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State of Platform Engineering in the Age of AI (Category AI)

#AI #Management #Leadership #Software #SoftwareDevelopment #Metrics #Devops #Processes #ML #DevEx

An interesting report from late 2024, prepared by Red Hat with research agency Illuminas. Red Hat is a global enterprise open-source leader working on platform engineering and AI in enterprise infrastructure. Illuminas specializes in IT and B2B research. The report draws on a survey of 1000 platform engineers and IT leaders in the US, UK, and Asia-Pacific region.

The methodology:

  • A 20-minute online survey with 1000 respondents, evenly split between engineers and leaders.
  • Some 35% represented medium-sized companies and 65% large companies.
  • Industries included IT, finance, retail, healthcare, and professional services.
  • Geography covered the US, UK, and English-speaking APAC countries.

The main findings:

1. Platform engineering is becoming strategic Some 62% of companies already have dedicated platform engineering teams. Beyond infrastructure automation, it is a strategic way to accelerate innovation and AI adoption.

2. AI’s impact Some 76% already use generative AI for development tasks such as documentation, code generation, and suggestions. Some 45% see AI as central to their platform strategy.

3. A platform-engineering maturity model Red Hat identifies 4 stages:

  • Exploring: investigating the approach.
  • Emerging: beginning adoption.
  • Established: a settled practice.
  • Advanced: a mature, developed capability. High-maturity companies report 41% better results in productivity, innovation, and security.

4. Main adoption drivers

  • Security.
  • Better collaboration between teams.
  • Automation and faster processes.

5. Changing investment priorities

  • Early stages: infrastructure modernization, at 55%.
  • Advanced stages: automation at 85%, security at 59%, and developer tools at 55%.

6. Problems and challenges Workflow integration and security risks remain challenges at every maturity level, each at 37%.

  • Early stages: skill and budget shortages, at 40%.
  • Advanced stages: incompatible tools, unstable platforms, and knowledge gaps, around 30%.

7. Success metrics Mature companies track more measures, averaging 7, including productivity, security, application performance, and developer satisfaction. Earlier-stage organizations focus more on cost reduction.

My takeaways:

  • Platform engineering is becoming a distinct profession requiring new skills and team structures.
  • Companies and vendors should integrate AI into platforms rather than treat it as a separate tool.
  • Standard architectures and good practices accelerate adoption and reduce risk.
  • Security and automation are central priorities for future development.

#Management #Leadership #Software #SoftwareDevelopment #Metrics #Devops #Processes #AI #ML #DevEx