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. Leaders of all kinds are recognizing that a successful AI program begins with an operational problem, and the sophistication of the AI needs to be matched to the scale of that problem.
That also means organizations must be willing to redesign their operations around what AI makes possible. Only then can clients and providers have a meaningful conversation about value and how to price it. For example, one enterprise client's finance team was struggling to get a single, accurate view of its cash position across dozens of markets and currencies.
Each region had recorded the same transactions differently for years, and leadership had spent more than five years trying to standardize the process without success. Before this, there was no credible baseline for measuring improvement, making it nearly impossible to agree on the value AI was expected to create.
A client’s business operations are often just as unsettled as the technology helping run them. The contracts holding the two together were built for a past world with more predictable staffing, fixed scope and stable demand.
There is no one right pricing model
The natural response to this uncertainty is to look for a new pricing model to replace the old one. But no single model solves the problem. Outcome-based, consumption-based, and AI-enabled pricing approaches each have advantages in the right circumstances and introduce new trade-offs. The challenge lies in creating enough flexibility to operate when effort, cost and demand no longer move predictably.
Outcome-based pricing works well when a result can be clearly defined and attributed. That gets much harder once AI-driven workflows start feeding into one another, making it difficult to trace which system or step contributed to a business result several automated stages downstream.
Consumption-based pricing ties cost to usage, which sounds fair until you run into the token paradox. While the price of a single AI query keeps falling, total spend keeps climbing anyway, because usage is no longer limited to one person typing one prompt. It's chains of AI agents running continuously in the background, each step triggering the next and including its own line item.
Take a lesson from a banking client running a 20-developer modernization program. Token costs per developer had climbed to roughly $1,000 a month. The team reduced that to $200 by routing simple work to lower-cost models, identifying usage patterns where token consumption was four times higher than the team average, and using deterministic automation for repetitive tasks where AI wasn't needed.
Gain-sharing models sound intriguing because the provider takes on more risk in exchange for a share of the outcome. However, they can become difficult to manage when both parties disagree about which improvements resulted from the provider’s work and how much credit each side deserves.
Why pricing flexibility is vital
These pricing models each assume either effort, cost or demand will remain predictable enough to price against. Increasingly, that is not the case.
While the market has not settled on a single approach, we believe hybrid structures have the greatest potential to become the preferred commercial model for AI-enabled managed services. A hybrid model combines a predictable base fee for core services, a variable component tied to AI consumption and outcome-based incentives where results can be accurately measured.
For CIOs, this approach provides greater budget predictability while offering some protection against consumption spikes. For providers, it helps manage AI consumption volatility while creating incentives for efficiency, continuous improvement and value creation.
Designing partnerships for uncertainty
The goal was never to find the perfect pricing model for AI, but rather to build commercial structures that can flex when conditions change again. That starts with determining clear baselines before any gain-share or outcome-based conversation begins.
It continues by intentionally building in scheduled points to recalibrate scope and pricing instead of being locked into the traditional three-year contract. Transparency around performance, costs, governance and AI outcomes also needs to be prioritized so that both sides trust the numbers.
We are still in the early innings of figuring this out. But it’s clear that AI is changing the role of the technology partner, from delivering capacity to helping enterprises manage uncertainty and accelerate business outcomes. Enterprises must build enough flexibility into their pricing models to adapt as the technology, demand and value equation changes.
While no one can perfectly price the future, we can design commercial models that evolve alongside the technology and the value it creates.
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