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State of platform engineering in the age of AI (AI column)

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

Interesting. report finish 2024 The project was prepared by Red Hat in cooperation with the research agency Illuminas. Red Hat is a global leader in enterprise open source solutions, actively developing platform engineering and implementing AI in enterprise infrastructure. Illuminas is an experienced research company specializing in IT and B2B markets. This report is based on a survey. 1000 Platform engineers and IT executives from the US, UK and Asia Pacific.

The methodology of the study was as follows:

  • It was an online survey. 20 minute 1000 respondents (Equally Engineers and Managers)
  • 35The percentage of participants were medium-sized companies and 65% - large
  • Employees of companies from industries: IT, finance, retail, healthcare, professional services participated Geography of the survey: USA, UK, English-speaking APAC countries

Key ideas and conclusions of the survey are as follows: 1. Platform Engineering Becomes Strategic 62% of companies already have dedicated platform engineering teams. Platform engineering is not just infrastructure automation, but a strategic tool to accelerate innovation and AI adoption. 2. The impact of artificial intelligence 76% of organizations already use generative AI for development tasks (documentation, code generation, tips). 45Percentages see AI as a central element of their platform strategy. 3. The platform engineering maturity model Red Hat highlights 4 level of maturity:

  • Exploring - research Emerging - Start of implementation
  • Established - established practice Advanced - Advanced level Companies with high maturity reach 41Percentage of best results (Productivity, Innovation, Safety). 4. The main drivers of implementation
  • Security. Improved interaction between teams Automation and acceleration of processes 5. Evolution of investment In the early stages – focus on infrastructure modernization (55%) In advanced stages - automation (85%)safety (59%)Developer tools (55%) 6. Challenges and challenges Difficulties with workflow integration and security risks remain at all maturity levels (along 37%). In the early stages, lack of skills and budget (40%) At advanced stages – tool incompatibility, platform instability, knowledge deficit (near 30%) 7. Metrics of success Mature companies track more indicators (average 7)including productivity, security, application performance, developer satisfaction. In the early stages, the focus is often on reducing costs.

If we summarize the report, we can conclude that Platform engineering is becoming a separate profession requiring new skills and a team structure. Companies and vendors should integrate AI into platform solutions rather than treat AI as a standalone tool. Standardization of architectures and best practices accelerates adoption and reduces risk. Security and automation are key priorities for future development.

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