AI Bandwidth Commitments Are the New Migration Circuits
Networking teams across enterprise IT are provisioning bandwidth right now — committing to carrier circuits, cloud interconnects, and data center cross-connects — for AI workloads that do not yet exist at production scale.
The provisioning is happening before the workloads are deployed. The contracts are being signed before the actual usage patterns are known. And in most enterprises, no one has been designated to reconcile the committed bandwidth against actual AI traffic once the deployment goes live.
The forecasts driving these decisions are real. Goldman Sachs projects global data center power demand up 50% by 2027, driven by enterprise AI inference moving out of hyperscaler training environments and into hybrid and on-premises infrastructure. Dell’Oro projects rising demand for near-edge connectivity and high-speed private interconnects to support those workloads.
What the forecasts do not capture is the accountability gap behind them — and the pattern Bearstone has watched compound across enterprise carrier portfolios for the last decade. The category is new. The governance gap is not.
The pattern enterprises have already lived through, applied to a new workload
Every prior generation of network architecture has produced the same accountability gap. MPLS migration circuits were provisioned for peak load and never rightsized. SD-WAN bandwidth tiers were committed at deployment and never validated against actual usage. Cloud interconnect commitments were sized for projected growth and never reconciled when the projection missed.
In each case, the carrier had no incentive to surface the variance. The committed bandwidth was billed monthly. The variance from actual usage stayed inside the line items. And in the absence of a designated owner inside the enterprise, the gap compounded — quarter after quarter, renewal after renewal — until someone pulled the contracts and the invoices apart and looked at them line by line.
AI infrastructure is now entering that same pattern, with three additional complications.
Three things that go wrong with AI bandwidth commitments
First, the committed bandwidth almost always exceeds actual production usage. AI workloads are over-provisioned at deployment because the cost of an under-provisioned circuit is a model timeout, and the cost of an over-provisioned circuit is invisible until someone validates the bill. Networking teams optimize for availability. The bandwidth commitment, once signed, sits at peak-load pricing whether or not the traffic ever materializes.
Second, the actual AI traffic profile rarely matches the contracted profile. Inference workloads are bursty — they look nothing like the sustained throughput patterns most carrier circuits are priced against. Enterprises end up paying for a sustained-throughput commitment to support a workload whose real exposure is latency, packet loss, and occasional burst. The contract terms permit the carrier to bill for what was committed. They do not require the carrier to flag the mismatch.
Third, the cloud interconnect and cross-connect fees compound alongside the carrier circuit commitments. AI workloads typically traverse a carrier circuit, a cloud interconnect, and a data center cross-connect — three separate billing relationships, each with its own commitment terms, its own validation requirements, and its own variance pattern. Most enterprise IT teams have a single owner for one of those three. The other two go unwatched.=
The accountability question
Inside most enterprise telecom environments, the accountability question for AI bandwidth has not been answered. Networking owns provisioning. Procurement owns the contract. Finance owns the budget. None of them own monthly reconciliation against the contracted commitment.
In the absence of that ownership, the carrier will bill for the commitment, and the enterprise will pay it. Not because the enterprise agreed to overpay, but because no one was assigned to validate the difference between what the contract permits and what the traffic actually requires.
This is the governance gap that has produced the largest unauthorized charge exposures Bearstone has identified across enterprise telecom portfolios — not in AI yet, but in every prior generation of bandwidth commitment that followed the same pattern.
The governance principle
Validation has to be assigned. It does not happen by default.
The principle is the same across every generation of network architecture. Contracted commitments require monthly reconciliation against actual usage, line by line, against contract terms. Variance gets disputed when it appears, not after it compounds for thirty-six months. The enterprises that get this right are the ones that designate the owner before the deployment goes live — not after the first invoice arrives that nobody recognizes.
AI bandwidth commitments are about to become the largest unmanaged contracted spend category in enterprise telecom. The provisioning is happening now. The reconciliation is not. The exposure compounds in the gap between the two.
