Forward deployed engineering AI programs expand as enterprises push past pilots

Forward deployed engineering AI is becoming a larger line item for cloud providers: Microsoft said it will spend $2.5 billion on Microsoft Frontier Company and place 6,000 engineering experts in customer operations, while AWS said in July it would invest $1 billion in its own forward-deployed engineering hub. The announcements reflect a push to get enterprise AI systems into production workflows, where integration work and operational change have proved harder than a pilot.

Neither company disclosed a comparable unit price or financial outcome for these programs in the reporting reviewed. Microsoft said Land O’Lakes and Unilever were already using Frontier Company, and said it would extend the effort through partners including Accenture, Capgemini, EY, KPMG and PwC.

What is forward deployed engineering AI?

Forward deployed engineers are specialized staff from a technology provider or services firm who work closely with a customer to connect AI systems to business processes and support implementation and adoption in customer operations. The model is intended to close the gap between software availability and a working deployment inside a company’s existing systems, data and teams.

Alex Coqueiro, a senior director analyst at Gartner, told CIO Dive that enterprises seek such teams because they can shorten deployment timelines and address the difficult last stage between a pilot and production in complex environments. He forecast that more than 85% of technology providers would have launched FDE programs as a core AI-delivery method by the end of 2026. That is a forecast, rather than an observed adoption rate.

The broader demand is tied to pressure on IT leaders to show returns from AI investments, according to Jennifer Hamel, IDC’s research vice president for enterprise data and AI services. Hamel told CIO Dive that companies are moving beyond experimentation and toward selecting products that produce business value. CIO Dive reported that OpenAI, Anthropic, Accenture and Deloitte are also investing in FDE hiring.

Why do enterprise AI deployments stall?

The engineering model does not remove the underlying work. Cohere, in a vendor-authored deployment guide, identified integration, AI deployment costs, security, performance and scale, and customization as common obstacles in moving AI from pilots to production. In a customer-experience example, Alorica executives told CX Today that fragmented legacy systems, data silos, scaling across channels and geographies, and change management create barriers. That example also shows why handoffs between automation and human staff can matter in live service operations.

For buyers, FDE should be treated as a procurement and operating-model choice, not an outsourced shortcut. A useful engagement starts with a defined use case and measurable production outcome, then assigns internal technical and business owners. Companies should set data, security and integration boundaries before work begins, require knowledge transfer, and budget for platform, integration and continuing-service costs.

Cost and dependence are the central trade-offs. CIO Dive reported Gartner estimates that FDE consulting could cost $200,000 to $400,000 per use case each quarter, before platform and integration expenses. Gartner also predicted that 70% of enterprises would abandon agentic-AI projects originating in FDE-led engagements within two years, citing high vendor costs and a lack of internal skills. Coqueiro warned that without internal ownership and an exit plan, an engagement can become ongoing staff augmentation and deepen vendor lock-in.

Microsoft and AWS are therefore selling more than access to AI infrastructure. Their reported investments indicate an effort to provide implementation capacity alongside it, while leaving customers to decide whether the resulting systems can be maintained independently.

This story draws on original reporting from CIO Dive.