Gartner: 70% of Enterprises Will Abandon Vendor-Built Agentic AI by 2028 Due to Rising Costs

Gartner Predicts Mass Abandonment of Vendor-Built Agentic AI

Gartner has issued a stark warning about the future of agentic AI implementations, predicting that by 2028, 70% of enterprises will abandon agentic AI systems built through vendor forward-deployed engineering (FDE)1

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. Organizations will find themselves trapped by soaring costs and unable to evolve the technology on their own. This forecast underscores mounting AI adoption challenges as enterprises struggle with vendor dependency and the limitations of current implementation models.

Forward-deployed engineering embeds a vendor's engineers directly with customers to build and deploy solutions tailored to specific requirements3

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. While this model can deliver rapid early progress, it often leaves customers dependent on expensive external expertise. According to Mukul Saha, Senior Director Analyst at Gartner, FDE engagements frequently fail when customers do not acquire the knowledge and control needed to maintain and develop systems after the vendor leaves1

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High Costs and Dependency Issues Drive Abandonment

The prediction reflects a broader pattern of AI implementation struggles. Enterprises abandon agentic AI primarily because they fail to build internal capabilities during vendor engagements. Software engineering leaders end up paying premium rates for work that traditional consulting services or partner models could deliver more cost-effectively and predictably2

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. Organizations see faster early progress but cannot sustain or evolve solutions independently once vendors exit.

Gartner also forecasts that through 2028, less than 20% of FDE engagements will turn recurring customer requirements into capabilities in the vendor's core product1

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. This exposes a growing risk of FDE washing, where conventional consulting services are marketed as something more specialized and strategic without delivering the depth, program management, or change management maturity to justify premium fees2

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Three-Phase Framework for Successful FDE Engagements

Saha emphasizes that FDE success starts with getting the engagement structure right from day one. "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," he stated2

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. FDE engagements often fail structurally before they fail technically, requiring software engineering leaders to follow three critical phases.

Before signing, organizations must use FDE only for problems requiring deep product expertise, rapid adaptation, or close integration between vendor technology and operating environments3

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. Leaders should identify an executive sponsor accountable for business outcomes and establish key contractual requirements including deliverables, knowledge transfer, intellectual property rights, and exit strategies. During the engagement, embed FDEs with internal domain experts, engineers, and end users so critical knowledge is shared2

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. Establish iterative business validation that evaluates technical performance and the operating model required to scale AI responsibly.

The exit phase proves most critical. Organizations must execute the exit plan established at the start rather than extending engagements because internal teams aren't ready3

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. Success is measured not by AI implementation completion but by the enterprise's ability to manage, optimize, and scale the technology independently.

Broader Pattern of AI Project Failures

This warning aligns with Gartner's earlier predictions about generative AI projects and AI agent deployments. The firm previously forecast that at least half of generative AI projects will exceed budgets due to poor architectural choices and lack of operational know-how1

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. Most organizations attempting to build custom models will abandon them for similar reasons. Gartner also expects 40% of AI agent deployments will be scaled back or decommissioned as organizations encounter governance problems.

The underlying pattern is familiar from enterprise software: technically strong, product-literate people placed close to customers. What distinguishes current FDE models is the intensity of hype surrounding them2

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. 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.

What Organizations Should Watch

Enterprises must scrutinize vendor promises and insist on structured knowledge transfer from the outset. The shift from vendor-built agentic AI to internally managed systems will require organizations to develop capabilities, governance, and operational ownership before vendors exit. Software engineering leaders should evaluate whether traditional services or partner models might deliver better long-term value than premium-priced FDE engagements. As FDE washing becomes more prevalent, distinguishing genuine forward-deployed engineering from rebranded consulting services will prove essential for avoiding costly dependencies and abandoned AI implementations.