Enterprise IT leaders have always struggled with AI pricing, especially the need to pay for AI in a way that delivers ROI. But the typical IT exec may not be right person to decide how a company uses AI — and how it tries to deliver ROI — because so many line-of-business workers and partners are now experimenting with the technology on their own.

And if IT leaders don’t have a grip on how they want to use AI over the next year or two, it’s impossible to figure out how they want to pay for it. They likely hate the current method of paying per token. And other options, such as SAP’s push to charge per AI task completed, aren’t any better. 

To use a sales analogy, IT doesn’t want to pay a lot of money for leads, because there’s no way to know if those leads will generate any revenue — let alone how much. What IT leaders want is the tech equivalent of paying commission, where they only pay when a lead converts into a paying customer. And even then, they only pay a percentage of the final sale. That guarantees ROI for the enterprise.

The problem: no AI vendor would ever go for it because that approach puts too much risk on them. 

Finding a pricing model that works for both enterprise IT and AI vendors is all but impossible as long as IT is trying to deliver ROI.

Irfan Khan, president of SAP Data & Analytics, said the problem is challenging for both sides. “Everyone is scrambling to justify their investments,” and “the day one cost is not necessarily the day one value,” he said.

The problem is one of sequence. Pricing has to be negotiated and locked in long before a project starts. But with technology as new and experimental as agentic AI, there’s almost no solid information about what benefits it will (or will not) actually deliver. 

Beyond that, generative AI (genAI) and agentic AI systems might well deliver benefits that are harder to jot down in a spreadsheet. Let’s say the CFO wants to see a sharp rise in order fulfillment. But what if AI “manages to fulfill those orders more efficiently,” Khan said. “And what are the likely ripple effects of bringing more efficiencies into the process?”

Justin Greis, CEO of consulting firm Acceligence, frames the AI pricing disconnect in terms of market economics:

“The market is trying to force-fit AI into infrastructure-era pricing models, when AI is fundamentally closer to labor augmentation and business process transformation than compute consumption,” Greis said. “The core disconnect is: Enterprise IT buyers want pricing aligned to realized business value. AI vendors want pricing aligned to resource consumption and platform utilization. Those are very different economic models. 

“Token pricing is attractive to vendors because it is measurable, scalable, and predictable. But from the enterprise perspective, tokens are almost meaningless as a business metric. Nobody on the CFO side cares how many tokens were consumed if the process improvement never materialized.”

The competing pricing strategies overwhelmingly rely on just two factors: what delivers the most profit and which is the easiest to execute. Given human nature, the latter is usually the path most often taken.

It’s like one of my favorite jokes. A guy is heading to his car when he sees a man with a flashlight intently looking at the ground right next to a streetlight pole. 

“Can I help you? Are you looking for something?” the guy asks.

“Yes, I lost my car keys.”

“Silly question, but where do you last remember having them?”

“I was standing over there in that dark alley up the street. A cat screeched and I dropped my keys.”

“Wait a second — if you lost your keys over there, why are you looking here?”

“The light’s better over here.”

The lesson: taking the easy route usually beats realizing the actual objective.

Greis argued that not only would it be hard to persuade AI vendors to accept ROI pricing, but if they did  somehow agree, the unintended results could prove disastrous. 

“AI vendors cannot realistically absorb unlimited downstream business risk tied to variables they don’t control — poor internal adoption, broken processes, bad data, organizational politics, weak change management, or unclear KPIs. But the moment vendors are compensated primarily on outcomes, you create strong incentives for increasingly autonomous optimization behavior. That sounds great until organizations realize that AI systems may pursue the metric rather than the intent behind the metric,” Greis said. 

“We’ve already seen versions of this in recommendation engines, ad targeting systems, and engagement algorithms. The system learns to maximize the measurable outcome even if the methods become operationally risky, ethically questionable, reputationally damaging, or strategically misaligned. In enterprise environments, that could become dangerous very quickly. An AI system incentivized around reducing service costs might aggressively deflect legitimate customer issues. A model rewarded for sales conversion could push manipulative messaging or optimize for short-term wins at the expense of customer trust. A procurement optimization engine might lower costs while quietly increasing supplier concentration risk or degrading operational resilience.

“The more autonomous these systems become, the harder it is to separate ‘successful outcome’ from ‘acceptable behavior.’”

The best way to resolve this is potentially the most difficult. Every AI project must be approved by an AI committee whose members must ask the hard questions. What are you hoping to accomplish? If it works, specify and quantify your best-case scenario benefits. What are the most likely ways it could fail? What are the costs and disruptions most likely to happen if it fails in that way? Quantify those. 

The committee should have at least a couple of members who know exactly what these models can and cannot do to serve as a reality check. 

Next, require the LOB chief, or whoever the most senior exec involved in the project is, to share in the pain. Tie gains or losses to executive bonuses. Give those execs a reason to make sure their people are honestly and creatively thinking the project all of the way through. 

Only once that happens can a CIO know how to negotiate a fair and reasonable AI pricing deal.