AI Support Woes Highlight Need for Clearer Service Level Agreements, Says Gradient Labs CEO
London, UK – June 30, 2025 – As organizations increasingly integrate artificial intelligence into their operations, a growing concern is emerging around the reliability and support of these sophisticated systems. In a recent interview with The Register, Adam Elman, CEO of AI development and deployment firm Gradient Labs, has voiced a strong opinion: businesses should not be expected to pay for AI support failures.
Elman’s remarks come at a time when the rapid adoption of AI technologies is outpacing the development of robust and universally understood support frameworks. Many companies are investing heavily in AI solutions, expecting them to function seamlessly and deliver on their promised benefits. However, when these systems falter, the experience of seeking and receiving adequate support can be frustrating and, crucially, costly.
The core of Elman’s argument centers on the principle of accountability. He suggests that if an AI solution is provided as a service or product, the vendor or developer should be responsible for its ongoing performance and for rectifying any issues that arise. Charging clients for support when the AI itself is not performing as expected creates a disconnect, potentially leaving businesses paying for services that are not being delivered effectively.
“When you purchase a complex system, you expect it to work, and if it doesn’t, the responsibility for fixing it should lie with the provider, not the customer,” Elman stated in the interview. He further elaborated that the current landscape for AI support is often ambiguous, with unclear service level agreements (SLAs) that may not adequately cover the unique challenges presented by AI systems.
Gradient Labs, as a company focused on enabling businesses to harness the power of AI, understands the critical importance of reliable performance and responsive support. Elman’s comments likely stem from observing firsthand the difficulties many companies face when their AI deployments encounter unexpected problems. These issues can range from subtle performance degradation to outright system failures, impacting productivity, decision-making, and customer experiences.
The CEO’s call for a more transparent and accountable approach to AI support is a timely one. As AI becomes more deeply embedded in critical business functions, the need for clear expectations and contractual obligations regarding its maintenance and troubleshooting is paramount. Businesses need assurance that their AI investments will be supported, not just when things are going well, but especially when they are not.
Elman’s perspective suggests a broader industry conversation is needed. This includes defining what constitutes an “AI support failure,” establishing robust SLAs that address AI-specific complexities, and ensuring that vendors are incentivized to deliver high-quality, reliable AI solutions. Ultimately, the goal is to foster trust and confidence in AI technologies, allowing businesses to fully realize their transformative potential without the added burden of paying for their own system’s shortcomings.
The Register’s reporting on Elman’s insights highlights a crucial aspect of the ongoing AI revolution: the evolution of the supporting infrastructure and the business models that underpin it. As the technology matures, so too must the expectations and practices surrounding its deployment and ongoing operational success.
Don’t pay for AI support failures, says Gradient Labs CEO
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