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Gartner’s May 2024 outlook identified five trends intended to make software engineering more productive and sustainable: software engineering intelligence, AI-augmented development, green software engineering, platform engineering, and cloud development environments. Its July 2025 update shifted the emphasis toward AI-native engineering, LLM applications, and GenAI platforms while retaining green software engineering. The original five remain a useful framework, but Gartner’s forecasts are predictions—not proof that adopting a trend will produce a particular result.
What Gartner’s five trends mean for engineering teams
Gartner connected the trends to a broader business problem: engineering organizations need to deliver software that meets business objectives while reducing friction and maintaining quality. In a Gartner survey of 300 software-engineering and application-development managers in the United States and United Kingdom, conducted in Q4 2023, 65% said meeting business objectives was among their top three performance objectives. Gartner vice president analyst Joachim Herschmann said the identified trends were already helping early adopters achieve business objectives.
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| Trend | Potential benefit | Quality, risk, or governance consideration | Organizational investment |
|---|---|---|---|
| Software engineering intelligence | Visibility into engineering flow, velocity, quality, organizational effectiveness, and business value. | Metrics need to be interpreted in context; a single productivity measure cannot establish the quality or value of software delivered. | Adopt a platform and agree on useful, transparent measures across teams. |
| AI-augmented development | Assistance with design, code generation, design-to-code work, and testing may reduce repetitive effort. | Generated work still needs engineering review and testing; teams need governance for how AI is used. | Evaluate tools against actual workflows and establish review practices. |
| Green software engineering | Reduce software-related energy use and carbon impact through choices across the software lifecycle. | Sustainability goals need to be made concrete in requirements and considered alongside performance and reliability. | Build sustainability into design decisions, engineering practices, and infrastructure choices. |
| Platform engineering | Reusable internal capabilities can reduce cognitive load and make common development tasks easier. | A platform that does not fit teams’ needs can add friction rather than remove it. | Invest in platform teams, reusable capabilities, and an internal developer experience. |
| Cloud development environments | Ready-to-use remote workspaces can reduce setup work and accelerate onboarding. | Teams must account for their development workflows and the operational needs of hosted environments. | Provide and maintain cloud-hosted workspaces suited to the team’s tools and projects. |
1. Software engineering intelligence
Software engineering intelligence platforms aim to give leaders a unified, transparent view of how engineering work moves through an organization. The useful shift is from relying on disconnected indicators to examining flow, quality, organizational effectiveness, and business value together. That visibility can help teams identify bottlenecks and discuss outcomes, but it should support diagnosis and improvement—not become a simplistic ranking of individual developers.
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2. AI-augmented development
In Gartner’s 2024 framing, AI assists across design, coding, and testing. Examples include generating code, transforming designs into code, and enhancing testing. These uses target different kinds of work: code generation can help with implementation, design-to-code can shorten translation between design and development, and testing assistance can help teams examine software behavior. The practical question is whether assistance improves a team’s workflow without weakening review, testing, or accountability.
In a Gartner survey, 58% of respondents said their organization was using or planning to use generative AI within the next 12 months to control or reduce costs. That finding reflects respondents’ reported use or plans, not demonstrated savings. For teams considering AI coding tools, start with a defined workflow, evaluate output through existing engineering standards, and decide how generated code and tests will be reviewed before expanding use.
3. Green software engineering
Green software engineering means designing and building software to be carbon-efficient and carbon-aware. Gartner’s description spans architecture, design patterns, algorithms, data structures, programming languages, runtimes, and infrastructure. Sustainability is therefore not only a hosting decision: software design and implementation choices can affect the resources needed to run a service.
Gartner predicted in 2024 that 30% of large global enterprises would include software sustainability in non-functional requirements by 2027, up from less than 10% in 2024. The forecast points to a practical organizational step: specify sustainability expectations as requirements that engineering teams can consider alongside other system qualities, rather than treating them as an informal aspiration.
4. Platform engineering
Platform engineering provides reusable capabilities through internal platforms and developer portals. The aim is a “paved road”: a supported way to carry out common engineering tasks that reduces cognitive load, saves developer time, and can improve job satisfaction. A platform may help teams avoid repeatedly solving the same setup and delivery problems, but it needs to serve developers’ real work rather than impose an extra layer they must work around.
Gartner predicted in 2024 that 80% of large software-engineering organizations would establish platform-engineering teams by 2026, up from 45% in 2022. The prediction is about large organizations and team formation; it does not establish that every organization needs a dedicated team or that platform engineering automatically improves delivery.
5. Cloud development environments
Cloud development environments are remote, cloud-hosted workspaces prepared for development. They can reduce the time spent configuring a workstation, separate development from a particular physical machine, and make onboarding easier by giving developers a ready-to-use environment. Their value depends on whether those workspaces are convenient and suitable for the team’s actual tools and work; the trend is a delivery model for development environments, not a guarantee of faster engineering by itself.
How the 2025 update changes the picture
Gartner’s July 2025 update reframed the agenda as six trends: AI-native software engineering; building LLM-based applications and agents; GenAI platform engineering; maximizing talent density; growth of open GenAI models and ecosystem; and green software engineering. Compared with the 2024 list, AI is no longer just one kind of development assistance: the updated agenda also covers engineering practices built around AI, products that use LLMs, and platforms that provide GenAI capabilities. Green software engineering remains on the list.
Gartner’s 2025 forecasts illustrate that shift:
- Gartner predicted that 90% of enterprise software engineers would use AI code assistants by 2028, up from less than 14% in early 2024.
- Gartner predicted that at least 55% of software-engineering teams would actively build LLM-based features by 2027.
- Gartner predicted that 70% of organizations with platform teams would include GenAI capabilities in internal developer platforms by 2027.
- Gartner predicted that 30% of total global enterprise GenAI spend would be on open GenAI models tuned for domain-specific use cases by 2028.
These are Gartner predictions, not measured adoption or spending outcomes. The release’s stated figures do not specify a regional breakdown for these forecasts.
How to decide which trends to pursue
The trends address different problems, so they are not a single package that every organization should adopt at once. Match an investment to a specific constraint, then assess whether it improves the work without creating a larger burden elsewhere.
- If leaders lack a clear view of delivery: consider software engineering intelligence, with measures that connect engineering activity to quality and business outcomes.
- If repetitive design, coding, or testing work is a bottleneck: assess AI assistance in a bounded workflow and retain human review and established quality controls.
- If energy and carbon impact are organizational priorities: make software sustainability part of requirements and design decisions.
- If teams repeatedly solve setup and delivery problems: explore platform engineering and reusable internal capabilities before building a platform that does not have clear users or needs.
- If workstation setup or onboarding creates friction: consider cloud development environments and verify that they support the team’s working practices.
In 2025, teams planning AI work should also distinguish between using coding assistants internally, building LLM-based features, and adding GenAI capabilities to an internal platform. Those are related but different decisions, with different product, platform, and governance implications. Gartner vice president analyst Joachim Herschmann described the broader change this way: “AI-enabled tools and technologies are fundamentally changing how software is built and delivered.”
For engineering leaders, the central test is not whether a trend is prominent, but whether it addresses a real organizational need and can be adopted with suitable quality, sustainability, and governance practices. Gartner characterized its 2025 trends as a roadmap for AI-driven automation, talent strategies, and sustainable, AI-native engineering; teams still need to choose investments according to their own constraints and goals.
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