Technology vendors are investing heavily in forward deployed engineering (FDE) teams, as providers compete to help enterprises challenged with delivering measurable outcomes from their AI implementations.
Amazon recently announced a $1BN investment to create a dedicated Amazon Web Services (AWS) Forward Deployed Engineering organization, while Microsoft has introduced the Microsoft Frontier Company, a new business unit focused on helping enterprises design, deploy and manage AI systems.
Francessca Vasquez, Vice President of Frontier AI Engineering and Services at AWS, wrote in a blog post:
“I have… heard loud and clear that many customers need expert AI engineers working directly with their teams to help them build and become AI-native organizations.”
The organization’s agentic-first approach aims to reduce project timelines “from months to days,” and leave customers self-sufficient when a deployment ends.
Similarly, Microsoft is investing $2.5BN in Microsoft Frontier Company, embedding 6,000 industry and engineering experts at customers to “co-design, co-innovate, deploy and continuously improve AI systems at scale based on measurable business outcomes,” Judson Althoff, CEO at Microsoft Commercial Business, wrote on the Microsoft blog.
Enterprises need to establish an intelligence platform that compounds their proprietary data, expertise, workflows and decision-making processes over time, and a trusted platform that allows them to govern and secure their AI solutions, according to Althoff. “Enterprise AI engineering expertise with deep industry knowledge is required to build a system that acts as a continuous loop of improvement between the two platforms.”
The announcements indicate that enterprise AI vendors are placing greater emphasis on helping customers bring AI into their day-to-day operations, as enterprises increasingly encounter the challenges of moving projects from proof of concept into production.
Enterprise AI Buyers Need More Than Access to Models
While foundation models have become widely available, and AI developers have moved on to frontier models, deploying AI in enterprise environments remains a complex task. Teams must integrate AI with existing systems, prepare proprietary data, establish governance frameworks and meet security and compliance requirements before they can deploy applications.
That implementation challenge is driving demand for FDEs, technical teams that work directly with enterprise customers.
For enterprise buyers, the growing investment suggests vendors increasingly recognize that successful AI adoption depends on deployment expertise as much as model performance.
As John Kim, Co-Founder and CEO of Delight.ai, told CX Today in an interview, enterprise buyers need to evaluate AI vendors not only on model capabilities but also on their ability to guide organizations in their deployments.
"It's really, really important to understand how a lot of these AI vendors are approaching rolling out into production.”
Organizations should assess whether vendors have established frameworks for evaluating AI readiness, proven processes for preparing enterprise data and the operational experience needed to deploy AI "as quickly and safely as possible," Kim added.
That will become increasingly important as enterprises begin deploying AI against sensitive business data. As Kim said:
"When you're dealing with massive amount of very important client customer data, you have to really invest in… the trust environment for adopting AI."
Enterprise customers are no longer evaluating AI vendors solely on the quality of their models, but also on their ability to implement AI securely, govern it effectively and deliver measurable business outcomes.
Technology Vendors Embrace Embedded Engineering
While AWS and Microsoft are among the latest companies to expand forward deployed engineering, Palantir Technologies helped establish the modern model following the launch of its Artificial Intelligence Platform (AIP) in early 2023.