Artificial intelligence is fundamentally transforming the way managed services are delivered and priced. Historically, service providers have been compensated based on metrics like headcount, hours worked or tickets resolved, where more effort translated directly into more revenue. With AI increasingly solving problems autonomously and reducing manual intervention, this traditional model is rapidly becoming outdated. This shift brings both challenges and opportunities. Clients are now prioritizing measurable business outcomes: higher productivity, improved uptime and reduced costs. As AI enables these results with less human effort, conventional pricing structures start to lose relevance. Organizations are responding by exploring innovative approaches, such as performance-based and outcome-driven commercial models. These new frameworks align the interests of both clients and providers, rewarding genuine value creation instead of simply activity. In practice, we see AI delivering substantial benefits in environments like service desks, cloud operations and manufacturing; from lowering incident rates and operational costs to enhancing user experience and overall performance. The emergence of new commercial models ensures that both clients and providers can share in these improvements. However, the most significant challenge is not determining the right pricing but establishing robust governance. It’s essential for organizations to define how outcomes are measured, how value is calculated and how benefits are distributed. Leading providers are already transitioning to AI-enabled platforms and contracts focused on results rather than labor. As automation continues to reshape the industry, the future of managed services will hinge on collaborative value creation and sharing. The central question for leaders becomes: when AI reduces the workload but amplifies the value, how should these benefits be shared between client and provider?