At this year’s SIFMA Ops Conference, operations professionals explored the evolving landscape of capital markets, from the shift to T+1 settlement to the rise of digital assets. As the industry undergoes rapid transformation, organizations are increasingly turning to data and AI-driven technologies to boost operational efficiency and resilience.
Data is the foundation of every transaction, decision, and transformation in financial services. As global data volumes grow exponentially – reaching 402.74 trillion terabytes in 2024 (Statista) – manual ingestion, cleansing, and structuring are no longer feasible.
To remain competitive, firms must adopt technologies that not only automate data processing but also enable intelligent, adaptive decision-making. Firms that don’t have the right systems and tools in place to manage data effectively will fall behind operationally, competitively, and financially. This is where the synergy between data automation and agentic AI workflows becomes a game-changer.
Trust in humans vs AI: Risk or reward?
A recent KPMG survey highlights a surge in AI adoption, revealing the daily use of productivity tools has more than doubled from 22% to 58%, with generative AI now embedded in 35% of workflows. Yet only 1 in 10 (11%) firms have deployed AI agents, revealing a significant gap between interest and implementation.
This trend was echoed in our partner workshop with OnCorps AI, where we asked participants about their AI readiness. Our results revealed that while many firms are exploring AI, most remain in early stages of deployment. Here’s what we found:
From hesitancy to opportunity: AI in the planning stage
- 44% are still researching AI opportunities
- 34% are in the pilot or proof of concept stage
- 54% are using a hybrid approach of internal and external AI systems
Narrow focus on potential
- Top use cases identified: document extraction (30%) and agentic workflows (27%)
- Overlooked areas: reconciliation break management (7%), legal contract analysis (17%), and fraud detection (20%)
Barriers to adoption
- 79% cite lack of trust in data quality and accuracy as a major concern
- 44% are unfamiliar with agentic AI, and only 22% are actively deploying it
Enhancing accuracy and efficiency with workflow automation
While many organizations are still navigating the early stages of AI adoption, whether researching opportunities, piloting use cases, or grappling with trust in new technologies, there's a clear path forward.
An effective data automation platform can help bridge the gap between experimentation and implementation. By automating data extraction and manual, repetitive tasks such as data entry or reconciliations, these platforms streamline workflows while maintaining governance and auditability.
“We’re starting to see agents move beyond basic automation. In reconciliations, they’re beginning to actively resolve exceptions—the kind of work people used to handle manually.” —Bob Suh, CEO, OnCorps AI
This shift reflects a broader trend: AI agents are no longer just supporting operations; they're becoming integral to them. This not only reduces human errors, exceptions, and operational costs, but also enhances overall data management. Teams, as a result, are freed to focus on strategic initiatives, ensure businesses remain compliant, and reduce risk.
As firms shift from on-premises systems to SaaS and integrated AI strategies, interoperability becomes essential for long-term success.
From data processing to intelligent operations
The convergence of data automation and agentic AI signals more than just operational efficiency; it marks a fundamental shift in how organizations operate.
No longer confined to routine data processing, financial services firms can now unlock intelligent, adaptive, and resilient operations. This is not just evolution; it’s transformation. And, for those ready to move beyond the status quo, the opportunity is clear: harness this synergy to streamline operations, strengthen risk management, and turn data into a strategic asset.
Ready to make the leap to intelligent automation operations? Start here.
- Assess Readiness: Evaluate your data infrastructure, governance practices, and team skills. Identify gaps in data quality, legacy systems’ interoperability, and AI literacy.
- Target high-impact use cases: Go beyond document extraction and apply to lesser-known use cases such as reconciliation break management, where data automation and agentic AI can deliver measurable value.
- Invest in data governance: Address one of the top barriers to data automation and AI adoption, trust. Implementing tools that ensure accuracy, auditability, and compliance while maintaining human-in-the-loop reviews.
As organizations strive to implement AI and data automation technology, the ability to orchestrate data across people, systems, and AI provides a critical advantage. Platforms like Xceptor are leading the shift, bridging the gap between legacy systems and intelligent automation.
By transforming unstructured data into accurate, actionable insights, Xceptor not only enhances operational efficiency but also lays the foundation for trustworthy AI.
When leveraged in combination with agentic AI, such as OnCorps AI’s AI algorithms, organizations are empowered to unlock new levels of efficiency and intelligence across their most critical workflows.
Get in touch with a member of our team or schedule a demo to see our platform in action.