Executives are laser-focused on how generative artificial intelligence can have an impact on employee output. As the chief strategy officer for a DevSecOps software company, GitLab, I spend a lot of time speaking with customers about AI’s impact on software development.

Organizations have largely moved past the fear associated with AI and are now looking to make AI scalable and sustainable. However, many executives need help quantifying AI’s impact on productivity. In a recent GitLab survey, over half (57%) of executives said measuring developer productivity is key to business growth, and 51% feel their methods for measuring developer productivity are flawed — or they want to measure it but aren’t sure how.

So, what does “productivity” mean in this context? How should executives frame and measure the impact of generative AI on their developer teams?

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

Measuring beyond output

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. Capturing AI’s contribution involves more than just tallying time, team dynamics and tasks; these metrics should lead to tangible business outcomes such as user adoption, revenue and customer satisfaction. Also, it is worth noting that business outcomes may differ from company to company or project to project.

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. Peer support, working environment, job engagement and collaboration significantly influence employee turnover and productivity.

Developers spend only about 25% 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 utilize 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. Gen 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. Gen AI’s ability to tailor software to user feedback rapidly and accurately 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.

Making strategic choices to empower developers

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

1. 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.
2. 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.
3. 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.
4. 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.

Maximizing what you measure

Developer productivity is multidimensional. 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 69% of C-suite respondents said they are shipping software at least twice as fast as a year ago – highlighting that acceleration is underway – only 26% reported implementing AI.

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 maximize it.

Ashley Kramer is chief marketing and strategy officer at GitLab Inc. She wrote this article for SiliconANGLE.

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