AI Pricing and the pathway to a durable standard
Technology will deliver value, but establishing the baseline, financial modeling, and real-world success remains a work in progress.
Most homeowners discover they’re underinsured after the fire, not before it. The policy said “replacement cost,” but when the check arrives, it reveals something different. What happened between those two things was a set of definitions that were in the contract the whole time, written in language that few read until the claim is denied.
Many customer experience leaders are in a similar situation right now with their AI vendor contracts.
In the first two articles in this series I took a look at the problem from a higher altitude. First, seat-based pricing collapsing because AI agents doing work that the usual pricing measures have no standardized way to account for. Second, the margin problem underneath is structural: AI runs on compute, compute doesn’t produce software margins, and venture capital is covering the gap for now. In this piece, I want to come down to ground level. Specifically, to the conversation customer experience leaders are increasingly being asked to have: how do you frame AI returns honestly, without building a case on numbers that don’t yet have solid foundations?
The core tension is that while individual user AI productivity gains are real, the organizational value is often less apparent. Ninety-seven percent of executives report personal benefit from AI tools, but only 29% of organizations see significant ROI at the organizational level. That gap traces back to three assumptions embedded in most AI contract, not because vendors are obscuring them, but because the industry hasn’t yet settled on what the right units of measurement even are. Both sides are still working this out, often mid-contract.
The three open questions
When you sign an AI contract today, regardless of pricing model, you are making three commitments in areas where the industry hasn’t reached consensus. That’s not a criticism of vendors, rather it is a description of where we are.
The first open question is your vendor’s cost trajectory, and where it lands relative to your own usage growth. AI processing costs have fallen roughly 280 times in 18 months, and the optimistic case is that this continues. But every AI contract implicitly assumes something about where those costs land relative to your own usage growth. Agentic workflows, the kind that handle customer journeys end to end, use 5 to 30 times more processing per task than a simple chatbot. If your usage grows faster than your vendor’s costs fall, the economics get worse as you scale. Many buyers haven’t modeled this as a risk in their AI planning.
The second is how your own consumption patterns can be reliably modeled, on either side of the table. AI systems don’t use resources neatly. An agent that fails and retries uses compute whether it succeeds or not. The gap between pilot budgets and production reality is significant enough that 62% of organizations experienced major unexpected costs in their first year of scaled AI deployment. The projections were built on pilot behavior, not the messier reality of running at full scale.
The third is the varying definition of “resolved” in a real-world environment. This risk sounds like a detail but is actually the heart of the pricing problem. Under outcome-based pricing, you only pay for successful resolutions, which aligns incentives in exactly the right direction. The industry is generally moving here, and it’s the most promising development in AI pricing in two years. The broadly unresolved question is identifying, at scale, what a resolution is, and at what granular level resolution is achieved. In CX deployments, for example who decides when a customer issue counts as closed? If a customer contacts you again within 24 hours about the same problem, does the first interaction count? These aren’t gotchas buried in fine print, rather they’re genuinely hard operational questions that vendors and buyers are still working out together. The contract you signed reflects where that conversation stands today, which is incomplete. Having built value models for many enterprise CX deployments, I’ve seen this resolution question as the most challenging one in estalishing a success baseline.
A cautious reason for optimism
None of these issues are a reason to slow down. Pricing models are genuinely maturing. Eighty-four percent of CEOs now expect returns from AI to take longer than six months, which gives buyers real leverage to negotiate better terms. Organizations running AI in production for three or more years are already documenting a 25% reduction in cost per customer interaction. A workable equilibrium is forming albiet slowly, imperfectly, but visibly.
The key is to dial-in realistic definintions of success after a pilot implementation before launching a project to deploy AI.
The insurance analogy is worth revisiting. Two homeowners can have identical policies and walk away from a claim with very different expectations, because one understood what they were covered for before anything happened. That knowledge doesn’t change the policy. It changes what you do with it.
The same logic applies to your AI vendor relationship. The three open questions aren’t traps. They’re the natural byproduct of an industry that is still figuring out how to price something genuinely new. The customer experience leaders who navigate this well won’t necessarily have better contracts than everyone else. They’ll just have gone in with a clearer picture of where the gaps are, and that changes every conversation that follows.