AI coding?
AI tools monitor efficiency and output, delivering real-time metrics to gauge developer productivity and highlight improvement areas.

Q3: How does AI foster confidence in software development?
By ensuring decision-making is transparent and explainable, AI tools assist developers in understanding why suggestions are made, which builds 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

Interested in boosting your team’s productivity with AI in coding and enhancing code quality? Contact Generation Digital to explore how Sonar’s tools and other AI-driven platforms can revolutionize your development process.