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AI is driving significant gains in software development 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 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 (LLMs) 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 over the next year:
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 operate effectively only if they capture the necessary historical organizational context, which extends far beyond the codebase. 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 costs.
Here are several key areas where we anticipate seeing this impact in the coming year:
Organizations have rapidly integrated AI into their operations in the past year, from software development to decision-making and customer service. While they are tapping into the power of AI, they still need to work on measuring its impact across various teams and business functions.
This is partly because they need to ask the right questions. Leaders tend to focus on macro issues that are hard to measure, such as “How is AI helping to increase my bottom line?” Instead, they should focus on specific business outcomes that are easier to measure.
In software development, this means looking at the impact of AI and automation on metrics like time to market for new applications and features, software quality, operating costs and developer productivity. Next year, senior leaders will sharpen their focus on these outcomes, allowing them to accurately quantify the gains from AI and justify further investments by focusing on the tasks where AI excels.
AI assistants are getting smarter, moving beyond prompt-based interactions to anticipate developers’ needs and proactively offer suggestions. This evolution is driven by the rise of AI agents, which can independently execute tasks, learn from their experiences and even collaborate with other agents. Next year, these agents will serve as a central hub for code assistance, streamlining the entire software development lifecycle. AI agents will autonomously write unit tests, refactor code for efficiency and even suggest architectural improvements.
Developers’ roles will need to evolve alongside these advancements. AI will not replace them. Far from it; proactive AI assistants and their underlying agents will help developers build new skills and free up their time to focus on higher-value, more strategic tasks. Developers can now act as “AI architects,” designing and guiding intelligent agents to tackle complex challenges. The result will be higher productivity, better-quality code and greater focus on solving broader business problems.
AI models are more powerful when trained on internal company data, which allows them to generate insights specific to an organization’s unique operations and objectives. However, this often requires running models on premises for security and compliance reasons.
With open source models rapidly closing the performance gap with commercial offerings, more businesses will deploy models on premises in 2025. This will allow organizations to fine-tune models with their own data and deploy AI applications at a fraction of the cost.
This is particularly attractive for highly regulated industries such as banking and healthcare, which can run on-premises models in air-gapped environments to ensure maximum compliance.
The expanding use of AI in software development signals more profound changes ahead. AI’s role is quickly growing beyond code generation to become an integral part of the software development lifecycle, improving security and performance while reducing technical debt.
Organizations that adapt to these changes the fastest and can measure AI’s return on investment will gain a distinct market advantage, but AI adoption requires a deliberate strategy with investment in skills and infrastructure. Overall, organizations that leverage AI effectively will thrive in the years ahead.
