Why AI is upending traditional technology pricing

You don't need to understand the mechanics of AI tools to understand what's happening to traditional technology pricing.

AI is breaking the link between effort, cost and value that technology services have relied on for decades.

Imagine two managed service providers responding to the same production incident.

One assigns an engineer who spends five hours tracing logs, checking dependencies and isolating the root cause.

The other uses AI-enabled observability to identify the issue in minutes, trigger the appropriate remediation and restore service before users notice.

The second provider used fewer hours but delivered more value.

Should they be paid less? Enterprise technology services are running into the same pricing challenge, but on a larger scale.

The old pricing model equated more hours with higher cost and, in theory, greater value.

But today, AI is enabling organizations to move from reactive support to self-healing operations.

In our own client engagements, we're seeing auto-generated tickets fall by as much as 70%, roughly one in five operational events resolved autonomously, and up to 45% fewer major outages where predictive models are in place.

That should translate into straightforward cost savings, but the reality that CIOs and CFOs are grappling with is more complicated.

AI can deliver the same or greater value, with significantly less effort, which undercuts the assumptions behind what they are paying for.

Managed services providers are facing the inverse of this same problem: How do you price services when the traditional relationship between effort, cost and value no longer holds? Increasingly, predictability is replacing capability as the bigger challenge to address.

What senior leaders are saying about AI The challenges of identifying the right AI use cases are coming up in conversation after conversation with CXOs and increasingly with business leaders beyond IT.