A research report from Orient Securities Company Limited indicates that the commercial value of enterprise AI delivery capabilities is becoming increasingly apparent, with leading model providers integrating deployment capabilities into the core of enterprise AI commercialization. FDE is expected to become a critical delivery layer between models, cloud platforms, and enterprise applications. As large model capabilities continue to strengthen and Agents evolve, demand for FDE is also anticipated to accelerate. The firm believes that amidst the ongoing development of large models and Agents, FDE has emerged as a key link for enterprises to implement AI capabilities, and its demand will likewise see a rapid uptick.
The commercial value of enterprise AI delivery capabilities has already been demonstrated in Palantir's performance. FDE, or Forward Deployed Engineer, was pioneered by Palantir. Palantir's AIP platform can integrate models from multiple vendors including OpenAI, Anthropic, and Google, with its competitive edge largely stemming from the combination of the Ontology platform and the FDE delivery system. FDEs delve deep into client operations, connecting models to enterprise data, business logic, permission frameworks, and operational workflows, while handling development, testing, and continuous optimization around specific business objectives. Palantir's recent financial reports showing simultaneous high revenue growth provide supporting evidence for the commercial value realization of this model.
OpenAI has established a Deployment Company, signaling that leading model providers are placing deployment capabilities at the core of enterprise AI commercialization. In May 2026, OpenAI announced the formation of its majority-owned Deployment Company and reached an acquisition agreement with Tomoro. The transaction's completion remains subject to regulatory approvals and other conditions, and is expected to bring in approximately 150 FDEs and deployment experts. The company has secured over $4 billion in initial funding and has partnered with 19 investment institutions, consulting firms, and system integrators, with its collaborators covering more than 2,000 portfolio companies. Specific projects will begin with the selection of high-value workflows, where FDEs will handle system design, development, testing, and deployment within client organizations, connecting OpenAI models to customer data, tools, access controls, and core business processes. The substantial capital investment and direct deployment team allocation by model vendors reflect that enterprise clients' focus on AI has expanded to encompass system integration, day-to-day operations, and quantifiable business outcomes. Token procurement volumes and POC counts are no longer sufficient metrics to gauge genuine enterprise AI adoption, with deployment and delivery emerging as the primary bottleneck.
FDE is poised to become the crucial delivery layer between models, cloud platforms, and enterprise applications, with commercial value driven by both deployment revenues and resource consumption. Enterprise AI production systems must simultaneously address private data integration, model evaluation, system integration, security compliance, permission governance, and organizational coordination—challenges that enhanced model capabilities cannot automatically resolve. Through on-site development and continuous feedback, FDEs convert client requirements into connectors, evaluation sets, industry templates, tool interfaces, and platform features. These reusable assets help shorten subsequent deployment cycles and drive sustained consumption of model calls, cloud resources, databases, and security governance services. Business models can simultaneously encompass project delivery fees, software subscriptions, usage-based billing, and outcome-linked pricing. As large model capabilities continue to strengthen and Agents evolve, FDE demand is also expected to accelerate.
Risks include intensifying industry competition and commercialization progress falling short of expectations.