Across the financial services industry, institutions are under pressure to
get
more productive with fewer resources — while serving more customers better, meeting stricter compliance standards, and modernizing aging infrastructure. That pressure has only increased
as emerging AI technologies offer tantalizing new possibilities.
Among the most exciting developments are
AI agents:
intelligent systems capable of reasoning, adapting, and executing tasks autonomously. Unlike traditional automation, which follows predefined rules, AI agents can make decisions in real time and even adjust to changing circumstances dynamically. That kind
of flexibility has huge appeal in finance, where teams often face a mix of structured tasks and unpredictable exceptions.
But as many organizations are discovering, implementing AI agents in isolation creates more problems than it solves. Agents that aren’t coordinated within
a broader end-to-end process can act out of sync, introduce compliance risks, and frustrate both customers and employees.
That’s where process orchestration comes in.
Why process orchestration is essential for AI agent success
Think of a business process like a relay race. Even the best runners will fail if they don’t know when to pass the baton. AI agents, for all their intelligence,
need that same level of coordination. Without orchestration, you end up with disconnected tasks, unpredictable outcomes, and a serious lack of visibility.
Process orchestration ensures that every piece of an end-to-end automated process — whether it’s a human, a legacy system, or an AI agent — fits together
in a coherent sequence. For AI agents, that might mean knowing exactly when they should step in, what data they should act on, and when to hand control back to a person or another system.
This is especially important in finance, where compliance and auditability aren’t optional. AI agents making autonomous decisions about customer risk profiles,
transaction limits, or fraud detection must be governed by clear logic and a transparent audit trail. Orchestration provides the guardrails that make this possible.
Banking needs a hybrid approach: Deterministic meets dynamic
Traditionally, banks and financial institutions have relied on deterministic process orchestration. This approach works well when processes are stable,
repeatable, and heavily regulated — think loan origination or KYC. But deterministic business process models can struggle with flexibility. A deviation or exception often requires manual intervention, which slows things down.
On the other end of the spectrum is non-deterministic orchestration, which allows AI systems to determine what should happen next based on context and
real-time data. A non-deterministic business process model offers greater agility but introduces unpredictability, which is something most compliance teams aren’t ready to accept.
Agentic process orchestration blends both approaches. It allows institutions to design business processes that are mostly deterministic — with key moments
where AI agents can take over, interpret data, and act autonomously. These hybrid processes offer the best of both worlds: control where it matters, and flexibility where it counts.
Imagine a KYC process where the basic steps are strictly defined, such as collecting documents, verifying identity, screening against watchlists. But in
cases where a document is unclear or customer data doesn’t match, an AI agent can step in to guide the customer, request new information, or even escalate to a human agent. All of this can happen within a clearly defined business process that maintains compliance
and transparency.
Avoiding the trap of siloed automation
One of the biggest risks with AI agent deployment is fragmentation. Many financial institutions already suffer from siloed automation: RPA bots working
in one department, disconnected SaaS tools in another, and legacy systems that can’t talk to anything new. Adding AI agents without orchestration only compounds this problem. In fact,
85% of organizations face challenges
in scaling and operationalizing AI across their business.
In practice, fragmented, disconnected processes lead to slow customer service, inconsistent decisions, and compliance headaches. A customer might upload
documents online, call a support line, and receive conflicting information — all because the processes behind the scenes aren’t orchestrated properly.
Process orchestration provides the connective tissue that ties these efforts together. It enables financial institutions to integrate AI agents with existing
systems, ensure consistent execution, and surface issues before they become risks. Over time, it can also provide the visibility and metrics needed to continuously improve processes based on real-world performance.
Orchestrating and operationalizing AI in the real world
Here are a few examples of how process orchestration can work with AI in the real world. For example, one major U.S. bank used process orchestration to
streamline its digital onboarding experience after a large merger. By orchestrating existing tools across both legacy organizations, they created a seamless customer journey where applications could be started online, paused, resumed in-branch, and completed
without friction.
Imagine a different scenario where a global investment firm leverages AI agents for real-time portfolio monitoring. But rather than turning them loose,
the firm embeds those agents within orchestrated business processes. This allows AI to flag anomalies and suggest actions, while still routing critical decisions through compliance checks and human review.
These kinds of orchestrated deployments show what’s possible when AI agents operate within an end-to-end business process. By embracing agentic process
orchestration, financial institutions can unlock real business value: faster decision-making, improved compliance, better customer experiences, and more resilient operations. Most importantly, they can do it in a way that scales — without losing control.
The real power of AI in finance doesn’t come from the intelligence of the agents alone. It comes from how well they’re orchestrated. Financial institutions
that recognize this early by investing in the architecture to operationalize AI will be the ones who win.
