Let’s be real, as developers and business leaders, we’re constantly being challenged to innovate while reducing time-to-market. It’s a heavy demand to meet, and while many are interested and experimenting with AI coding assistants to do this, the leading sentiment is still one of skepticism. There are a lot of questions circulating about how effective and useful AI is for coding. When you step back and estimate what could be gained by deploying coding assistants in your daily routine, the potential could be transformative. So much so, that it could save developers a significant amount of time. 

AI code assistants can help developers automate routine tasks, such as generating boilerplate code and generating unit tests. They can also help reduce context switching by minimizing the time spent searching for documentation or fixing minor errors. Additionally, they can accelerate the learning curve through inline suggestions and best practices that can lead to higher code quality. The list goes on. 

But one thing about these benefits that can’t be downplayed is how they can translate directly into time saved, and time that can be reinvested into higher-value work. According to an IBM developer survey, 41% of developers reported that AI tools saved them 1-2 hours per day, while 22% reported even more significant savings of three+ hours per day. This is substantial when you consider a typical 8-hour workday. How much faster could you move if certain manual processes were done with a few clicks? 

Quantifying the Savings 

Let’s see how much time could be saved with an example. 

Imagine a scenario where an organization estimates that every developer saves just 10% of their working hours by using an AI code assistant. 

For context: 

• The average developer workload: Approximately 2,000 hours per year. 

• Time saved per developer: 10% of 2,000 hours equals 200 hours per year. 

Now, let’s scale this up. Let’s say you’re an enterprise with 1,000 developers: 

• Total time saved per year: 200,000 hours annually! 

In essence, with the right tools, governance guardrails and human oversight in place, AI code assistant technology can free up a substantial amount of developer time. This isn’t just a boost in productivity; it’s a major strategic advantage that can reduce project delivery timelines, enhance quality and innovation, lighten your cognitive load and reallocate resources to more critical business challenges. In a market where speed and quality are key, AI-augmented software development can be the differentiator that offers a competitive edge. 

Supporting Statistics and Case Studies 

GitHub’s survey of 2,000 software development professionals across the U.S., Brazil, India and Germany found that developers are using the time saved with AI coding tools to focus on higher-value activities. Specifically, 47% of respondents in the U.S. and Germany reported using this extra time for collaboration and system design. 

Forrester’s recent Developer Survey indicates that 49% of developers are either already using or expecting to use generative AI assistants in the coding phase of software development. 

In the marketing field, professionals using AI tools predict that they save about 5 hours of work per week, which amounts to over a month per year of time savings. 

A broader study from the Federal Reserve Bank of St. Louis found that among workers who used generative AI, the average time savings was 5.4% of work hours. For someone working 40 hours per week, this translates to approximately 2.2 hours saved weekly. 

IBM’s Own Case Studies 

We have also seen the potential results here at IBM using AI coding assistants. For example, during an internal experiment, our software team saw how they could achieve remarkable efficiency gains by using watsonx Code Assistant to summarize code files, reducing the time required from three minutes per file to just 12 seconds in its tests, a time savings of more than 90%. This particular use case, if expanded, could create a dramatic shift in how our developers leverage AI tools and what the freed time allows them to focus on instead. 

rKube, a Morocco-based IT solutions provider, transformed 80% of the in-scope WebSphere application code automatically into contemporary frameworks using watsonx Code Assistant in a proof of concept. Westfield saved 150 development hours in just eight weeks during an application modernization pilot. 

These case studies dive into the real-world examples of the potential time savings and productivity gains that can be achieved through AI-assisted development tools. 

Final Thoughts 

While the idea of relying on AI for coding might raise eyebrows, the numbers speak for themselves. It not only boosts productivity but can also provide a strategic edge in innovation and market agility. 

Embracing AI code assistant technology is not just about keeping up with trends; it’s about unlocking a profound efficiency that can significantly impact your organization’s future. For business leaders and developers alike, the potential benefits far outweigh the skepticism.