Srinivasan Seshadri, Chief Growth Officer and Global Head, Financial Services.

Autonomy is becoming one of financial services’ most widely used – and inconsistently defined- terms. It gets conflated with automation, confused with AI, or presented as a future where banks and insurers run themselves.

At its most honest, autonomy in financial services is a shift in the relationship between people and systems. From human-led operations supported by technology, to technology-led operations guided by human judgment. That changes what people inside these institutions spend their time on, and everything downstream, from cost structures and speed to market, to customer experience and competitive positioning.

The institutions getting this right aren’t chasing a destination. They’re progressing along a spectrum. And the ones making real headway share something in common: they start with the outcome and work backward.

Start with the customer experience

Here’s a useful question for any institution evaluating its own progress: what does your customer actually experience?

Srinivasan Seshadri

A payment clears without intervention. A potential overdraft is identified before it occurs. A mortgage application progresses without repeated requests for updates.

The desired outcome is service that’s invisible when everything is fine, but becomes present and personal when it isn’t.AI can help deliver that personalization at scale, not as a luxury for high-net-worth clients, but as a standard for every customer, without inflating cost-to-serve.

Most institutions have built the front door for this. What’s missing is the operation behind it that can actually deliver on what the interface promises.

Embed intelligence within clear guardrails

Anticipating customer needs requires more than just faster execution. Systems must be able to meaningfully surface the right offer at the right time, flag a potential issue before it escalates, or adjust a risk threshold in real time, within guardrails and with traceability.

Embedding intelligence across the value chain can support underwriting, surveillance, claims handling, advisory, and product recommendations – not just enabling, but constantly learning, adapting, and acting.

This is the difference between automation and autonomy. Automation does what you told it to. Autonomy does what the situation requires, within the boundaries you’ve set.

And for financial services institution operating under regulatory scrutiny, those boundaries matter more than the intelligence itself.

Engineer trust into the system

For most financial institutions, the focus falls on capability rather than trust.

Trust must be supported by structural properties: decisions should be explainable, traceable, auditable and reversible. The data informing them must be secure, while outcomes must be monitored for accuracy, accountability and bias.

In financial services every autonomous action needs to be compliant, defensible and reversible. That’s not a constraint on autonomy. It’s the precondition for it.

Trust must therefore be treated as part of the architecture, not as an assurance exercise added later. Governance, security and explainability should be designed into systems and workflows from the outset. People must also retain the ability to intervene, override or redirect decisions when the stakes require it.

 Connect information and orchestrate work

Intelligent systems depend on clean, connected, timely information. Yet data siloes, legacy technology and fragmented integrations continue to limit what most institutions can achieve.  When the architecture is composable and governed, new capabilities plug in without rework. Data moves where decisions need it. Partners connect cleanly. Regulatory requirements in new jurisdictions don’t require starting from scratch. Without this foundation, AI can act only on the partial information available to it, creating blind spots and risk.

The same principle applies to workflows. Many operations still rely on people to pass files, chase approvals, check exceptions and reconcile information across systems. Every handoff adds time, cost and potential error.

Self-steering workflows can handle routine volumes across onboarding, identity checks, reconciliations, disputes, payment exceptions and claims. People then intervene by exception rather than by default, focusing on complex risk decisions, sensitive customer conversations and strategic judgement.

Turning autonomy into measurable value

Commercially, autonomy should deliver two outcomes: lower structural costs and faster growth. Better orchestration can reduce manual work and prevent efficiency gains from disappearing between transformation programs. Connected, governed infrastructure can also make it easier to test products, enter markets and scale successful ideas.

Progress should be incremental and measurable. Institutions should identify where autonomy already works, determine where the next step will create the greatest value, and measure improvements in cost, speed, risk and customer experience.

Autonomy will remain a buzzword unless it becomes defined by specific, measurable, operational terms. The institutions that get this right won’t be the ones that moved fastest. They’ll be the ones that moved with the most clarity, maintaining trust, compliance and control at every stage.