SPONSORED CONTENT PRESENTED BY NEXCESS
- The cloud contracts most community and regional banks are running on today were designed for predictable, transactional workloads. AI does not work that way.
- Every bank now runs AI in some form. Most of those deployments are sitting on infrastructure agreements that did not anticipate the compute intensity, data movement, or sovereignty requirements those tools demand.
- The gap is not theoretical. It shows up as cost overruns, data governance gaps, and contractual lock-in that slows the institutions trying to move fastest.
- Five contract review questions can surface whether the infrastructure agreement a bank is operating under was actually designed for the bank it is becoming.
- Infrastructure designed for AI workloads is not a future investment. It is needed now. For institutions that move carefully with governance intact, it is already a competitive differentiator.
Keeping your cloud infrastructure current is one challenge. Adding AI on top of it is another. Most community and regional banks are doing both simultaneously — on contracts that were written for neither.
A previous piece, Why Your Systems Keep Slowing Down examined where that question leads: A large share of community and regional bank systems are running in environments that were never designed for them. Customer portals inside core processing stacks. Fraud analytics on a shared public cloud. AI tools deployed wherever there was room. The infrastructure placement decision was never made deliberately. The symptoms arrived later.
This article is about the layer underneath that problem: the contracts.
A recent PYMNTS analysis observed that the cloud agreements underpinning most financial institutions were built for a different era: predictable transaction workloads, scheduled risk model cycles, stable compute consumption. That world changed when AI entered banking operations. Not hypothetically. Right now, in institutions of every size, AI tools are generating continuous, unscheduled, compute-intensive data flows that the contracts governing those environments were never written to accommodate.
The result is a double gap. Systems are running in the wrong environment. And the contracts governing those environments are built for the wrong workload. Both gaps are addressable. But only if a leadership team knows where to look.
AI Changed the Infrastructure Question. Most Contracts Did Not.
When a community bank runs a fraud detection tool, that tool is not making one calculation per transaction and waiting. It is processing continuously, correlating behavioral signals across accounts, running inference on incoming data in real time. That is a fundamentally different computational pattern than a payment that processes at night in a batch.
Most cloud agreements were priced and scoped before institutions knew they would be running AI at this level. They were built around storage tiers, uptime guarantees, and predictable compute consumption. They did not anticipate continuous data pipelines crossing multiple systems. They did not address what happens to customer financial data when AI training workloads require it to move through the provider’s infrastructure in ways the original agreement never defined.
CSI’s 2026 Banking Priorities survey found that 53% of banking institutions are highly concerned about cloud-based technology risk. The concern is valid. But concern without a mechanism to act on it produces nothing. The mechanism starts with the contract review.
The Hidden Costs of a Misaligned Cloud Contract
- The first cost is financial. AI workloads consume compute differently than traditional banking applications. Inference spikes unpredictably. Training runs are intensive. An agreement priced around transactional workloads will bill those spikes as overages. Banks do not always notice until the infrastructure bill arrives looking nothing like the forecast.
- The second cost is strategic. An institution that wants to move its AI capabilities to a different provider, add a partner tool to its ecosystem, or shift workloads based on performance requirements may discover that its existing agreement constrains that movement. Exit terms in legacy cloud contracts were not written with AI portability in mind. Data stored deep inside a provider’s proprietary toolchain is hard to move without losing the integrations that make the AI work.
- There is a third dimension that community bank leadership teams increasingly cannot afford to overlook: data sovereignty. Geopolitical pressure and evolving federal examination expectations are pushing institutions to demonstrate that customer financial data remains within defined jurisdictions. A cloud agreement written before those pressures emerged may not include the contractual controls that make that demonstration possible. When an examiner asks, the answer cannot be reconstructed from architecture diagrams. It needs to exist in the contract.
Contract Review Questions Worth Asking Before the Next AI Initiative Launches
These questions are not technical due diligence items for IT. They are governance questions that belong in a leadership conversation, ideally before a new AI deployment is approved, not after it is running.
1. Does this agreement define what happens to customer data when an AI tool processes it?
AI inference and training require data to move in ways that traditional banking applications did not. The contract needs to specify where that data goes, whether it can be used to improve the provider’s own models, and who retains ownership of the intelligence derived from it. The absence of clear language on this point is itself a finding. Regulators are not waiting for the industry to settle this question on its own.
2. Is the compute pricing model compatible with how AI workloads actually run?
Legacy agreements priced storage and compute around predictable patterns. AI does not follow predictable patterns. Inference spikes. Training runs are intensive and irregular. If the pricing model does not accommodate that variability, the institution will pay overage rates for standard AI operations. The contract should be reviewed against the actual consumption pattern of every AI tool currently running, not the projection made at signing.
3. Can customer financial data be demonstrated to stay within required jurisdictions?
Major cloud providers distribute data across regions for efficiency and resilience. That architecture made sense for traditional workloads and was acceptable under the regulatory environment when most community bank cloud agreements were signed. That environment has changed. If an institution cannot point to a contractual commitment that customer financial data stays within a defined jurisdiction, that is a gap to close now rather than explain to an examiner later.
4. What does it cost to move, and what does the institution lose if it does?
Exit terms in legacy agreements were often written when switching costs were primarily about data migration. In an AI environment, the cost of switching includes losing integrations built on proprietary toolchains, retraining models on a new platform, and rebuilding the data pipelines that connect AI capabilities to banking systems. An institution that discovers its AI strategy is effectively locked to a single provider’s ecosystem has a governance problem, not just a vendor preference. The contract review should surface this before a new AI investment deepens the dependency.
5. Does the agreement assign clear accountability when an AI-dependent system fails?
This question connects directly to the infrastructure accountability issue raised in the previous piece. A contract that defines uptime for a storage environment is not the same as a contract that defines responsibility when an AI tool running on that storage produces a wrong output at a critical moment, or fails entirely during peak load. As AI moves closer to core banking functions, the SLA language governing it needs to match the stakes. A named contact with a documented response obligation is the minimum. A conference call to sort out ownership while a customer-facing system is down is not a recovery plan.
The Infrastructure Decision and the Contract Decision Are the Same Decision
Community and regional banks that addressed the infrastructure placement problem first—putting non-core mission-critical systems in environments designed for regulated financial workloads rather than shared public cloud or inside core banking stacks—discovered a second benefit. The contract governing a purpose-built, dedicated infrastructure environment is a different kind of document than a hyperscaler agreement written for a thousand different industries at once.
A specialty infrastructure environment built for regulated banking addresses the contract questions above at the point of design, not through amendment. Data stays where the institution needs it to stay. Accountability is named and documented before anything is deployed. Compute is sized for the actual workload profile, including AI inference patterns, not an industry-average prediction. Exit terms reflect the workload, not a generic SLA written before the institution’s AI strategy existed.
The institutions that are best positioned heading into the next examination cycle are not the ones that moved fastest on AI. They are the ones that moved deliberately, with governance intact. That means an infrastructure environment built for regulated AI workloads, and a contract that reflects the bank those institutions are becoming rather than the bank they were when the agreement was signed.
The contract review is not a legal exercise. It is a governance decision about whether the infrastructure foundations of the bank’s AI strategy were actually built to carry that strategy forward.
At Nexcess, these are not hypothetical problems. They are the conversations our infrastructure architects have with community and regional bank teams every week—working through where AI workloads actually sit, what the current agreement does and does not cover, and what a purpose-built environment designed for regulated financial workloads looks like in practice. If your institution is running AI and has not reviewed the cloud contract governing it, that is the right starting point. We are happy to have that conversation—to hear what you are seeing, share what we are seeing, and help you find the right path forward for your organization.
Erin Raese is CMO at Nexcess, a Specialty Cloud provider focused on high-performance, compliance-ready environments for community and regional banks. She has 30+ years in technology and 15+ years advising large financial institutions on infrastructure strategy and go-to-market execution.
Sources: CSI 2026 Banking Priorities Executive Report (survey conducted October 2025, 252 community banking professionals); PYMNTS, “Banks Confront the Cloud Contracts Built for Yesterday,” April 2026.
