How AI and DevOps Are Shaping the Future of Enterprise Operations

Research System Progress in R&D Operations

With large‑scale DevOps practice, DevOps has become a key driver of digital transformation, improving efficiency, customer experience, and business innovation. The DevOps capability maturity model has expanded from seven to fifteen components, covering continuous testing, performance measurement, platform engineering, SRE, intelligent R&D operations, and more.

The 2024‑2027 Information Standard Construction Action Plan issued by three ministries encourages domestic standards to be compatible with international standards and promotes enterprise participation.

Since 2024, the institute has launched the ITU DevOps International Standard Assessment, upgrading assessment scope, reports, and certificates.

Organization‑Level DevOps Capability Building

BizDevOps extends DevOps to the business side, integrating lean R&D concepts. A new BizDevOps standard supports business value management, demand management, agile development, continuous delivery, and operational management, enhancing capabilities such as performance measurement, security compliance, stability, user experience, design, and observability.

Tool‑platform construction is emphasized, with policies encouraging one‑stop R&D collaboration platforms. Over 35 enterprises in finance, telecom, energy, automotive, and manufacturing have passed tool‑platform assessments.

Organization‑level DevOps assessment consists of general capability (standards, processes, platforms, talent, metrics) and practice capability (project pilots, domain promotion, enterprise‑scale rollout). Evaluation requires coverage of at least two capability domains such as business value management, agile development, continuous delivery, DevSecOps, technical operations, or SRE stability.

AI‑Empowered R&D Operations

AI is a national strategic focus. In 2024 the institute released the “General Capability Requirements for R&D Operations Large Models” standard, promoting deep integration of large models in development, testing, and operations.

Intelligent R&D assistants, adopted by more than 30 enterprises, improve code quality, reduce maintenance cost, and enhance system stability. The standard defines over 30 modules and 200 items covering intelligent requirement management, project tracking, design, development, acceptance, and collaboration.

AI agents for operations (AI Agent) address the “brain‑without‑hands” problem, enabling automated decision‑making, observability, and fault management. A maturity matrix for DevSecOps defines AI‑supported security checks, risk assessment, and automated response.

Digital employees, another research hotspot, have defined capability requirements for lifecycle management, technical complexity, and maintainability, enabling collaborative analysis, content generation, and task execution.

Foundational Support – Intelligent Operations Assurance

The institute has built an AI‑driven operations assurance system (SOMM) covering intelligent operations, fine‑grained operations, SRE stability, data capability, and technical foundation. Key components include AIOps, FinOps, SRE, data governance, and technical operations.

AIOps encompasses large‑model capabilities, AI agents, system observability, and intelligent computing. FinOps focuses on cloud cost management and governance. SRE ensures system stability across development and operations. Technical operations provide monitoring, incident, change, configuration, capacity, high‑availability, business continuity, and user‑experience management.

By the end of 2024, more than 20 enterprises have passed DevOps technology‑operation assessments, demonstrating growing emphasis on operational maturity.