Gartner has identified six strategic trends that are set to redefine software engineering in 2025 and beyond, highlighting the growing influence of AI and the need for sustainable, talent-focused, and future-ready development practices.
AI-Native Software Engineering is emerging as a transformative force across the software development life cycle. AI is becoming embedded at every stage — from design to deployment — enabling autonomous or semi-autonomous execution of tasks. By 2028, Gartner expects 90 percent of enterprise developers to use AI code assistants, shifting their role from hands-on coding to orchestrating systems and ensuring high-quality outcomes.
LLM-Based Applications and Agents are revolutionizing how applications interact and perform. With over half of software engineering teams expected to develop LLM-powered features by 2027, organizations must invest in upskilling, experiment with generative AI outputs, and implement robust risk management strategies to successfully deploy these capabilities.
GenAI Platform Engineering is focused on integrating generative AI into internal developer platforms. By 2027, 70 percent of platform engineering teams will embed GenAI tools, enabling self-service access to AI features while ensuring compliance with governance and security protocols.
Maximizing Talent Density has become essential for high-performing teams. Rather than solely relying on traditional hiring, organizations are cultivating teams rich in high-skilled professionals. Building a strong culture of continuous learning, collaboration, and adaptability is key to remaining competitive in a rapidly evolving landscape.
Open GenAI Models and Ecosystem are gaining momentum as enterprises seek alternatives to proprietary AI. These models offer cost-effective, customizable, and flexible AI solutions without vendor lock-in. Gartner projects that by 2028, 30 percent of global enterprise GenAI spending will be on open models tailored to specific domains.
Green Software Engineering is rising in importance as organizations work to align their digital ambitions with environmental goals. With the energy demands of AI and compute-heavy workloads increasing, integrating sustainable practices — such as carbon-aware programming and energy-efficient design — into the development process is becoming critical.
