AI ushered in a new era in software development last year, driving significant gains in productivity, code quality, and innovation. As technology leaders navigate this new landscape, many are still determining how to target future strategic investments and seeking new opportunities to gain a competitive edge through AI.

In 2025, organizations will expand their use of AI to new areas that move beyond simple automation to contextual awareness and proactive decision-making. Leaders will also learn to quantify AI’s impact on the business, helping to steer future investments to where they yield the greatest returns.

Open-source AI technologies will continue to improve in performance, providing more cost-effective options for training and operating large language models behind corporate firewalls. This will allow organizations in tightly regulated industries to build more powerful applications using internal corporate data.

Here are four ways AI will transform software development in the next year:

The Future of Software Development is Context-Aware

While many development teams already embed AI in some of their workflows—such as code completion assistance and code explanation—context-aware AI is the next frontier and a crucial foundation for the development of agentic AI. Agents can only operate effectively if they capture the necessary context, which extends far beyond the codebase itself. Context-aware AI has the potential to reshape software development through applications that understand and adapt to environmental context.

When AI understands both​​ user and application context, it can automate more complex tasks, anticipate a developer’s needs, and make better-informed decisions. This translates into increased efficiency and accuracy and allows developers to apply their expertise to more creative and strategic work. Eventually, AI will go beyond simply adhering to development best practices and optimize code based on different variables such as performance, scalability, or even costs.

Here are several key areas where we anticipate seeing this impact in the coming year:

Infrastructure-aware updates: Managing updates to legacy systems can be particularly challenging. AI can help by considering both the codebase and potential implications for the underlying cloud infrastructure and application performance when proposing changes to maintain security and compliance.

Enhanced code understanding: As AI matures, it will be able to analyze existing codebases and proactively suggest new functionality that integrates seamlessly with existing architecture, infrastructure, and application needs. It will also automatically adhere to an environment’s security and compliance guardrails.

Streamlined code reviews: Code reviews can be a bottleneck, but AI can help streamline the process. AI-assisted code reviews will flag potential issues based on existing standards, best practices, and predicted performance implications, helping development teams better collaborate with shared context.

Improved testing: By understanding application logic and performance characteristics, AI can generate more comprehensive tests to identify and prevent code defects before they reach production proactively.