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

Publisher IDC
Published Sep 19, 2025
Length 10 Pages
SKU # IDC20411681

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

Table of Contents

10 Pages

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

Advice for the Technology Buyer

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Related Research

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