The Banking Operating Model Is Stuck Without AI Validation
When I was on the executive team at one of the largest US banks, we struggled with two questions that kept people up at night: Who's accountable for what AI does once it’s deployed? How do we know it keeps taking the right actions once we run it at scale?
Banking leaders today want to alleviate the same tension I saw from the inside: banks are moving fast on AI and have finally broken through the deployment barrier. But most of the scattered pilots and vendor solutions the industry adopted have yet to deliver something they’d trust to run a real, regulated workflow.
I want to lay out what we at Skan AI believe about AI transformation because it's based on the production systems I myself have seen fall short in enterprise banking and what we know works, what we’ve proven works alongside our customers.
If your AI systems are a black box, or if you can’t see, transform, and validate how work happens, then you’re building on operational context and governance you can’t trust. That means no matter how promising your results are in the pilot stage, you can’t trust your execution to hold up at scale either.
If you're running operations, you need AI to deliver outcomes that actually move a P&L: cost per transaction down, throughput up, and your best people freed for the exceptions that actually need a human.
If you don’t have a mechanism to actually trace the workthat your teams and AI agents are doing, then you have no way to validate what happened and why.
If you own risk, controls, or compliance, you can’t unleash agents in your regulated workflows until you have continuous visibility. This is exactly the controls monitoring conversation the industry needs to be having right now; banks that skip straight to transforming workflows before they understanding them are unintentionally undermining compliance.
Unless you have visibility over everything that comes before an agent acts, including the process variants and exceptions and escalations, then you don't have a full audit trail. And saying "the model did it" is not an answer any regulator will accept.
Fundamentally, before you build and deploy agents, you need to understand what you expect them to do. That means seeing the process. Going beyond what documentation or SME interviews tell you is happening in the business and actually capturing all the ways that payments, loans, and customer service cases are handled.
If your teams can't see the patterns that make up your real-world processes, then your AI agents can’t either. And they won’t be ready to help your teams transform mission-critical work at enterprise scale.
Here’s how we approach this:
Map your work and own your context.As you scan work at scale and build your Context Graph of Work, you’re building your architecture, your data, all inside your own walls. Every type of work activity captured, case followed, and process mapped gives you the insight to build agentic workflows that sustainably and measurably transform banking.
Redesign workflows and governance in sync.Once you can see and understand the work, you can understand what needs to change. Instead of agentifying broken processes, you can build agents that are embedded into workflows designed for what your teams need right now. And you keep controls and governance tied to how agents work, even as your human and agent execution becomes more coordinated and continues to evolve.
Track what matters, starting before you build.Every recommendation comes with a dollar figure attached, ranked by the metrics that matter and reflect the work your teams actually do. Cycle time, quality, risk, productivity tell you how well your operating model is working. You should be able to see what your AI implementation has done and how much it has cost or saved at every level of the business.
Seven of the ten largest US banks already do this with us. We’re helping them escape the gap between AI deployment and real, measurable business impact behind.
