Enterprise AI adoption has stalled at a familiar bottleneck: legacy systems, compliance requirements, data quality issues, and organizational workflow constraints that no general-purpose model solves alone. The FDE model addresses this by supplying scarce human expertise to customize deployments. The strategic shift is still unfolding—it remains unclear whether these companies can systematize FDE-driven deployments into repeatable infrastructure or whether they will remain locked into high-touch, consulting-style engagements as the primary path to enterprise adoption.
Attorneys advising AI vendors or enterprise customers should track this model's evolution closely. For vendors, the question is whether FDE-heavy deployment becomes a sustainable competitive advantage or a cost center that erodes margins. For customers, the risk is vendor lock-in through embedded technical relationships. The broader implication: enterprise AI may not follow the self-serve SaaS playbook. Instead, it could resemble earlier eras of bespoke enterprise software, where implementation expertise and customization were the real product—a shift with significant implications for pricing, competition, and contract structure.