Gartner predicts that 70% of enterprises will abandon vendor-led agentic AI by 2028 due to ballooning costs and lack of internal operational control. For investors, this highlights a potential sustainability risk in the AI implementation revenue models currently pursued by many global IT service providers.
Gartner has released a stark forecast for the enterprise AI market, predicting that 70% of businesses will move away from vendor-deployed agentic AI solutions by 2028. The research firm identifies a growing trend of 'FDE washing'—or Forward-Deployed Engineering washing—where service providers charge premium consulting fees for standard AI implementations that fail to deliver long-term business value.
For investors in the technology and IT services sector, this report serves as a critical indicator of the risks embedded in current AI growth narratives. Many IT service providers currently position their AI transformation services as a major revenue driver, banking on long-term client dependence for ongoing system management. However, the Gartner analysis suggests that this model may be structurally flawed. When companies hire vendors to build and manage AI systems without transferring the necessary intellectual property or internal expertise, they often find themselves locked into expensive, inefficient service contracts that provide diminishing returns.
The Risk of High Vendor Dependence
The fundamental issue highlighted is not a technical failure, but a governance and operational one. Many enterprise AI partnerships collapse because companies fail to establish a clear exit strategy or a path to internalize the technology. Instead of building core product capabilities that the business can manage independently, organizations often rely on vendor engineers who remain outside the company’s own operational teams. Gartner estimates that less than 20% of current FDE engagements will successfully translate into core internal capabilities by 2028, implying a significant risk of contract churn as businesses realize they are paying for AI systems they cannot truly control.
Investor Implications for the IT Sector
This trend creates a complex environment for IT services companies. While the initial wave of AI implementation is driving immediate revenue, the long-term sustainability of these contracts will depend on the value delivered. If clients begin to see these services as 'FDE washing' rather than genuine digital transformation, they are likely to cut spending or shift toward platforms that emphasize self-sufficiency and internal capability building.
Investors may want to monitor how IT service providers structure their AI contracts moving forward. Companies that focus on training client teams, transferring knowledge, and integrating AI into the client's own IT infrastructure may build more resilient, albeit perhaps slower, revenue streams. Conversely, firms that rely heavily on 'open-ended' service engagements—where the client remains indefinitely dependent on the vendor for basic system maintenance—could face a sharp correction if corporate spending budgets tighten or if clients demand better ROI for their AI investments.
Ultimately, the success of enterprise AI will likely be measured by the client's ability to operate these systems independently. The next important update for shareholders will be the trend in 'AI project retention' rates and whether service providers can prove that their deployments are leading to permanent efficiency gains, rather than just shifting costs from internal balance sheets to recurring vendor fees.