70% of Enterprises Expected to Abandon Vendor-Built Agentic AI Systems by 2028, Gartner Warns

Gartner Predicts Mass Abandonment of Vendor-Built Agentic AI

Gartner has issued a stark warning for enterprises investing in agentic AI: by 2028, 70% will abandon agentic AI systems built with vendor assistance, trapped by soaring costs and unable to evolve the technology independently1

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. This prediction centers on what the consultancy calls forward-deployed engineering (FDE), a model where vendor engineers embed directly with customers to build and deploy solutions for specific requirements. While FDE can deliver rapid early progress, Gartner argues it often leaves customers dependent on expensive external expertise without building the internal capabilities needed for long-term success.

The Forward-Deployed Engineering Trap

The core issue with forward-deployed engineering lies in its structural design rather than technical execution. According to Mukul Saha, Senior Director Analyst at Gartner, "FDE success starts with getting the engagement structure right, from scope and incentives to governance, ownership, and exit"2

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. FDE engagements frequently fail when customers do not acquire the knowledge and control necessary to maintain and develop systems after vendors leave. The best-scoped FDE engagements establish clear guidelines on governance, business value delivery, intellectual property ownership, project co-ownership, knowledge transfer, and exit strategies from day one1

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Rising Phenomenon of FDE Washing

Gartner warns of a growing risk called "FDE washing," where conventional consulting services are rebranded as specialized forward-deployed engineering. Many providers now use "forward deployed" as a label for AI implementation, professional services, solution engineering, or AI consulting—some thoughtfully, others simply because it sounds more strategic1

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. Some vendors charge premium fees without the delivery depth, program management, or change management maturity to justify them. This trend means customers may see faster early progress but fail to build internal expertise, ultimately paying premium rates for work that traditional services or partner models could deliver more cost-effectively with predictable results2

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Limited Product Integration and Recurring Customer Requirements

Gartner predicts that through 2028, fewer than 20% of FDE engagements will turn recurring customer requirements into features in the vendor's core product1

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. This statistic exposes a fundamental disconnect: while FDE is positioned as a strategic partnership, most engagements fail to influence vendor product roadmaps or create reusable capabilities that benefit the broader customer base. Software engineering leaders face a choice between continuing expensive custom implementations or abandoning systems that cannot evolve with their business needs.

Three-Phase Solution for Enterprise AI Adoption Challenges

To address high costs and dependency issues, Gartner recommends software engineering leaders adopt a three-phase approach. Before signing, use FDE only for problems requiring deep product expertise, rapid adaptation, or close integration between vendor technology and your operating environment. Identify an executive sponsor accountable for business outcomes, not just implementation budgets, and establish key contractual requirements including deliverables, knowledge transfer, intellectual property rights, and transition responsibilities2

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. During the engagement, embed FDEs with internal domain experts, engineers, and end users so critical knowledge is shared. Establish iterative business validation that evaluates technical performance and the operating model required to scale AI responsibly, including decision rights, autonomy versus human oversight balance, and governance mechanisms2

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. At exit, execute the exit strategies established at the start rather than extending engagements because internal teams are not ready. Success is measured not by AI implementation completion, but by the enterprise's ability to independently manage, optimize, and scale the technology2

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Broader Pattern of AI Project Failures

This warning aligns with Gartner's previous forecasts about generative AI projects and AI agent deployments. Earlier this year, the firm predicted that at least half of generative AI projects will exceed budgets due to poor architectural choices and lack of operational know-how, while most organizations attempting custom models will abandon them for similar reasons1

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. Gartner also expects 40% of AI agent deployments would be scaled back or decommissioned as organizations encounter governance problems. More recently, the analyst predicted nearly a third of employees laid off because of AI will need rehiring at significantly higher cost1

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. These predictions paint a sobering picture of enterprise AI adoption challenges, where enthusiasm often outpaces organizational readiness and vendor promises exceed delivery capabilities.