From Cloud-Native to AI-Native Software Engineering: How Strategic Considerations Are Shifting

Description

This IDC Perspective compares and contrasts cloud-native and AI-native software engineering, including discussing strategic considerations, priorities, efforts and target benefits."AI-native builds on cloud-native principles and delivery models. While cloud-native focuses on abstraction and resilience, AI-native - as shaped by current AI maturity levels - emphasizes systems infrastructure optimization, quality of data sets, models and pipelines, and designing practical governance structures. This transition reengineers stacks, reshapes roles, and reexamines governance posture and risk management practices." - Associate Research Director George Mironescu, Software Development and Delivery, IDC

Product Code: EUR153764425

Executive Snapshot

Situation Overview

Systems Architecture
- Cloud Native: Abstracting Every Software Delivery Capability Area
  - Efforts and Benefits
  - Key Design Considerations
- AI Native: Optimizing the Cost of Silicon-LLM Engineering
  - Efforts and Benefits
  - Key Design Considerations
Data Landscapes
- Cloud Native: Containerizing Distributed Data Environments
  - Efforts and Benefits
  - Key Data Considerations
- AI Native: Achieving Data Set, Model, and Data Governance Quality
  - Efforts and Benefits
  - Key Data Considerations
Culture
- Cloud Native: Orchestrating Teamwork and Building Technical Competence
  - Efforts and Benefits
  - Key Cultural Considerations
- AI Native: Guiding Agent Output and Overseeing Agent Fleets
  - Efforts and Benefits
  - Key Cultural Considerations
Governance, Risk, and Compliance (GRC)
- Cloud Native: Minimizing the Attack Surface in a Multi-Layered, Multi-Vectorial Environment
  - Efforts and Benefits
  - Key GRC Considerations
- AI Native: Balancing Agency and Control to Deliver Predictable and Auditable Output
  - Efforts and Benefits
  - Key GRC Considerations