The impact AI has had on the world at large in 2025 is difficult to quantify. Every facet of modern life, from culture and politics to communication and interpersonal interactions, has been and continues to be influenced by this emergent tech.
As a result, it’s easier to get a handle on just how significant AI is today by looking at just one area in which it is especially potent, and for positive reasons.
Software development is just such an example, with the rapid uptake of generative AI tools proving a boom to productivity in this sphere. Distillery explores the figures that offer context for this ascent and the areas of development that are particularly well-suited to its implementation.
Exploring AI Adoption in Software Development
A study from Stack Overflow found that 82% of software developers are already using AI tools to generate code, making it by far the most common use case for this technology.
The second and third most widespread implementations of AI are for answering specific questions and debugging. So it’s clear that developers are turning to GenAI to streamline some of the most fundamental parts of their jobs. This supports PwC’s claim that GenAI deployment can improve software developer productivity by anywhere from 20% to 50%.
As productivity increases, costs fall, as evidenced in survey data gathered by McKinsey. In it, 14% of software engineering organizations saw operational costs drop by between 11% and 19% over the past 12 months. For 7% of respondents, this decrease in expenses exceeded 20%.
Another offshoot of McKinsey’s investigation of GenAI is that high-growth, high-innovation businesses are more likely to be embracing this tech than their less forward-looking contemporaries. Thus, the productivity gains contribute to the momentum of the market, with the winners being those teams that are bold enough to recognize the potential that GenAI represents.
Investigating the Associated Effects
Enhanced software developer productivity from GenAI is only part of what’s taking place in the market at the moment. This technology’s reach is reshaping not just how teams work but also how they’re put together.
Software development lifecycle support providers have risen to prominence in recent years. They serve to fill gaps in internal development teams, with AI tools enabling outsourced solutions like these to be more readily integrated with in-house developers.
Businesses don’t require full-spectrum development to be sustained indefinitely, regardless of need. Instead, they can tap into nearshoring services as needed, allowing for a combination of productivity and agility that’s both cost-conscious and without compromises.
This relationship is playing out across every industry niche where software development projects are always ongoing, from retail to finance and beyond.
The Personalization Perspective
Another important point to make about software developer productivity in conjunction with GenAI is that these tools are advantageous not only during development cycles but also post-launch. Chiefly, this comes from the ability to personalize end-user experiences on the fly without the usual complexities and overheads.
The retail sector serves as a good example, particularly in light of growing consumer pressures that make personalized experiences a priority to deliver. McKinsey is once again a key source for data on this, with a survey finding 76% of people feel frustrated if they don’t receive personalized interactions from businesses and brands.
GenAI significantly lowers the barriers, meaning marketing software, web stores, and mobile apps can all become entirely adaptable based on each customer’s individual preferences and past history. All developers need to do is bake these tools into the platforms they create, and GenAI does the rest once they’re up and running. This means less time spent putting together many different permutations of the experience to accommodate different audience segments during development, and less troubleshooting down the line.
The Long View on GenAI in Software Development
In short, the rapid adoption of GenAI by software development teams and the consequent productivity gains represent the ideal use case of this technology. Human experts are augmented by AI, rather than being rendered redundant by it. Developers can refine outputs and speed up tedious manual tasks, while end users benefit from a better experience.
The role of outsourcing and nearshoring in development, in part made possible by AI’s arrival, is also likely to grow as time passes. Eventually, this could become the template followed by other industries and segments as the inevitable rise of intelligent automation continues.