The “Tesla and Waymo-fication” of Pricing Models

The way enterprises buy and consume technology and IT services is undergoing a structural shift. As AI becomes increasingly embedded across the deal lifecycle, enterprise buyers are reassessing traditional FTE-led commercial models and seeking pricing approaches that better reflect delivered value, realized productivity, and enabled autonomy.

Over the past six months, several buy-side enterprises have either started transitioning away from pure labor-capacity models or expressed a clear intent to do so. Traditional constructs such as Time and Material, staff augmentation, and output-based pricing are increasingly giving way to  platform-driven and value-linked commercial models that explicitly account for AI’s growing role in delivery. This evolution marks a fundamental change in work economics, where delivery platforms, intelligent automation, and agent orchestration reshape how services are priced, governed, and measured.

This Everest Group Viewpoint introduces a practical AI-first maturity ladder for service delivery, spanning AI-augmented, AI-accelerated, semi-autonomous, and supervised full autonomy operating bands. The framework complements familiar commercial constructs such as Resource Unit- and POD-based pricing while enabling gainshare and outcome-based incentives as AI-driven autonomy increases.

The report further examines what optimal pricing approaches may look like for buyers and providers based on market maturity, enterprise readiness, and commercial objectives. It highlights a real-world example of a global buy-side enterprise institutionalizing an autonomy-based commercial model and the benefits such a transition can unlock.

Finally, the Viewpoint provides actionable recommendations for buyers and providers transitioning from traditional labor-based pricing toward AI-native, value-led commercial models.