Why an internal platform affects efficiency, delivery stability, and scaling AI in software development.
December 5, 2025 This article is an adapted translation of the "2025 State of AI-Assisted Software Development" report by Google Cloud and DORA. The original is available at the link. Reading time: 9 min. This chapter was prepared with contributions from: Eric Maxwell, 10x Technology Lead, Google Cloud; Benjamin Good, Platform Engineering Lead, Google Cloud.
In the 2024 report, we began examining how internal platforms affect software delivery performance.
Back then, the data showed that platforms improve organizational performance and team productivity, but at the same time increase delivery instability and reduce throughput. In this year's study, we went beyond stating the value of platform engineering and focused on how successful platforms actually work.
The results show that a platform is not a set of tools, but a holistic user experience that directly affects efficiency, resilience, and an organization's ability to use new technologies.
Users perceive the platform as a single whole, and the overall coherence of that experience matters more than the quality of individual features. This chapter examines the key patterns in the evolution of platform engineering: widespread adoption, changes in team structures, and the role of platforms as a strategic foundation for innovation and risk management.
Platforms have become the standard: adoption is around 90%. 76% of companies use dedicated platform teams.
The task changes: what matters is not adaptation itself, but managing a multi-team platform environment.
Platform quality amplifies AI's impact on organizational outcomes.
The higher the quality, the more visible the effect.
Strong platforms improve team efficiency, productivity, and well-being.
The platform is a holistic product that must deliver a high-quality developer experience.
The platform helps manage risk and speeds up experimentation.
This comes with a small but justified increase in delivery instability, an acceptable trade-off for higher efficiency.
Most companies already use internal platforms.
The question is no longer whether a platform is needed, but how to build it. Ninety percent of organizations have implemented at least one platform, and nearly 30% operate in a multi-platform environment. Seventy-six percent of companies have a dedicated platform team.
More than a quarter of respondents work in organizations with multiple such teams.
This points not to duplication, but to a shift from a universal platform to a set of specialized and federated platforms for different domains and stacks.
The challenge for leaders has changed too.
Now it is important not just to "have a platform," but to manage an ecosystem of multiple platforms.
This requires principles of loose coupling between teams, clear ownership boundaries, and aligned interfaces.
This structure helps platforms work as a unified system, improving the developer experience instead of creating new organizational barriers.
Respondents were asked to rate how well their platform performs different tasks. For example: "The platform helps me build and run secure services."
Core technical capabilities such as reliability and security are seen as strengths. User experience elements such as feedback handling, ease of use, and automation are noticeably weaker.
A developer’s overall impression of the platform, as either a helpful partner or a source of friction, strongly affects how each individual feature is judged. The fact that platform capabilities cluster so tightly shows that respondents do not see the platform as a collection of separate components; they experience it as a single whole.
The experience gap likely reflects a situation where the platform's technical foundations are built first.
These data show that until the user experience improves, the platform does not realize its full value.
Adopting a product mindset for the platform, where internal development tools are treated as products and developers as users, helps keep the focus on the user.
This is a key takeaway from the 2024 report.
We'll reply within 30 minutes and send relevant cases, diagrams, or analyses tailored to your context.
A user-centered approach helps avoid common mistakes when building internal developer platforms. "Build it, and they will come"
The team builds the platform based on its own assumptions about what developers need, without doing research, interviews, or hypothesis testing.
They focus only on technology, assuming the value will be obvious on its own.
The platform becomes a ghost town: it does not solve real developer problems or fit into their workflows.
The product approach starts with empathy and an understanding of developer needs.
The platform team stays in constant contact with users to understand their main pain points and build what is actually needed.
The platform team operates like an infrastructure vending machine. They have no vision or roadmap; the work is entirely reactive and consists of an endless stream of developer requests ("Create a database", "Set up CI/CD").
A bottleneck emerges, increasing the load on both the platform team and developers.
The team spends all its time on one-off tasks and never gets around to building coherent, self-service capabilities.
The product approach means building a self-service platform with a clear strategy.
The goal is to remove ticket queues by giving developers tools and automation that let them get the resources they need on their own.
The platform as an ivory-tower elephant
A central team dictates the platform architecture and toolset from the top down, setting rigid standards without feedback or collaboration.
They act as technology gatekeepers rather than helpers for developers.
Developers feel disempowered. As a result, shadow IT or unofficial workarounds emerge, undermining the platform's purpose.
The platform product manager actively gathers feedback, and developers are treated as customers.
The platform should help, not constrain: it should offer convenient golden paths that can be used, but do not have to be.
When we look at platform capabilities and how they relate to one another, most of them correlate with each other at roughly the same level.
But two capabilities stand out more than the others. First, the one most strongly associated with a positive user experience is clear feedback on tasks being performed.
When a platform clearly shows that a task completed successfully or failed, users feel they can move forward: fix it, investigate it, or proceed to the next step instead of wasting time guessing what happened. Second, a "simple and clean interface" shows a weaker correlation.
Polished UI can improve perception, but it does not make a platform effective on its own.
Although clear feedback and UI are closely tied to other capabilities, they remain among the weakest in perception.
This means that improving one capability in isolation is not a workable strategy.
To improve the perceived quality of the platform, teams must treat it as a cohesive internal product and improve the entire developer journey.
If a platform is technically strong but not user-oriented (see the DORA 2024 report), it cannot be considered successful.
A high-quality platform, by our metrics, has a broad and statistically significant positive impact.
It is associated with higher organizational performance, better product metrics, and increased productivity. In line with previous research, we also found that the better the platform, the greater the small but statistically significant increase in software delivery instability, meaning a higher rate of change failures and rework.
"The general idea was to do continuous delivery as often as possible: if a feature made it into main, ship it, run it, execute the tests as much as possible so we could catch issues."
But the main thing was simply to ship. And in most cases that was fine, because even if something broke during deployment, we had plenty of mechanisms that kept the site from going down.
In other words, a deployment could fail, and we had to fix it fast.
A good platform acts as an amplifier: it directly increases productivity and efficiency.
A small increase in instability can be a sign of a healthy high-speed system, where additional failures are acceptable as long as they do not affect product operation.
The increase in efficiency despite greater instability points to a kind of risk compensation: the platform makes rollback fast and inexpensive, teams recover from errors more easily, and they can experiment more often by accepting a slightly higher level of minor failures in exchange for speed.
A small amount of instability should be viewed as a controlled tradeoff for a substantial gain in performance.
The product quality improvements a platform enables are likely more important than a moderate increase in delivery speed and a slight drop in stability.
Investing in a high-quality platform is a powerful strategic lever that delivers broad and lasting impact.
The positive impact of adopting AI on organizational effectiveness directly depends on the quality of the internal platform.
If platform quality is poor, AI's impact is barely noticeable.
When the platform is strong, the effect becomes clear and positive.
This is a critical takeaway for any leaders investing in AI. "Wayfair found that the biggest impact came not only from detecting errors faster, but also from reducing the effort required to fix them.
Embedding AI into the CI/CD cycle showed that developers are most likely to use precise, explainable recommendations and automatically generated fixes when they appear directly in the tools they already use." - Wayfair
A high-quality internal platform serves two key functions, amplifying AI's impact on organizational outcomes. First, it acts as a layer of distribution and governance that makes it possible to scale AI's benefits from individual productivity gains to systemic improvements across the organization.
Without that foundation, AI adoption remains a set of disconnected local optimizations.
The platform provides a shared context and takes on the complex or routine aspects needed for AI to be useful and operate at scale.
At the same time, it is important to remember that AI is evolving very quickly.
Do not over-standardize AI practices, tools, and ways of working, because that can limit its benefits and the organization's ability to adapt to change. Second, the platform acts as a risk-management mechanism.
Since the platform reduces risk in other areas of engineering, the same effects are also useful for AI adoption.
The platform should create a safe space where employees can learn and experiment.
This space allows the platform and its teams to adapt faster and support new models, interaction methods, and approaches to application development.
In addition, whether code is written manually or with AI, it goes through the same automated testing and deployment processes, which helps ensure changes are safe.
Investments in AI without parallel investments in high-quality internal platforms are unlikely to produce meaningful organization-wide impact.
To gain real competitive advantage from AI, leaders need to treat platform engineering as a fundamental strategic tool.
You cannot fix a bad platform by improving one or two features.
Treat the platform as a full product and work on the entire developer experience loop, from feedback to automation. 2.
Make the platform the foundation for AI
The platform is a strategic prerequisite for unlocking the value of AI across the organization.
It is the engine that turns investment in AI into a competitive advantage. 3.
Use the platform to manage risk
A good platform changes how a company approaches risk: mistakes become cheap and easy to fix.
It is important to understand and manage the balance between the speed a platform provides and the instability that comes with it, while remembering that the platform does not eliminate the local effects of risk.
To understand how organizations turn AI from isolated improvements into scalable outcomes, we turn to value stream management.
This is where it becomes clear why some teams get real results from AI while others get lost in chaotic activity.
The seventh part of the study shows that the key factor is not delivery speed or a set of practices, but the ability to see the entire value creation journey and manage it as a coherent system.
This is the foundation that turns AI into a competitive advantage rather than a source of added complexity.
Contacts
Leave your current contact details and describe your task. We will come back with clarifying questions and a proposal for the next step.
