TLDR: Gartner has identified six strategic trends that will profoundly influence software engineering practices from 2025 onwards. These include the widespread adoption of AI-native engineering, the development of applications powered by Large Language Models (LLMs), and the integration of Generative AI (GenAI) into platform engineering. The trends also emphasize maximizing talent density, the growing prominence of open GenAI models, and the critical importance of green software engineering practices to address environmental impact.
Gartner, a leading research and advisory company, has released its top strategic trends poised to reshape the landscape of software engineering from 2025 and beyond. These six pivotal trends are designed to guide software engineering leaders in accelerating innovation, optimizing talent strategies, and adopting future-ready, sustainable practices, with a significant emphasis on artificial intelligence (AI).
Joachim Herschmann, VP Analyst at Gartner, stated, ‘AI-enabled tools and technologies are fundamentally changing how software is built and delivered.’ He further elaborated that leaders who capitalize on these trends will be better positioned to accelerate innovation, strengthen their teams, and generate long-term business value.
The Six Strategic Trends Identified by Gartner:
1. AI-Native Software Engineering: This trend signifies the deep integration of AI across the entire software development lifecycle (SDLC), from design to deployment. AI will increasingly handle tasks autonomously or semi-autonomously, shifting the developer’s role from implementation to orchestration, problem-solving, and system design. Gartner predicts a dramatic increase in AI code assistant usage, with 90% of enterprise software engineers expected to utilize them by 2028, a substantial leap from less than 14% at the beginning of 2024. Success in this area requires a careful balance between automation and human oversight, considering the criticality, risk, and complexity of workflows.
2. Building LLM-Based Applications and Agents: Large Language Models (LLMs) are set to revolutionize software engineering by enabling applications to interact intelligently and autonomously, mimicking human-like behavior. By 2027, Gartner forecasts that at least 55% of software engineering teams will be actively building features based on LLMs. This necessitates that leaders rethink their strategies, invest in upskilling their teams, and implement robust guardrails for risk management and governance.
3. GenAI Platform Engineering: This trend involves integrating generative AI (GenAI) capabilities into internal developer platforms, leveraging established platform engineering approaches. Gartner anticipates that by 2027, 70% of organizations with platform teams will incorporate GenAI capabilities into their internal developer platforms. This integration aims to make AI capabilities easily discoverable through self-service portals, prioritize features based on developer needs, and ensure compliance with organizational policies, thereby accelerating AI-powered application development.
4. Maximizing Talent Density: Beyond traditional hiring, organizations must focus on cultivating high talent density—building teams with a concentrated pool of highly skilled professionals. This involves fostering a culture of continuous learning, collaboration, and adaptability to evolving business needs, which will be a crucial differentiator for competitive advantage and customer value delivery.
5. Growth of Open GenAI Models and Ecosystem: Open GenAI models are gaining significant traction due to their flexibility, cost-effectiveness, and freedom from vendor lock-in. These models allow organizations to customize, fine-tune, and deploy AI solutions tailored to their specific needs, whether on-premises or in the cloud. Gartner projects that by 2028, 30% of global enterprise spending on GenAI technologies will be directed towards open GenAI models customized for domain-specific use cases, making advanced AI more accessible and affordable.
6. Green Software Engineering: As companies develop and acquire more software, particularly with the rise of computationally intensive AI applications, the increasing carbon footprint becomes a concern. This trend emphasizes the adoption of sustainable software development practices to align with environmental goals. Herschmann noted, ‘Creating intelligent applications with GenAI consumes a lot of energy, making green software engineering practices essential.’ This highlights the need for energy-efficient coding, infrastructure, and deployment strategies.
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These strategic trends underscore a fundamental shift in software engineering, driven largely by the pervasive influence of AI, demanding proactive adaptation from leaders to ensure long-term success and sustainable innovation.
