Systems integrators (SIs) have been integral to IT projects for decades, providing consulting services and helping enterprises build and launch technology tools.
Now, as organizations move to deploy agentic AI, top large language model (LLM) providers are looking to get in on that action. A proliferation of Forward Deployed Engineer (FDE) services embeds AI experts directly into customer teams to help create, customize, and launch AI services.
For instance, this week, Microsoft launched a $2.5 billion venture, Microsoft Frontier Company, that the tech giant says “goes beyond” FDE, and Amazon Web Services (AWS) announced its own $1 billion investment into a new AWS FDE platform.
Both projects will integrate thousands of Microsoft and AWS engineers into customer environments to help them not only build AI tools, but learn essential skills to handle projects on their own going forward. Other big model players, including Anthropic, are also getting into the game with their own FDE services.
The gap between AI investment and ROI is growing, noted Thomas Randall, research director at Info-Tech Research Group, and organizations are under pressure to show production value from AI deployments.
This is the context in which vendor FDEs such as those from Microsoft and AWS will become relevant to “compress learning curves from their deep product knowledge, establish reusable processes, and build capabilities that can then be transferred,” he said.
Frontier transformation
Microsoft Frontier Company will place 6,000 experts with customers to “co-design, co-innovate, deploy and continuously improve” AI systems based on their specific business goals, Judson Althoff, CEO of Microsoft Commercial Business, wrote in a blog post.
The new offering focuses on what Microsoft calls “Frontier Transformation,” helping customers build an intelligence platform based on their proprietary data and internal expertise, workflows, and decision-making processes. Based on FinOps principles, the offering helps users “observe, govern, manage, and secure” AI tools across their stacks, and their intelligence compounds over time, Althoff said.
Microsoft Frontier Company is a “model-diverse, open, heterogeneous” platform, Althoff noted; customers can choose their own models: ChatGPT, Claude, Microsoft Copilot, or other open source or industry-specific models.
“Customers shouldn’t be locked into a single model any more than they should be locked into a single technology vendor,” Althoff noted. Further, he emphasized, customer data and IP are protected, and are not used to train Microsoft’s models.
The tech giant says it will leverage its SI and FDE partnerships with Accenture, Capgemini, EY, KPMG, PwC, and others to help scale the platform. Early users including London Stock Exchange Group (LSEG), Land O’Lakes, Unilever, and Novo Nordisk are already seeing “measurable outcomes,” Althoff said.
For instance, AI embedded into LSEG Workspace helps finance experts ask complex questions and get quick answers based on structured and unstructured financial data. The underlying foundation is “iteratively refined” through client feedback and real-time user testing, Althoff explained. This accelerates each cycle and improves model quality and scope.
This is the value of FDEs, he contended: “Enterprise AI engineering expertise with deep industry knowledge is required to build a system that acts as a continuous loop of improvement,”
Compressing timelines
Like Microsoft Frontier Company, AWS FDE embeds its experienced engineers into customers’ business, engineering, and security teams to help them build and launch agents purpose-built on their specific data, processes, and governance frameworks, AWS’ VP of frontier AI engineering and services Francessca Vasquez explained in a blog post.
“Unlike traditional consulting that assesses, recommends, and treats each deployment as a standalone project, AWS FDE builds for the long term,” she noted. Customers become “self-sufficient with AI,” moving from “observers to co-builders to autonomous operators” as they learn AI skills, workflows, and patterns that they can use to build AI going forward.
The platform is agentic-first and designed to compress timelines “from months to days,” and the derived business intelligence compounds to support future projects, Vasquez said.
Embedded engineers, many of whom build AWS AI services, verify and guide projects; AWS says it is also investing in training, tools, and resources for partners, to bolster the platform.
Customers gain access to runbooks, and architectural documentation, and a semantic layer connects to their data sources to create a knowledge graph that AI agents can reason over, Vasquez said.
She emphasized that domain expertise resides in the customer’s code, agents, and systems, so institutional knowledge does not get lost with employee turnover. Further, security tools provide hardware-based isolation and end-to-end encryption.
AWS FDE is not intended for those merely experimenting with AI, Vasquez noted, it is “built for organizations that have moved past experimentation and need production AI systems running real business processes.”
Still a market for SIs
SIs have enjoyed decades of high-margin relationships with their customers, so it makes “eminent sense” for hyperscalers to try to grab some of that business for themselves, noted technology analyst Carmi Levy.
“Both Microsoft and Amazon are aggressively looking for ways to tighten customer lock-in and open up more opportunities to get inside both their clients’ operations and decision making apparatus,” he pointed out.
In addition, Randall said, Info-Tech’s research reveals that 77% of organizations do not have a corporate-wide AI strategy. FDEs will address this by being narrow and specific to the customer’s working AI systems, reference architecture, runbooks, and other deliverables.
SIs, however, provide a different service, he said. Their relevance will be in broader integration knowledge across systems, managing change, and scaling programs. “Their deliverables will be more strategic and broader in scope.”
Of course, there is overlap, he said, and Microsoft will work closely with global SI partners. The investment gap and implementation complexity put hyperscalers under pressure to “provide more white-glove services to pull their customers along.”
Considerations for enterprises
Levy noted that, for customers who have already decided on a particular AI stack, these platforms may be worthwhile as long as they’re comfortable taking a single-vendor route.
“Assuming Microsoft and Amazon are price- and service-competitive with systems integrators, they may represent a compelling alternative,” he said. Still, using their services could come at a cost of potentially reduced choice, which could limit longer-term options.
It remains to be seen whether these types of platform are better for the customer or the vendor, and deliver more value than existing alternatives, he said, but the market will ultimately decide.
With that in mind, he advised IT decision makers to deep-dive not only into Microsoft’s and Amazon’s agentic delivery competencies compared to those of SIs, but into whether their underlying motivations are “truly in the customers’ best interests.”
Info-Tech’s Randall also advised enterprises to consider the output they’re looking for. FDEs will fast-track accurate builds on specific platforms they specialize in, while SIs will then help make the platform work across an enterprise context.
FDE options are best for organizations looking past AI pilots to quick, effective product buildouts, he said. SIs are needed when those organizations need to scale that pattern across messy enterprise processes.
Another factor to consider: “FDEs are not suitable for organizations still working on basic AI strategy questions or that want to remain cloud neutral,” said Randall.
This article originally appeared on CIO.com.