As AI-native infrastructure raises the stakes, platform engineering is moving from a supporting role to a core business capability.
Execution quality, leadership alignment, and operating models are now the difference between acceleration and stagnation.
AI-native workloads are fundamentally changing the job of platform engineering teams by introducing far more dynamic, decentralized and difficult-to-control infrastructure environments than traditional cloud-native deployments.
“It brings more ‘chaos’ — infrastructure management becomes much more fragmented,” says Pavlo Baron, CEO of Platform Engineering Labs. “Enforcing a single workflow or a manually managed source of truth that classic IaC requires is not possible.”
He points out changes happen through all kinds of paths — tools, ClickOps, APIs — and it is more necessary than ever to stay in control, for example, by using solutions that eliminate drift automatically.
Baron adds AI workloads create multiple parallel ways for infrastructure to be provisioned and modified, often outside formal deployment pipelines, as teams spin up and adjust resources through automation tools, application interfaces and on-demand operational changes.
That shift makes traditional infrastructure-as-code governance models harder to sustain and increases the risk of configuration drift across environments.
As a result, platform engineering teams are increasingly responsible not only for building standardized platforms, but also for continuously monitoring and correcting infrastructure state to maintain security, compliance and operational stability in fast-changing AI environments.
Pipelines, Tooling, Models
Yasmin Rajabi, COO at CloudBolt, says AI-native workloads expand the surface area, forcing platforms to own economic and operational outcomes even more than traditional cloud-native efforts.
“It’s more than just providing pipelines and tooling; it also requires governing data and models while still enabling experimentation,” she says.
With a larger surface area and an easier ramp to adoption, costs can quickly spiral out of control, so platforms need to account for that from day one.
Organizational misalignment—rather than tooling or architecture—continues to be one of the primary reasons platform initiatives stall, even in environments with modern stacks, according to Rajabi.
“Incentives are almost always misaligned,” Rajabi said. “Developers are rewarded for speed at any cost, while platform teams are responsible for enabling them and ensuring the efficiency of the platforms they run.”
She says platform teams often lack both authority and application-level context, which makes optimization efforts contentious.
“Without much authority or context on the applications being deployed, platform teams end up at odds over how to optimize, and no one wants to be blamed for a change that slows development teams—or, even worse, causes downtime,” Rajabi explains.
When it comes to proving business value, Rajabi says platform teams must move beyond delivery and reliability metrics and show direct operational impact.
“Business value can be demonstrated through a reduction in waste or time spent in toil,” she says.
In practice, that means quantifying improvements such as driving down cost per workload, reducing manual interventions, and enabling faster MTTR from performance-related issues.
MTTR—mean time to recovery—measures how quickly teams restore services after incidents, while toil refers to repetitive, manual operational work that does not create lasting system improvements.
Importance of Leadership
Leadership behavior also plays a defining role in whether platform programs accelerate or stall.
Rajabi says effective leaders break down silos, force uncomfortable conversations through clarity of decision-making, and sustain sponsorship of initiatives that aren’t easy.
She adds that adoption improves when leaders ensure the platform becomes the easiest path to solving difficult toil challenges—while reducing what’s on developers’ plates.
At an organizational level, Rajabi says funding and governance models are becoming as important as technical design.
“Centralized funding with explicit business outcomes and buy-in from the very top is essential,” she says. “Platforms can’t be a side project; they must sit at the center of development and infrastructure, with clear ROI expectations from day one.”