Gartner Predicts 70% of Enterprises Will Abandon Agentic AI Built by Vendor Forward-Deployed Engineering by 2028

By 2028, 70% of enterprises will abandon agentic AI built by vendor forward-deployed engineering (FDE), trapped by soaring costs and unable to evolve it on their own according to Gartner, Inc., a business and technology insights company. Vendor FDE is a model in which vendor engineers work directly with customers to build and deploy solutions.

“FDE success starts with getting the engagement structure right, from scope and incentives to governance, ownership, and exit,” said Mukul Saha, Sr Director Analyst at Gartner. “The best-scoped FDE engagements have clear guidelines on governance, business value delivery, IP ownership, project co-ownership, knowledge transfer, and an exit strategy from day one.”

FDE engagements often fail structurally before they fail technically. To be successful, software engineering leaders must follow three tenets of the FDE model (see Figure 1). The outcome is shaped by decisions made before contracting, during delivery, and at transition, when software engineering leaders still control scope, incentives, governance and ownership.

Phase 1: Before Signing

Software engineering leaders must use FDE only for problems that require deep product expertise, rapid adaptation, or close integration between vendor technology and your operating environment.

“Identify an executive sponsor who is accountable for business outcomes, not simply the implementation budget,” said Saha. “Establish key contractual requirements beyond procurement's scope, including deliverables, knowledge transfer, intellectual property rights and transition or exit responsibilities.”

Phase 2: During the Engagement

Embed FDEs with internal domain experts, engineers, and end users so that critical knowledge is shared and the solution reflects how work actually gets done.

“Establish a cadence of iterative business validation that evaluates not only technical performance but also the operating model required to scale AI responsibly,” said Saha. “Use each increment to define decision rights, determine the appropriate balance between autonomy and human oversight, clarify how confidence and exceptions are communicated and build the governance mechanisms that sustain user trust and accountability.”

Phase 3: Exit and Prove Independence

Execute the exit plan established at the start rather than extending the engagement because internal teams are not ready.

“Ensure the organization develops the capabilities, governance, and operational ownership required to independently sustain and evolve the solution over time,” said Saha. “Success is measured not by implementation completion, but by the enterprise’s ability to manage, optimize, and scale the technology, including adapting human-AI decision models as business processes, priorities, and risk profiles evolve.”

FDE Must Be Viewed as a Delivery Model

The underlying pattern during these three phases is familiar from enterprise software: technically strong, product-literate people placed close to the customer. What is new is the intensity of the hype.

“Many providers now use ‘forward deployed’ as a label for implementation, professional services, solution engineering, or AI consulting; some thoughtfully, others because it sounds more strategic,” said Saha. “Some charge premium fees without the delivery depth, program management, or change management maturity to justify them.”

Customers may see faster early progress but fail to build internal capability and end up paying premium rates for work that, once the scope is clear, a traditional services or partner model could potentially deliver more cost-effectively and with more predictable results

Because of this, Gartner predicts that through 2028, less than 20% of FDE engagements will turn recurring customer needs into capabilities in the vendor’s core product, exposing a growing risk of “FDE washing,” where consulting services are marketed as FDE.