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Managed SPs: Optimizing the Value of AI with Managed Cloud Services

Publisher IDC
Published Jan 27, 2026
Length 19 Pages
SKU # IDC20818888

Description

This IDC Perspective discusses how success for managed SPs in utilizing automation, particularly AI, will require implementing a holistic set of capabilities that will optimize the value of automation while meeting the expected outcomes that organizations are looking to achieve when using managed cloud services. This document examines factors that involve defining the expected value and measuring outcomes when using AI with managed cloud services, providing insight into enterprise business concerns with AI when using managed cloud services, establishing a risk curve for automation, defining the critical capabilities required to manage and mitigate risks when utilizing AI as part of managed cloud services, and understanding the impact of competition and a potential move to new business models."With automation taking center stage in supporting managed cloud services, managed SPs need to design an effective blueprint for how best to deploy the many types of automation, particularly AI, to support provisioning these services, which requires taking a life-cycle approach for managed cloud services," says David Tapper, program VP, Outsourcing and Managed Cloud Services at IDC. "Investments that managed SPs must make to ensure that they can effectively support client use of AI as part of managed cloud services involve aligning the type of AI by stage in the life cycle of services based on KPIs and risk factors, architecting a business structure to support the use of AI, building an AI inventory management capability, and creating a road map of the evolution of AI versus labor."

Table of Contents

19 Pages

Executive Snapshot

Situation Overview

Executive summary

Bridging the business structure with AI across the life cycle of services for managed cloud services

The need to align AI types across the life cycle of services

Embedded transformation with managed cloud services

Aligning AI with each stage in the life cycle of services to optimize outputs while managing risks

Business structure required for automating the life cycle of services

Value of AI: Expected outcomes

Defining outcomes

Measuring outcomes

SLAs and SLOs drive the degree of risk across the life cycle for managed cloud services

Business concerns and risks with AI

Business concerns

Risk curves: Strategic considerations when using AI with managed cloud services

Life cycle curves of risks with AI

Business and IT KPIs align AI across the life cycle of services for managed cloud services

Operational risk factors for AI with managed cloud services

Managing and mitigating risks for AI while ensuring expected outcomes

Ownership of risk

Requirements for managing risks

Structural requirements

End-to-end inventory management system

Impact of partners and new business models

Public cloud providers

Industry business models

Advice for Service Providers

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