Jawwad Rasheed, Financial Services Transformation and Advisory Lead at Camunda
The banking and financial services sector is undergoing a structural and transformational shift. In corporate lending, the global private credit market is expected to exceed $2 trillion in 2026 and approach $4 trillion by 2030, as non-bank lenders capture business that used to sit on bank balance sheets. In retail banking, digital-first competitors grow exponentially thanks to modern, personalized experiences and lower cost-to-serve. For example, Revolut now serves 68 million customers globally, which represents a 30% increase over the last 12 months. Against this backdrop, the pressure is on traditional banks and financial services providers to innovate otherwise they risk losing further ground.
Understandably, many banking and financial services firms have placed AI and automation at the top of their strategic agenda in response. According to Camunda’s 2026 State of Agentic Orchestration and Automation Report, three quarters of banking and financial services firms are already using AI agents and 82% plan to increase automation spending over the next two years. This ambition spans their entire operating model, from copilots accelerating loan origination and underwriting decisions, to agents handling KYC document review and customer onboarding, through to automated transaction monitoring, fraud detection, and AI-driven personalization in retail banking.
But while the ambition and investment are in place, execution is holding many firms back. The same research finds 70% of industry leaders see a gap between their AI vision and operational reality, and only 12% of agentic use cases reached production over the last year.
Analyzing the gap
While it’s clear many banks and financial service organizations are experimenting with AI agents or plan to, trust remains a major barrier to wider adoption. For instance, 80% of banking leaders said a lack of transparency around how AI is used in business processes was holding them back, while a further 60% cited compliance concerns around the use of AI agents. In a sector where consistent compliance adherence and risk management are non-negotiable, banks and financial services providers need assurance that AI agents will perform consistently, handle requests accurately, and operate within well-defined guidelines.
This helps explain why AI adoption has been uneven, as firms are more comfortable deploying agents for low-risk tasks or as internal copilots. However, this caution carries its own risks. When AI remains stuck in pilot mode, the potential gains in efficiency, resilience, and competitive advantage go unrealized. By contrast, banking and financial services firms that embed AI agents into enterprise-grade processes can strengthen risk management and compliance, improve resilience to market volatility, deliver more personalized customer experiences, and optimize operations for profitable growth.
From isolated agents to orchestrated capability
A further barrier to production-scale AI is the spread of siloed agents that are not integrated into orchestrated business processes. In banking and financial services, where workflows span multiple devices, partners, and customer touchpoints, disconnected agents risk adding complexity without delivering meaningful performance gains.
To build a scalable foundation for trusting AI agents in production, agentic orchestration provides banking and financial services organizations with a control layer for agent behavior.
This means using deterministic process models to define where agents are allowed to act, which decisions require human approval, what to do when confidence scores are low, and how to capture a complete audit trail of every step an agent takes. Ultimately,. agentic orchestration connects agents to process endpoints and systems of record, with explicit rules, observability, and human-in-the-loop checkpoints to ensure governance.
Research shows that agentic orchestration, not standalone agents, is the key to closing the AI vision-reality gap. Using deterministic process models, clear guardrails, and event-driven orchestration to coordinate agents, people, systems, and devices, enterprises can build a foundation for AI agents they truly trust. With agentic orchestration, banking and financial services organizations will turn today’s AI experiments into durable, business-critical capabilities that improve planning and forecasting, secure information flows through new technologies, and enhance the overall customer experience.
The work ahead
Amid mounting macroeconomic volatility, margin pressure, and fintech competition, banking and financial services organizations are accelerating efforts to automate core processes. The question is no longer whether to deploy AI agents, but how to scale and govern them effectively. Agentic orchestration, not standalone agents, is key to closing the gap between AI ambition and reality. By orchestrating agents, people, and systems, banks can build a trusted foundation for AI at scale.
