Michel Isnard, GitLab: Transforming software development with AI

Michel Isnard, Vice President of EMEA at GitLab, provides a unique perspective on the role of AI in software development.

The implementation of AI technologies, particularly generative AI, offers a promising avenue for developers looking to supercharge their processes. According to a recent GitLab survey, many UK businesses are accelerating their software release cycles—yet only a third have integrated AI into their processes. 

However, alongside the opportunities, organisations face challenges in scaling AI and effectively measuring its impact on productivity.

Developer: In your view, what are executives focusing on when it comes to maximising the impact and output of AI implementation in their businesses?

Michel Isnard: Executives are laser-focused on how generative AI can impact employee output. As head of EMEA for a DevSecOps software company, I spend a lot of time listening to and sharing with customers about AI’s impact on software development.

For UK organisations, the boost AI promises for developer output and productivity could not come at a better time. According to a recent GitLab survey, 65% of UK businesses are releasing software at least twice as fast as last year, but just 31% of UK businesses are using AI for software development. AI could supercharge their software development process.

D: What are some of the main challenges UK organisations are facing in regard to AI implementation?

MI: Organisations are now looking to make AI scalable and sustainable while quantifying its impact on productivity. Nearly two-thirds (60%) of UK respondents said measuring developer productivity is key to business growth, yet 50% of global C-suite respondents feel their methods for measuring it are flawed—or they want to measure it but aren’t sure how. Therefore, one of the main challenges is quantifying AI’s impact on developer output. 

D: Should executives stick to conventional metrics for measuring productivity, or should they consider other proof points?

MI: Traditional metrics, such as lines of code, code commits, or task completion, often overlook the essential elements of software development, such as problem-solving, teamwork, and innovation, which are crucial for assessing business impact. Over half of C-level respondents in the UK (54%) said they focus on metrics such as the quantity of code contributions, 48% on qualitative metrics like code quality or bug/flaw frequency, and 50% on metrics like faster time to market or product enhancements.

Capturing AI’s contribution involves more than just tallying time, team dynamics, and tasks. Senior leaders need tangible business outcomes like user adoption, revenue, and customer satisfaction.

Integrating AI into organisational workflows can drive better business results, help build strategic capabilities, and enhance competitiveness. Developers are pivotal in all three aspects. Finding meaningful ways to optimise AI’s impact on developer productivity in these domains is essential to unlocking its strategic value by connecting it to business outcomes.

D: Can you provide examples of alternative metrics for executives to focus on to measure developer productivity?

MI: It’s vital to track the completion time of entire projects and maintain a comprehensive view of the development pipeline. This includes monitoring deployment frequency, lead time for changes, and service restoration times to provide a holistic view of project efficiency. Moreover, evaluating team metrics is crucial and must be measured alongside traditional productivity metrics, not as an afterthought.

UK developers spend less than a third (29%) of their workdays writing code; the rest is devoted to fixing errors, resolving security issues, or updating legacy systems. Automating these tasks with generative AI allows developers to utilise their expertise more effectively, focusing on creativity and complex problem-solving. This not only drives innovation but also enhances job satisfaction. Performance reviews, turnover rates, and internal customer satisfaction surveys are valuable tools for tracking these improvements.

Furthermore, AI is crucial in predicting development bottlenecks and automating routine tasks, leading to more predictable release cycles and faster market entry. AI improves code reviews and creates comprehensive testing scenarios, enhancing code reliability, and reducing bugs, which leads to improved software quality and higher customer satisfaction. AI’s ability to rapidly and accurately tailor software to user feedback ensures that products more effectively meet customer needs and expectations.

These AI-driven improvements can be measured through customer feedback, service requests, analyst and peer reviews, and overall market performance, providing a clear picture of AI’s contribution to business objectives.

D: What steps should management take to instigate the necessary change in their workplace?

MI: Knowing that AI’s impact on developer productivity impacts business performance, strategic capabilities, and a company’s competitive edge, management should make strategic choices about AI’s deployment to empower development teams:

• Empower developers as decision-makers: Give developers decision-making power over which AI tools can improve their sense of ownership and engagement, encouraging them to decide how AI can be integrated into their work. 
• Iterate and adapt: Encourage a culture of experimentation and iteration with AI tools. Allow development teams to go through trial-and-error phases to understand how AI best fits their processes. Support them during potential short-term productivity declines as they adjust to new tools, aiming for long-term gains.
• Watch for bad habits: AI has the potential to help less-experienced developers write code faster and enhance their skills. However, it can also potentially teach them poor coding practices inadvertently. Development team leaders should monitor this closely.
• Embrace AI for long-term transformation: View AI not as a temporary solution but as a transformative tool that can fundamentally change software development. Companies can ensure sustainable growth and leadership in technology-driven markets by aligning AI strategies with long-term business goals.

D: Any final thoughts?

MI: Developer productivity is multi-dimensional. It goes beyond task completion and time management to encompass team dynamics, problem-solving skills, and more. To truly understand how developers contribute to business value, management needs a more holistic point of view.

Forward-looking executives should explore how AI tools can enhance the quantity of work produced and the quality of business outcomes. This way, companies will not only be able to measure AI’s true potential but also have the power to maximise it.

See also: Couchbase tackles agentic AI development challenges

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Tags: AI, artificial intelligence, coding, development, generative ai, gitlab, michel isnard, productivity, programming