OpenAI is now selling agents like a consulting firm, and there's a Palantir-shaped reason
Curated by the Inblix editorial team
OpenAI is abandoning its self-serve roots for a new enterprise product called Presence, a managed service delivered by the company’s own Forward Deployed Engineers. Announced July 22, it’s a clear admission that connecting a powerful model to a messy enterprise is a labor problem, not just a software one. The company is betting that sending its own people into the trenches—scoping business outcomes, navigating legal review, and managing staged rollouts—is the only way to stop the failure rate Gartner predicts will claim over 40% of agentic AI projects by 2027.
The offering treats each deployment as a project, starting with a single, tightly-scoped job like a billing dispute. The customer writes the rules for escalation and sign-off, and a feedback loop called Codex analyzes production sessions to suggest guardrail tweaks. It’s a pragmatic response to the reality that governance and operational discipline, not model capability, are what kill enterprise AI initiatives. OpenAI’s own phone support line, 1-888-GPT-0090, is the proof point, and the company claims the agent there now resolves 75% of inbound issues without a human.
But the model’s constraints are as interesting as its promise. Access isn’t gated by a credit card but by “delivery capacity,” which is a consulting bottleneck, not a compute one. Borrowing the Forward Deployed Engineer title from Palantir signals a move into high-touch, people-heavy implementation work. That creates an awkward overlap with the systems integrators OpenAI also needs to scale, an arrangement that works at low volume and gets complicated fast. It also blurs the lines of accountability when the model vendor is also the team plugging it into your core systems.
Early customers are kicking the tires rather than running at scale. BBVA is exploring voice support in Mexico, SoftBank is testing Japanese-language conversations, and IAG is looking at high-demand event support. Daniel Ordaz from BBVA describes the bank as a “design partner” helping shape the product, which is honest language for a product that’s still being finished in the field. The managed model is a sensible fix for a real problem—it’s also a bet that OpenAI can master a services business before the integrators it’s competing with figure out the technology.
💡 Key Takeaways
- OpenAI's move to a managed services model directly tackles the governance and integration failures Gartner predicts will kill over 40% of enterprise AI projects by 2027.
- The product's scale is gated by the availability of highly-specialized engineers, not API capacity, meaning it can't grow with the exponential curve of a pure software product.
- By being both the model vendor and implementation partner, OpenAI blurs accountability for production failures in a way enterprises will need to address in contracts.
- The listed customer engagements are early-stage explorations, suggesting the product is further from broad enterprise readiness than the 'limited GA' label implies.
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