Artificial intelligence is becoming increasingly common across the banking industry. From underwriting support to compliance checks and document processing, financial institutions are experimenting with AI tools designed to automate routine tasks and improve operational efficiency.
However, many organisations are discovering that deploying individual AI tools can create new challenges. According to cloud banking platform provider nCino, the issue is often not the capability of individual agents, but the lack of coordination between them.
Banks may end up managing dozens of specialised AI agents that perform narrow tasks but operate in isolation. Integrating these tools, governing their actions, and ensuring consistent decision-making can quickly become complex for teams already focused on serving clients and managing risk.
From disconnected agents to an AI operating model
To address this challenge, nCino has introduced what it calls an Agentic Operating System, designed to coordinate how AI agents operate within banking workflows.
The concept centres on creating an orchestration layer that assigns roles to AI systems, ensures decisions remain auditable, and keeps automated processes aligned with banking regulations and internal governance frameworks.
Rather than deploying large numbers of individual agents, the platform groups AI capabilities into role-based “digital partners” that mirror existing banking functions.
The idea is to enable human employees and AI systems to work alongside each other in a coordinated environment rather than managing multiple disconnected tools.
A dual workforce in banking
Within this framework, AI systems are designed to support key roles across the banking organisation.
Analysts, for example, often spend significant time conducting financial spreading, covenant testing, and early warning monitoring. Digital partners can automate much of this data-heavy work, allowing analysts to focus on interpretation and decision-making.
Similarly, processors involved in loan origination and transaction management frequently manage large volumes of documentation, conditions, and compliance checks. Automating these processes can help reduce administrative workload and speed up deal completion.
Relationship managers and client-facing teams also benefit from real-time insights that surface relevant customer information during conversations, enabling more informed discussions with clients.
In this model, AI systems do not replace human roles but handle the repetitive analytical and operational tasks that consume much of employees’ time.
Addressing the coordination problem in AI adoption
Industry research highlights how difficult it can be for organisations to scale AI initiatives effectively. Studies from consulting firms and technology analysts have shown that many generative AI projects fail to move beyond pilot stages because companies struggle to integrate AI capabilities into day-to-day workflows.
This challenge often arises when AI tools are introduced as standalone features rather than integrated components of a broader operating model.
By aligning AI capabilities with existing banking roles and processes, orchestration platforms aim to reduce the complexity of managing multiple systems and ensure AI outputs remain consistent with institutional governance standards.
Supporting compliance and transparency
Regulatory requirements remain a critical consideration for financial institutions adopting AI.
Systems used in banking must produce auditable outcomes, operate within permission frameworks, and support explainable decision-making. Integrating AI within existing governance structures can help institutions maintain compliance while introducing automation.
As regulatory expectations continue to evolve, embedding compliance checks directly within automated workflows may also help reduce operational overhead.
Changing how bankers spend their time
The adoption of AI-supported workflows may ultimately change how banking professionals allocate their time.
Automated systems can handle large volumes of repetitive tasks such as document validation, financial data extraction, and monitoring activities. This allows employees to concentrate on areas where human expertise remains essential, including relationship management, strategic decision-making, and complex credit assessments.
According to nCino, the long-term vision is not a workforce replacement but a dual workforce model where human bankers and digital partners collaborate within the same operational environment.
A shift in how banking technology is deployed
As AI adoption accelerates, the banking industry is beginning to move beyond experimentation toward more structured approaches to automation.
Rather than focusing solely on individual AI tools, financial institutions are increasingly exploring how AI can be embedded within broader operating models that support governance, coordination, and real-world decision-making.
If this approach proves successful, digital partners may become a standard component of banking teams, helping institutions manage complexity while improving efficiency and customer experience.
Read the full blog from nCino here.
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