Microsoft, AWS deploy engineer armies to help make AI profitable

2026-07-02 · Source: News on Artificial Intelligence and Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Corporate Strategy & Leadership · Depth: Intermediate, quick

Summary

Microsoft and AWS are launching major initiatives to embed their engineers directly within client companies, aiming to accelerate AI profitability. Microsoft established the Microsoft Frontier Company with a \$2.5 billion investment and 6,000 experts, while AWS announced its Forward Deployed Engineering unit with a \$1 billion investment. These efforts address the challenge that despite almost nine out of 10 companies deploying AI by the end of 2025, 94% report no significant benefits from these expenditures, according to McKinsey. The cloud giants believe their engineers can deliver results faster than in-house teams, emphasizing a problem-first approach rather than forcing technology. This strategy echoes similar moves by OpenAI and Anthropic, and a concept pioneered by Palantir, as tech companies seek to recoup substantial AI infrastructure investments amid market pressures and job cuts, such as Microsoft's 15,000 job reductions in 2025.

Key takeaway

For Directors of AI/ML struggling to demonstrate tangible ROI from AI initiatives, recognize that simply deploying tools is insufficient. Your strategy must shift towards deep integration and process re-engineering. Consider leveraging external, embedded expertise from cloud providers like Microsoft or AWS. Focus on defining business problems first, then apply AI solutions. Prioritize speed and measurable outcomes to avoid becoming part of the 94% reporting no significant benefit.

Key insights

AI profitability demands embedded expertise and process re-engineering, not merely tool deployment, to overcome widespread lack of significant business benefits.

Principles

• AI adoption requires rethinking core work processes.
• Prioritize problem definition over technology imposition.
• Speed of implementation is a key client metric.

Method

Cloud providers embed their engineers directly into client companies to integrate AI, focusing on problem-first solutions and re-engineering workflows to drive tangible business value and speed.

In practice

• Integrate external experts for complex AI deployments.
• Prioritize business outcomes over AI tool acquisition.
• Resist deploying AI without clear problem definition.

Topics

• AI Profitability
• Cloud AI Services
• Microsoft Frontier Company
• AWS Forward Deployed Engineering
• AI Implementation Strategy
• Embedded Engineering

Best for: CTO, Executive, Investor, Director of AI/ML, Consultant, VP of Engineering/Data

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Editorial summary, takeaway, and curation by AIssential. Original article published by News on Artificial Intelligence and Machine Learning.