The only thing worse than high costs is high, unpredictable costs, a reality that the current state of enterprise AI has driven home painfully to IT decision-makers. And it seems like the challenge is only going to get worse, no matter what aspect of AI you’re talking about.

For a white paper on the top enterprise communications/collaboration/CX tech issues in 2027, I recently spoke with Kevin Kieller, analyst and consultant with Enable UC. (You can find the full white paper here.) Kevin is one of the sharpest analysts of comms/collab AI pricing, and he told me pricing/spending for GenAI-driven tools will likely continue to bedevil enterprise decision-makers.

With AI features in the mix, many vendors are moving away from pure flat-rate pricing for cloud communications services, in favor of a hybrid model featuring a base per-seat cost supplemented with tiered pricing that meters use of AI “credits,” Kevin explained. Vendors position these tiers as an answer to the problem of runaway spending on per-token pricing, since the enterprise decides what volume of AI credits to purchase. But Kevin questions how much predictability these structures really provide. They may establish boundaries on spending – however, potentially at the cost of restricting employee productivity. If an employee exceeds their monthly AI credit allotment, either their work is sidelined, or you have to move them up into higher tiers, which essentially returns you to the problem of unpredictable cost.

The bottom line, Kevin told me: “The vendors have shifted the risk entirely to the customers.”

And the struggle is ongoing. Citing a recent report from Splunk, CIO Dive recently reported that over half of enterprises say they can’t control AI costs, and that over half also report “unexpected AI-related billing spikes create operational strain.”

Management vendors are responding to the challenge with observability and accountability features targeting AI costs, according to CIO Dive. These are especially important as AI usage expands out to more complex agentic AI workflows. Such scenarios may also require tools that can optimize agentic processes’ use of multiple sizes and types of models, in an echo of networking principles of least-cost routing. Management tools may also track usage more granularly, to facilitate departmental chargeback.

This last principle may become increasingly important, Kevin Kieller told me. AI has forced IT decision-makers ever deeper into the weeds of business processes, and while it’s important to understand how the business works, ultimately it’s up to these departments to figure out how extensively to use AI in pursuit of their goals, Kevin said. Where possible, IT should provide the collaboration platform with baseline AI capabilities like transcription, and leave it up to each department to specify which add-on AI features it needs to do its job, and pay for this out of its own budget.

The AI industry writ large is anything but settled, so pricing and business models will remain in flux. Predictability in cost to the enterprise will be critical, but likely also elusive.

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