Perforce Software’s latest study links platform engineering maturity with successful AI implementation.
Perforce Software’s latest annual State of DevOps report suggests organizations with standardized platform engineering practices will be better positioned to scale generative AI technologies. The study, titled The State of DevOps Report: Platform Engineering 2023, surveyed technology professionals across organizations globally and found that platform engineering maturity was considered a critical component of AI success by 73% of respondents. By comparison, only 44% of less mature organizations agreed with that statement.
AI Adoption Is Outpacing Operational Maturity
The survey data comes as AI is making the leap from experimental projects to workflows organizations plan to operationalize. While 66% of respondents said their organizations are currently using AI across infrastructure and configuration workflows such as provisioning, drift detection, and compliance management, just 31% said their companies have achieved fully autonomous AI operations. The implication is that while many companies have begun experimenting with AI-driven workflows, few have cracked the code on how to safely scale such operations.
According to the report, governance is a major factor that separates successful platform engineering operations from the rest of the field. Organizations with formal governance practices reported trusting AI outputs 94% of the time. By contrast, organizations that lacked formal governance and instead relied on ad hoc processes only trusted AI 51% of the time. Mature platform organizations were also more likely to report having standardized internal developer platforms.
Why Platform Engineering Matters for AI
Aside from suggesting platform engineering will gain greater influence as AI becomes more ubiquitous, the findings also highlight how internal technology stacks can help companies more safely deploy generative AI. Internal platforms can help companies automate governance, create audit trails, enforce policy, and improve traceability as AI tools are leaned on more heavily for infrastructure work. Having those controls will be critical for industries that need to tightly control AI use while maintaining speed.
“AI starts to shine when you have high trust and high velocity,” Ron Hoffner, VP of Product Management at Perforce Software, said in a statement. “The data underscores that trust in AI is not accidental. It is engineered through governance, automation, and standardized workflows.”
Standardized Platforms Build Confidence in AI
The report noted that 92% of platform engineering organizations that use fully standardized internal developer platforms (IDPs) were confident in their AI outputs. Organizations that were considered IDP mature were twice as likely to run AI workflows with full autonomy. 52% of respondents said their organizations have fully automated audit trails.