Operational Alpha: How Agentic AI Will Rewrite Securities Operations

Since ChatGPT's 2022 debut, the financial services industry has keenly anticipated AI's transformative impact. That era of speculation is over; its practical application is here. Advanced Generative AI (GenAI) models, particularly when deployed through agentic workflows, are not just reshaping but fundamentally rewriting how broker-dealers, investment banks, asset managers, and custodians process and settle trades.

By orchestrating complex decision-making and automating intricate tasks like dynamic inventory management through collaborative AI agents, these GenAI-powered operations solutions will actively reduce risk, slash costs, and propel markets towards a real-time future. The impact will be so profound and arrive so swiftly that early adopters of these next-gen platforms can forge a significant competitive advantage -  "operational alpha."

This GenAI-driven evolution couldn’t be timelier. Soaring trading volumes strain legacy infrastructure, escalating operational risks. The shift to accelerated settlement and 24x7 trading compels market participants to accelerate post-trade processes, pushing towards an eventual necessity for real-time capabilities across all firms. Simultaneously, widespread capital market electronification squeezes margins. The pressure is undeniable: institutions need a next-generation operations model capable of this speed and scale, while minimizing costs and risks. Agentic AI offers a compelling answer.

From Language Models to Autonomous Agent Orchestration

The evolution from powerful language models to sophisticated reasoning engines (like OpenAI's o1, for instance) has paved the way for AI agents – specialized, autonomous entities designed for specific operational tasks. These agents can interpret vast, often unstructured datasets, and make context-aware decisions, often without human intervention. This leap enables AI not merely to support human operators, but to autonomously orchestrate entire operational sequences through coordinated agent activity. Think of it as a digital "special ops team”. These capabilities are fueled by rapid AI advancements and the critical creation of large, standardized datasets. Even the most sophisticated AI agents are only as effective as the data they access. Over the past decade, financial services firms and technology providers have strived to dismantle data silos, aggregating information into unified governance frameworks. At Broadridge, for instance, we've invested significantly in BRx, a global, multi-asset harmonized data ontology. This structured data becomes the bedrock upon which our OpsGPT™ platform deploys AI agents to execute complex tasks with precision.

Agentic Workflows in Action: The Digital Workforce

Today’s GenAI platforms, architected around agentic principles, are a leap beyond previous AI tools. Instead of merely flagging issues for human intervention, the platforms will deploy teams of AI agents that act autonomously and collaboratively.

Imagine a digital workforce:

• An Intake Agent reads and interprets an inbound email query regarding a settlement discrepancy.
• A Data Retrieval Agent is dispatched to query multiple internal (and potentially external) operational systems for all relevant trade details, positions, and counterparty information.
• An Analytical Agent processes this data, identifies the root cause of the discrepancy, and determines the optimal resolution path.
• A Communication Agent drafts an explanatory email or SWIFT message, or even initiates a corrective transaction, based on pre-defined rules and confidence scores.
• An Orchestrator Agent oversees this entire process, ensuring tasks are routed correctly and completed efficiently, escalating to human experts only for true exceptions.

This is agentic AI. In the critical area of trade fails, for example, specialized AI agents can now autonomously analyze root causes, classify fail types with high precision, and even initiate resolution protocols—often involving direct, automated communication with other internal systems or even counterparty agents. This cuts resolution cycles from days to mere minutes, paving the way to eventually both predict and prevent settlement failures proactively.

Beyond Fails: Systemic Operational Alpha

The applications of agentic workflows extend further, driving systemic improvements:

• Capital Efficiency: AI agents can proactively manage global securities inventory, almost like a digital treasurer, by identifying mismatches, recommending optimal asset transfers, and executing rebalancing actions to enhance capital utilization and reduce funding costs.
• Holistic Risk & Transparency: Agentic systems can integrate and mine data from siloed platforms, presenting a unified, real-time view of operational risk and performance across the entire firm. This firm-wide transparency enhances decision-making speed and strategic execution.
• Elevated Client Experience: Client interactions are upgraded through AI agents powering intuitive chat interfaces or managing automated, yet contextually relevant and personalized, email communications for inquiries and updates. Crucially, these agentic systems incorporate self-learning feedback loops. Each successfully (or unsuccessfully) executed workflow refines the agents' adaptive logic and improves the underlying models, making them progressively smarter. This means firms achieve operational alpha not by scaling headcount, but by cultivating increasingly intelligent digital workers. Firms can scale operational intelligence, not just operational capacity.

The First-Mover Imperative

While GenAI operations platforms with sophisticated agentic capabilities are relatively new, their power to transform securities operations is undeniable and imminent. According to Broadridge’s 2025 Digital Transformation & Next-Gen Technology Study, 72% of firms are making moderate to large GenAI investments this year, a significant jump from 40% in 2024. The urgency is clear: over a third expect ROI within six months of deployment. What does this ROI look like? By embedding real-time intelligence via agentic AI directly into post-trade processes and integrating these digital workers into daily operations, firms gain a measurable edge. They'll see rapid reductions in operational complexity, manual workloads, settlement penalties, and capital costs, alongside a corresponding surge in risk management prowess and efficiency. This isn't just theoretical; this is operational alpha in action, offering a critical, tangible advantage to early adopters ready to embrace the agentic AI revolution in securities operations.