Platform initiatives are diverging sharply in their ability to deliver visible business value, and the gap is being driven by culture, product mindset, and measurement discipline rather than technology selection.
This “execution gap” most often arises when platform teams are positioned as separate service providers instead of shared owners of engineering outcomes.
When responsibility is split without shared accountability, delivery teams lose autonomy and platform teams lose context.
This is reinforced by hierarchical decision-making, compliance-heavy processes, and KPI-driven management, which slow feedback and reduce trust.
Itamar Friedman, co-founder and CEO of Qodo, says the execution gap widens when quality and governance controls feel like imposed bureaucracy rather than infrastructure that accelerates delivery.
“In the AI era, this manifests as organizations trying to govern AI tools directly instead of embedding quality checks where code actually gets reviewed and merged,” he says.
He adds that continuous feedback loops with development teams are the only reliable way to understand whether new capabilities reduce friction or create it.
Ownership, Collaboration, Psychological Safety
Marcel de Vries, global MD and CTO for Microsoft Services at Xebia, says without a strong engineering culture that emphasizes ownership, collaboration, and psychological safety, platforms amplify existing silos rather than closing them.
He says platform engineering teams can translate technical efforts into business outcomes that executives understand and value by framing their work in terms of outcomes rather than technology.
Instead of focusing on tools or abstractions, they should connect platform capabilities to faster delivery, improved reliability, reduced operational risk, and greater ability to adapt to change.
“Executives value predictability and resilience, so the platform’s impact should be expressed through its effect on speed, stability, and decision-making across the organization,” de Vries says.
Lenses.io CEO Guillaume Ayme says closing the execution gap between early concepts and reliable platforms requires systems and infrastructures that operate on modern and “fresh” data – helping to avoid mistakes and wasted investment frequently caused by reliance on legacy data systems.
“When organizations combine fresh data pipelines with edge computing, developer teams can process their data right where it is created, cutting delays and reducing errors in pursuit of delivering actionable insights,” he explains.
From De Vries’ perspective, the execution gap closes when platforms are built with development teams rather than for them.
“Early involvement, continuous feedback, and shared ownership ensure the platform addresses real problems instead of theoretical ones,” he explains.
He notes adoption becomes reliable when the platform simplifies daily work, reduces friction, and allows teams to move faster without giving up control or accountability for their systems.
Balancing Productivity, Sustainability
Ayme says balancing short-term productivity and sustainability directly relates to the balance between human input and AI-driven analysis.
“This type of organizational structure allows IT specialists to focus on higher-level tasks that require human insight, letting autonomous technologies handle smaller and more frequent issues, like monitoring and resolving system failures, in real-time,” he says.
De Vries says teams balance short-term gains and long-term sustainability by avoiding convenience-driven decisions that introduce hidden complexity.
“Short-term productivity improvements should not come at the cost of ownership, adaptability, or learning,” he cautions. “Sustainable platforms remove repetitive toil while preserving teams’ responsibility for their systems and enabling continuous improvement as needs and constraints evolve.”
Friedman says sustainability comes from treating technical debt as a platform product concern.
“Platforms that accumulate complexity faster than they deliver value hit impact plateaus where maintenance overhead prevents new development,” he explains.
For AI and code quality, sustainability means building systems that enforce standards consistently as code volume increases.
“Organizations shipping 10 times more code through AI generation need quality infrastructure that catches critical issues before merge without creating review bottlenecks,” Friedman says.
Measuring Platform Engineering Maturity
Friedman explains that smart investment decisions require tracking leading and lagging indicators together. Adoption rate and time to first value indicate whether new capabilities work.
“Reduced production incidents and faster remediation cycles indicate sustained business value,” he says.
For code quality specifically, maturity appears in metrics like critical issues caught pre-merge, percentage of code meeting organizational standards, and review cycle time.
“If 90% of teams use the platform but review cycle time increases, you need to reduce noise and improve signal quality,” Friedman says.