Artificial intelligence has moved from promise to production at remarkable speed. The rapid rise of gen AI in particular has driven tangible, at-scale impact across large parts of financial services, and early adopters have seen meaningful gains in productivity and quality. In fact, software development throughput has increased by 30 percent or more, know-your-customer (KYC) and client-onboarding timelines have been shortened by as much as 30 percent, and customer service functions are being transformed through AI-enabled “intelligent” self-service and AI-agent-assisted issue resolution.
Yet capital markets and investment banking (CMIB) have not been included in the first few waves of adoption, experiencing far fewer of the benefits to date. Instead, financial institutions have, quite rationally, focused their initial AI investments on easy early wins. These have tended to be domains with a few defining characteristics: relatively lower risk, large volumes, semi-standardized processes, and clear economic upside from automation at scale. Functions such as retail operations, call centers, onboarding, and core IT delivery have therefore dominated the first wave of enterprise AI deployment.
In contrast, many CMIB processes have not lent themselves easily to traditional automation. Their workflows cut across organizational silos and systems, frequently demand bespoke solutions, and rely heavily on human judgment. As a result, automation in these areas requires a shift from a static, rules-based approach to dynamic, goal-driven orchestration. Agentic AI offers the solution.
Why CMIB has been late to the game
Attempts to automate CMIB processes have met with mixed results to date. Unlike retail banking or payments, CMIB processes tend to have characteristics that make automation more difficult:
- highly specialized, executed by small expert teams rather than large workforces
- deeply bespoke, varying by asset class, client, jurisdiction, or transaction structure
- exception-heavy, with frequent breaks from standard process flows
- front-to-back, requiring tight coordination across sales, trading, risk, operations, and finance
Examples abound, including collateral and margin management, trade processing and exception handling, instrument and reference data maintenance, regulatory reporting, and even research production.
While there have been notable successes, they remain the exception rather than the rule. For example, a top ten investment bank recently achieved a roughly 40 percent productivity uplift in its instrument data operations through a tactical automation approach, identifying repetitive processes that could be automated to save time. Such approaches have occasionally struggled to scale, particularly when using rules-based tools such as robotic process automation (RPA). As no two cases are exactly alike, requirements change frequently, rigid logic quickly breaks down, and edge cases proliferate. What begins as automation quickly turns into maintenance overhead.
In short, even when upstream systems and discrete tasks are digitized, CMIB requires significant human orchestration in the “last mile” to the end user.
Enter agentic AI: A potential paradigm shift
Agentic AI—and, more broadly, multi-agent architectures—offers a fundamentally different approach. Rather than automating individual tasks in isolation, agentic systems reason, plan, coordinate, and adapt entire workflows.
At their core, agentic architectures break down complex objectives into sets of interacting agents, each with a defined role. A planner agent, for example, determines what needs to be done and in what sequence. Specialized agents execute discrete steps—retrieving documents, extracting data, validating inputs, running analyses, or drafting outputs. A critic, or verifier, agent continuously assesses quality, adherence to policy, and completeness before results are passed downstream.
This shift is precisely what CMIB has been missing. In fact, the following characteristics of agentic architectures make them uniquely attractive for complex, bespoke domains.
Fast implementation with relatively low incremental investment. Agentic use cases can often be stood up in six to eight weeks, once a core platform and tooling layer are in place (generally a process of a few weeks). While there is an upfront investment in architecture, governance, and enablement, individual deployments tend to be lightweight and economical. Early implementations in post-trade processing at large capital markets firms, for example, have demonstrated that meaningful impact can be achieved without multiyear transformation programs.
Resilience in the face of ambiguity and change. Unlike traditional automation, AI agents are not hard coded around fixed rules. They can learn from context, handle unstructured inputs, and adapt to variation. This flexibility is critical in domains such as KYC, onboarding, and exception management, where requirements change frequently and one-off solutions are often the norm.
Reusable building blocks. Agentic systems encourage modularity. Once an agent for document retrieval, entity extraction, reconciliation, or planning has been developed, it can be reused across multiple processes and business lines. Over time, firms can build an internal “agent library” that accelerates delivery and reduces duplication.
Dynamic, front-to-back workflow orchestration. The planner agent effectively becomes a dynamic workflow engine, determining how agents interact based on the specifics of each case. This is a powerful capability for middle- and back-office processes that span multiple systems and teams, as well as for research and analytical workflows in the front office.
Embedded quality control and risk management. Critic, or verifier, agents provide continuous quality assurance, checking outputs against a “definition of done,” policy constraints, and risk thresholds. This allows quality control to be embedded at both a granular step level and an overall outcome level—a critical requirement in regulated environments.
High-impact use cases across the value chain
The potential applications of agentic AI span the full CMIB life cycle, supporting each of the following activities:
- front office: research development and publishing, bespoke client materials for capital markets sales, and pretrade analysis and documentation
- middle office: trade control, confirmation matching, break investigation, exception management, and regulatory reporting preparation
- back office: collateral and valuation processes, securities settlement, corporate actions processing, and life cycle event management
In many of these areas, agentic AI can go well beyond what stand-alone gen AI tools could achieve. For example, rather than solving isolated problems, such as drafting a document, summarizing data, or answering a query, agentic systems can own an entire outcome. In the future, CMIB firms could even offer clients this capability as a product, much as they offer prime brokerage services today, even extending into agentic flows for research, market data summarization, and other services.
First steps to implementation
The question is no longer whether CMIB can be transformed by AI, but how quickly firms can reimagine their processes, operating models, and talent to take advantage of it. Key aspects of that effort will include the following:
- Quantitative models and complex calculations. Many workflows depend on external pricing models, risk engines, and analytics platforms. Their seamless integration into agentic workflows is still evolving, and firms will need to master this ability—with appropriate controls—to succeed.
- Model validation and verification. Firms must generate robust approaches to testing, validating, and monitoring agent behavior over time, as these will be demanded by regulators and internal risk functions.
- Operating-model design. Firms must strike the right balance between reusable, centralized capabilities and AI that is embedded close to the individual business. Some organizations will favor federated models with AI teams in each unit; others will centralize more aggressively.
- Skills and roles. Teams will need new capabilities—not just to build agents but also to redesign processes and ways of working around them. The result will often be smaller, more cross-functional, and more agile teams interacting directly with AI agents as collaborators rather than tools. We also recommend extending the roles and capabilities of quantitative teams to build agentic flows, or training and embedding “super users” in each area of the business (while using common platforms, prebuilt integrations, and similar tools).
Looking ahead
The industry is still early in this journey, but momentum is building. In fact, several leading institutions have already gone public about their use of agentic approaches within capital markets and investment banking. If early results continue to scale, agentic AI may finally succeed where decades of automation initiatives have struggled—solving last-mile challenges and unlocking meaningful, sustainable productivity across one of the most complex domains in financial services.