The future of AI in financial services: Unifying banking automation under one governance model

Artificial intelligence (AI) and machine learning models are rarely the bottleneck. The fragmented automation beneath them is.

A regional bank approves a project to score transactions for fraud in real time. The machine learning model works. The data science team is strong. Six months later, it still isn’t in production. The model can detect fraud. It just can’t reliably get the data it needs because that data lives across seven systems that were never built to hand off data cleanly. A score that depends on current account history, a sanctions check and a customer record can’t pull all three in sequence, fast enough to act. So the project stays a pilot.

That pattern is common, and it explains something most banks miss about AI applications in financial services. The thing blocking them usually isn’t the AI. It’s the automation underneath it.

Most financial institutions already run a deep automation stack: workload automation, robotic process automation (RPA), integration platforms and a growing layer of AI tools on top, increasingly including generative AI. What they tend to lack is the connective tissue that binds those pieces into a single system. According to original research by Redwood Software, 80.4% of financial institutions use a centralized automation platform, yet only 18.6% have enterprise-wide orchestration with cross-system visibility. Adoption is nearly universal. Coordination is rare.

That gap is the real problem. Banks don’t have a banking automation problem. They have a coordination problem, and it sits directly between their AI ambitions and AI-ready operations.

AI already runs across the banking industry

Step through a bank today and AI shows up in nearly every function. On the front line, chatbots, virtual assistants and other AI assistants handle customer support, field routine customer interactions inside mobile banking apps and raise customer satisfaction without adding headcount. Behind them, machine learning models power credit scoring and underwriting and judge creditworthiness to speed loan origination, loan processing and loan approvals on everything from credit card limits to commercial loan applications.

Markets, risk and compliance 

In the financial services sector, algorithmic trading and AI-driven portfolio management shape investment decisions and react to shifting market conditions. Investment firms and investment management teams use forecasting to plan for volatility in financial markets by weighing signals ranging from market data to social media sentiment. Pricing teams use the same models to set the price of financial products. In risk and compliance, AI handles risk assessment and risk modeling, surfaces anti-money laundering (AML) patterns and trims the false positives that slow fraud detection on card and payment activity.

The data layer beneath every model

Most of this depends on data analytics applied to vast amounts of data, including the unstructured data buried in contracts, statements and customer messages. Natural language processing (NLP) and document processing read those documents. Deep learning and other algorithms find patterns across datasets that no analyst can review by hand, and related techniques watch for cyberattacks to strengthen cybersecurity around sensitive customer data. Banks and fintech firms treat these AI capabilities as table stakes now, and the benefits of AI are well understood across the financial services industry.

So the breadth is not in question. Using AI is no longer the hard part. The AI tools, platforms and technologies behind these functions are mature and widely available. Across the financial sector, AI helps with pricing, fraud, lending and service, and the use of AI keeps expanding into new corners of the bank. The harder question, the one that decides which of these AI applications actually reach production, is whether the bank’s systems can act on what the models produce.

AI applications in financial services depend on modern automation infrastructure

An AI model is only as good as the systems that feed it and surround it. That sounds obvious. It’s also where most banking AI programs quietly break.

Look at what your environment holds: core banking platforms, payment systems, customer relationship management (CRM) platforms, data platforms, compliance systems, mainframes and a widening field of cloud applications. Any AI application that does real work, whether it flags a suspicious transaction, scores credit risk or summarizes a customer’s exposure, has to reach across several of those systems, pull current and accurate data, then trigger the right action in the right order. The machine learning and predictive analytics are the visible part. The plumbing is the hard part.

Without orchestration to coordinate that plumbing, AI initiatives stay isolated pilots. They demo well in a controlled setting, then stall the moment they hit a production environment where nobody can constrain them. Redwood’s research backs this up: 65.1% of financial institutions say legacy automation platforms limit their ability to modernize, and 61.4% say siloed environments constrain their AI readiness.

So the issue isn’t that banks need more AI. The value of every AI application, from fraud detection to regulatory reporting to customer servicing, depends on the ability to connect data, systems and processes across the enterprise. Get that layer right, and AI scales. Skip it, and you have a smarter model sitting on a foundation that can’t act on what it concludes.

The hidden problem: banking automation is usually fragmented

The uncomfortable part is that the fragmentation blocking AI wasn’t an accident. It’s the result of how banks built automation in the first place: one process, one tool, one team at a time. Each decision made sense on its own. The sum is a web of automation that can’t see itself.

It tends to show up in a few predictable places:

• Workload automation and integration run on separate tracks: The teams running scheduled workloads, integrations and applications manage different tools with different priorities, and limited visibility across them creates silos.
• RPA solves isolated tasks but spawns new silos: Bots automate routine, repetitive work well, but they run independently of the broader process, so disconnected automations pile up faster than anyone can govern them.
• AI projects launch without operational controls: Many start as standalone experiments with no standardized oversight, so their outputs don’t integrate reliably with the processes meant to consume them.
• Legacy systems stay cut off from modern platforms: Core banking systems, mainframes and older applications need custom integration to talk to anything new, and every custom connection slows modernization.

Underneath all of it sits the layer that the rest depend on, which is data movement. This is where fragmentation does the most damage, and it’s the part banks consistently underestimate. In Redwood’s research, three of the top four automation challenges relate to data pipelines and cross-system coordination, not to automating individual processes. Specifically, 42.2% of institutions cite difficulty integrating data pipelines into workflows. When the data layer is inconsistent, everything built on top of it inherits that inconsistency.

Fragmentation also has a price. It drives up operational costs and processing times, multiplies manual tasks and manual effort across teams and quietly erases the cost savings that justified the automation in the first place. In banking, the stakes go further. Regulators expect auditable, controlled workflows across every system a process touches. High-stakes transaction processing leaves no room for silent failures, and brittle scripts behind payment rails like ISO 20022 and instant payments invite processing delays, reconciliation backlogs and compliance exposure. Know Your Customer (KYC), AML and credit decisions routinely run across systems that don’t share state. Fragmentation here isn’t a tidiness problem. It’s a financial and reputational one.

How IT teams can unify workload automation, RPA and integration under one governance model

When you finally confront this, the instinct is to rip everything out and standardize on one tool. That’s almost always the wrong move. It’s slow, risky and unnecessary. The goal isn’t fewer tools. It’s one coordinated system. There’s a more practical path, and it’s the one the most AI-ready institutions have already taken.

Put one orchestration layer over your existing tools

Start by adding a single orchestration layer on top of the tools you already run. Connect existing systems through APIs instead of brittle custom code, so workload automation, RPA and integration report into one control plane rather than consolidating onto a single vendor overnight. The aim isn’t more automation tools or another point automation solution. It’s coordinating the automation technology you already own, so rule-based jobs and no-code automations run as part of one chain instead of a dozen disconnected ones.

Bring it all under one governance model

Then bring those three under common governance, observability and audit. One policy model. One set of role-based access controls. One audit trail spanning scheduled workloads, bots and integrations. This is what turns “we run a lot of automation” into “we can prove how our automation behaves,” which is the distinction regulators actually care about.

Treat data movement as a first-class concern, not an afterthought. Standardize how data flows between systems so pipelines are monitored and recoverable rather than stitched together with scripts that fail quietly. This is the layer that analytical and agentic workloads draw on, so it can’t be the weakest link in the chain.

Finally, extend the same governance to AI and agentic processes. As AI shifts from recommending to acting, it should inherit the guardrails, approvals and accountability that governed, automated processes already follow, instead of operating in a separate ungoverned lane. Done well, automated workflows and automated systems streamline operations, optimize processing times and cut the manual intervention that slows everything down. Those are the gains in operational efficiency and scalability that make the rest of a digital transformation possible. None of it requires starting over. It requires deciding that orchestration and governance are the architecture, not a layer you bolt on later.

What a unified governance model looks like for banks

In practice, the target is one governance framework that spans everything that moves work or data: workload automation, RPA, integration, data movement, AI services and agentic AI systems. A few capabilities define it.

The reason to insist on one framework rather than four good ones is simple. AI doesn’t respect org charts. An AI-driven workflow will reach across the silos your teams maintain, and the only way to keep it accountable is to govern those silos as one.

Banking automation use cases that benefit from unified governance

This isn’t theoretical. AI-powered processes become more accurate and scalable when they run under a single governance model. The use cases banks care about most are exactly the ones that fall apart without coordinated automation. Redwood’s research shows what institutions have already automated, led by payment processing at 70.8% and regulatory compliance at 68.4%, and what they’re prioritizing next. The AI use cases banks are targeting over the next three to five years converge on a short list: AI-driven portfolio management, real-time risk and compliance coordination and customer onboarding with KYC. Every one of those depends on coordinated, real-time data and workflows.

Customer onboarding and digital identity

Customer onboarding and digital identity are the clearest examples. KYC, AML, identity verification and customer provisioning have to execute in sequence across on-premises, cloud and third-party data providers. A unified model traces and audits each step, rather than reconstructing it after a customer complains. We’ve made the case elsewhere that onboarding is a risk story, not just a customer experience metric.

Fraud detection and financial crime prevention

Fraud detection and financial crime prevention is another. Transaction monitoring, sanctions screening, case management and regulatory reporting all run on machine learning models whose real-time decision-making is only as reliable as the data feeding them.

Payments modernization

Payments modernization raises the bar further, since real-time payments, cross-border flows and ISO 20022 settlement are unforgiving of timing failures. Regulatory compliance and reporting benefits directly from a single audit trail and consistent governance across data collection, validation, compliance reporting and audit readiness. Few corners of the banking sector are untouched by these demands.

Customer experience and digital channels

One area deserves an honest caveat. Customer-facing processes are less automated and a lower investment priority than the compliance-driven workflows above, according to Redwood’s research. That’s not a reason to ignore omnichannel servicing, customer support or personalized, AI-driven recommendations. It’s a signal of where the next wave of value sits once the operational foundation is solid. Banks that orchestrate their back offices well are well positioned to extend automation across more of their banking operations without spinning up a new generation of silos.

Governance becomes the constraint as AI adoption grows

It’s tempting to treat governance as the brake on AI. It’s closer to the opposite. Governance is what lets AI reach production at all, and the reasons compound as adoption grows.

Regulators increasingly expect banks to demonstrate accountability for AI-driven decisions, with transparency, controls and auditability across AI-enabled operations. AI systems run on large volumes of sensitive customer data, which makes access control and privacy a governance question, not only a security one. Teams need explainability into how AI recommendations are generated, plus monitoring to prevent AI failures from quietly disrupting a critical process.

Then there’s the part still arriving: agentic AI. AI agents can take actions across multiple systems with limited human intervention. That’s the promise and the exposure in the same sentence. Governance frameworks define the guardrails, approvals and accountability that make autonomous action safe in a regulated industry. As AI moves from making recommendations to taking actions, governance has to extend past the model into the operational workflows where those actions land.

The maturity data shows how early most banks still are. Only 8.6% have reached fully autonomous operations. The distance between today and that number is the distance most institutions have to travel, and the route runs through governance rather than around it.

How leading banks are preparing for AI-ready operations

The institutions furthest along aren’t waiting for one platform to fix everything. They’re modernizing legacy automation, consolidating fragmented tooling and standardizing governance across the systems they already run, so AI has a coordinated foundation to build on instead of another silo to inherit.

Redwood’s research shows both the momentum and the distance left to cover:

• 80.1% of financial institutions increased automation spend in the past year
• 91.4% say automation improves compliance and resilience
• 54.8% still operate across five or more automation environments

That last figure is the fragmentation that holds AI back, and closing it pays off in daily operations. Redwood customers cite manual intervention as a recurring challenge 25% less often.

Building the foundation for AI in banking and financial services

AI success in financial services depends on operational execution. Fragmented banking automation caps the value of every AI application built on top of it. Closing the gap means running workload automation, RPA and integration as one orchestrated ecosystem under a single governance model, with infrastructure that supports compliance, resilience and scale rather than working against them. This is what a serious digital transformation in banking actually rests on.

The orchestration foundation this takes

This is the environment RunMyJobs by Redwood is built for. As an enterprise-grade data orchestration platform and the only SAP Endorsed App in the workload automation category, RunMyJobs connects hybrid data pipelines and workflows across SAP, cloud data platforms, partner systems and legacy infrastructure into one governed execution layer, with end-to-end monitoring, SLA management and dependency visibility. Rather than replacing the workflows that already run reliably, it coordinates them from outside the ERP core, so teams can manage data delivery like a production service instead of a collection of disconnected jobs. The agentless architecture means there’s no agent infrastructure to maintain as the estate grows.

The data backs this up. Redwood customers in the study were 1.5 times more likely to have enterprise-wide orchestration and 1.4 times more likely to reach the highest levels of automation maturity. They didn’t get there with better models or bigger AI budgets. They built a different kind of automation program, organized around coordinated data flows and end-to-end visibility instead of isolated process automation.

Recognize this early, and you do more than deploy another model. You give every AI application a foundation it can rely on, one that’s auditable, resilient and ready to scale as complexity grows, which in financial services it always does. That is what separates an AI pilot from the AI your business actually runs on.

Download State of AI and data pipeline automation in financial services 2026 to see where automation maturity stands today, where AI readiness gaps are widest and what the path from automated to orchestrated looks like in a regulated industry.