Local organisations have largely moved past the uncertainty associated with AI and are now looking to make AI scalable and sustainable. Today, companies are focused on how it can enhance team member output.
In my conversations with local business leaders, however, it’s clear that many still need help quantifying AI’s impact on productivity and business outcomes. This process starts with measuring AI’s impact on developers. In GitLab’s recent DevSecOps Report, two-thirds (66%) of Australian businesses surveyed said measuring developer productivity is key to business growth. However, many feel their methods for measuring developer productivity are flawed—or they want to measure it but aren’t sure how.
Integrating AI into organisational workflows can drive stronger business results, help build strategic capabilities, and enhance competitiveness—particularly for SMEs that may lack the resources of larger counterparts. Developers are pivotal for all three things. Finding meaningful ways to measure AI’s impact on developer productivity in these areas is essential to unlocking its strategic value by connecting it to business outcomes.
Here are three ways business leaders can better measure AI’s impact on developers and their businesses.
1. Shifting from output to outcome metrics
Traditional metrics, such as lines of code 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. Capturing AI’s contribution involves more than just tallying time, team dynamics, and tasks; these metrics should lead to tangible business outcomes like user adoption, revenue, and customer satisfaction.
Tracking the completion time of entire projects and maintaining a comprehensive view of the development pipeline is vital. This includes monitoring deployment frequency, lead time for changes, and service restoration times to provide a holistic view of project efficiency. Evaluating team metrics is also crucial. Peer support, working environment, job engagement, and collaboration significantly influence employee turnover and productivity.
Developers spend less than 25% of their workdays writing code; the rest is spent fixing errors, resolving security issues, or updating legacy systems. Automating these tasks with 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.
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 meet customer needs and expectations more effectively. 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.
2. Empowering developers
Knowing that AI’s impact on developer productivity impacts business performance, strategic capabilities, and competitive edge, business owners should make strategic choices about AI’s deployment to empower development teams:
Enable software 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.
Monitor 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, so this should be monitored 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 by aligning AI strategies with long-term business goals.
3. Maximising what you measure
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.
A recent study showed that while 65% of local businesses said they are shipping software at least twice as fast as a year ago – highlighting that acceleration is underway – only 33% reported implementing AI.
Forward-looking businesses 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.
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