Every bank in 2026 is under pressure to talk about AI. Boards want a strategy, vendors are pitching aggressively, and regulators are starting to ask pointed questions about explainability and governance.
In the middle of all this sits procurement.
Perhaps that’s you.
Perhaps you’re being asked to run RFPs for AI-ready core platforms, evaluate wildly different proposals, and somehow make sure the institution doesn’t buy an expensive experiment that fails six months after go-live. And maybe you’re currently trying to separate AI readiness from marketing spin.
This guide is written for procurement, legal, and commercial teams who are leading or supporting core modernisation decisions. It focuses on what an AI-ready core really means in practice, and how to bake those requirements into your evaluation and contracts without turning the process into a science project.
Why being AI-ready is a core problem, not a vendor feature
Most AI projects in financial services fail for the same reason: the models can’t get to the data they need, or they can’t be safely embedded into production processes.
This is an infrastructure issue.
If your core can’t provide clean, timely data in a controlled way, no fraud model, credit model, or chatbot will deliver what it promised in the slide deck. If your systems can’t safely call an AI service in real time, or roll back when something goes wrong, procurement is the team that will end up having to explain why the company has paid for value they can’t actually use.
So, just like when vendors said they were cloud-native, when they were truly cloud-washed, when a vendor claims their core is AI-ready, your job is to translate that into testable conditions by asking these questions:
- Can our teams get to the data they need without breaking production?
- Can we plug models into real journeys without six months of engineering?
- Can we control, audit, and, if necessary, turn those models off quickly?
Pillar 1: A data foundation your AI can truly use
AI lives or dies on data. An AI-ready core must make it simple and safe to work with production-grade data, not just promise good reporting.
At a minimum, you should expect your platform to support a clean separation between live transaction processing and analytics. In practice, that usually means a secure, read-only replica of the production database or a structured data layer that stays in sync. Your data and product teams should be able to query this layer directly or via familiar tools, without opening tickets every time they need a new feed.
The key question for procurement is not whether or not the core provides reports. It’s this: how do our data teams get access to granular customer and transaction history without risking downtime? If the answer involves manual CSV exports, nightly flat files, or a long list of to-be-developed connectors, that platform is not AI-ready, regardless of how many analytics dashboards appear in the demo.
You don’t need to specify the technology in the RFP, but you can specify outcomes. For example: the ability to provide a near real-time, read-only replica of core data, hosted under the bank’s control, and suitable for BI and machine-learning workloads. This is the foundation every later AI use case will rely on.
Pillar 2: Integration and extensibility; can you plug models in?
The second pillar is whether AI and decision engines can talk to the core in real time, and vice versa.
An AI-ready core exposes well-documented APIs for key operations: onboarding, transaction posting, credit decisions, fraud flags, and so on. It also provides a safe surface area where you can add or adapt decision logic without needing vendor-side code changes for every experiment.
In concrete terms, you’re looking for:
- Event-driven or API-based hooks where external models can be called as part of a journey, for example, during KYC, limit setting, pricing, or anomaly checks.
- A way to adjust business rules, product parameters, and flows through configuration or controlled custom logic, rather than full change requests.
From a procurement perspective, the right question is: if we had a working model tomorrow, how would we put it in front of real customers? If the answer is that you must open a change request, join the queue, and plan for a multi-month release cycle, the platform might be modern in theory, but not in operation.
When you structure your RFP, ask vendors to describe real customer examples where external models or decision engines are used in production. Push for specifics: which endpoints are called, how latency is handled, how are failures treated. That will tell you far more than a generic statement about supporting AI integrations.
Pillar 3: Governance, auditability, and risk
For your risk and compliance colleagues, AI-ready must mean that the firm can prove it works as intended.
A modern core should already be designed around strong audit trails and configuration governance. Every change to products, limits, fees, and workflows should be logged: who changed what, when, and under which approval. For AI-driven decisions, that becomes even more important. When a customer asks why they were declined, or a regulator audits a fraud system, you’ll need evidence: inputs, decision path, and overrides.
Procurement’s role is to make this explicit. Contractual and RFP language should cover:
- Immutable audit logs for configuration changes and system actions.
- The ability to retain and retrieve decision data for the lifetime required by regulation.
- Clear support for running AI in shadow mode before it affects customers, so risk teams can compare decisions safely.
When vendors talk about full flexibility, ask how that flexibility is controlled. Does the platform enforce approval workflows and separation of duties, or is everything effectively a super-user change? The more power the system gives you, the more you need to know how that power is constrained.
The goal is to give your institution confidence that it can use AI aggressively without losing line of sight over who is accountable when things go wrong.
Pillar 4: Commercial and operating model; will AI usage break your business case?
Even if the tech is right, the pricing and operating model can undermine AI adoption.
Some vendors treat every new integration, data feed, or model touchpoint as a separate professional services engagement. Others price APIs or consumption in ways that make experimentation prohibitively expensive. In an AI context, that kills momentum because the first three pilots can blow through the budget, leaving the firm to back away.
An AI-ready core commercial model should make it economical to:
- Provide ongoing access to a replicated data layer without per-query penalties.
- Add new integrations and decision flows through configuration or contained custom work, not recurring large projects.
- Run pilots and A/B tests without separate change fees every time you want to adjust a rule or model.
For procurement, this is where you can create real leverage. Ask vendors to show how their commercial model behaves as your AI usage grows: more API calls, more data, more experiments. Insist on clarity around what’s included and what generates additional spend. If the pricing makes sense at demo scale but not at production volume, you’ll feel it in year two.
How procurement can de-risk the decision
Beyond the feature lists, the way you structure the buying process can make a big difference to AI outcomes.
First, bring data, risk, and engineering into the procurement process early. Rather than treating them as sign-off functions at the end, ask them to help design the evaluation scenarios at the beginning. For example, agree on one or two realistic AI use cases, such as collections prioritisation or fraud anomaly detection, and use those as test cases with each vendor.
Second, focus on evidence. Ask for live or recent examples where the vendor’s platform has been used to support AI initiatives in production. Request to see the architecture and operational model behind those examples.
Third, be deliberate about pilots. If you’re going to run a proof of concept, define in advance what makes it meaningful: which data sources must be available, which APIs must be used, which metrics will you measure? A toy pilot that uses static files and sits outside real journeys won’t tell you whether the core is truly AI-ready.
Finally, lock in support expectations. Modernisation and AI projects invariably hit unexpected issues. Your contract should define how the vendor will show up when that happens. What are their response times, escalation paths, and the level of engineering engagement you can expect when model behaviour or data access becomes a blocker?
Core banking RFP: red flags to watch for
As you talk to vendors, a few patterns should trigger closer scrutiny:
- “We’ll export CSVs for you.” Occasional exports are fine, but if they’re the primary data access mechanism, your data team will be stuck stitching files together instead of building models.
- “We’re building that AI connector soon.” Roadmaps are not capabilities. Treat anything not in production today as a risk, not a promise.
- “We don’t expose direct database replicas for security reasons.” Security is essential, but there are mature ways to provide governed, read-only access. A blanket refusal often means the core wasn’t designed with analytics in mind.
- “All changes go through our professional services team.” That may be acceptable for deep changes, but if simple rule or configuration updates require full projects, your AI iteration will move at legacy speed.
None of these are automatic deal-breakers, but they should prompt sharper questions and, where possible, compensating controls in your contracts.
What a positive outcome looks like for procurement
A genuinely AI-ready core gives your teams:
- Reliable access to the data they need, when they need it, without endangering uptime.
- A clear path to embed models into onboarding, lending, fraud, and service flows safely.
- Governance and auditability that stands up to internal and external scrutiny.
- A commercial model that makes experimentation normal.
That doesn’t mean every AI project will succeed, but it does mean you won’t be blocked by the core every time the product or risk teams want to try something new.
For procurement, that’s the real success metric; that you’ve helped your institution buy infrastructure that can truly carry AI forward for the next decade.
See how an AI-ready core works with Oradian
If you’re running or planning a core banking RFP and AI is part of the brief, we can show you what AI-ready truly looks like in a live core, not just in a slide. Oradian’s cloud-native platform gives banks a governed data layer, real-time integrations, and the audit trails your risk team needs. Get in touch with our team by emailing vanda.jirasek@oradian.com to see how institutions like FairMoney, Esquire, and Salmon are activating AI with Oradian as the foundation.
Get the full guide: The Digital-First Bank’s Guide to AI
Financial institutions are pouring $97 billion into AI by 2027, but here’s what nobody tells you: 95% of AI projects fail to deliver on their promises. This guide tells you how to be part of the 5% that succeed.
