Overview

Challenges

Over the past two years, the technology industry has seen AI rapidly transition from early novelty to a critical driver of business outcomes. In tandem with this, there is an increasing demand for managed AI services that provide ongoing support and sustained value. This publication highlights the complexities enterprises face in leveraging AI, particularly in integrating it into existing workflows and addressing operational challenges.

Summary

The article discusses the evolving landscape of generative AI, moving from a phase of rapid initial adoption to a more mature stage where tangible business outcomes are prioritized. As enterprises recognize the challenges of integrating AI into existing workflows, the demand for managed AI services grows. Companies like OpenAI are pioneering strategies that include dedicated consulting services along with specialized roles, such as forward deployed engineers, to aid in navigating the complexities of AI implementation and ongoing operational maintenance.

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Included in the full report

From Products to Solutions:

There's a strategic shift from merely providing access to AI models to delivering guaranteed business outcomes that address complex operational challenges.

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'Last Mile' Challenge:

The gap between technical capabilities and effective application in enterprises highlights the need for robust consulting and integration services.

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Managed Services Model:

Companies that adopt a managed AI services model ensure ongoing operational efficiency, combating issues like model drift and data integrity.

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Importance of Data Integrity:

The success of AI systems heavily relies on the quality of data, underscoring the necessity for continuous data management throughout the AI lifecycle.

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Trust and Governance:

Establishing trust with clients requires managed service providers to implement strong data governance, security, and compliance frameworks.

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Publication date:

October 3, 2025

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