US Capital Markets
Top five trends for 2026
Alongside other forces – the shift in political administrations, evolving regulatory priorities, renewed trade and tariff tensions, and heightened socio-economic pressures – the evolution of domestic and global market structures and technologies are compressing decision timelines. We are witnessing a convergence of digital asset maturity and a fundamental redesign of trading windows that is shifting the industry away from traditional market-day constructs and normalizing near-continuous trading.
Businesses must also factor in the risks and rewards of artificial intelligence. Those who fail to master digital assets and AI agents will not only squander a wealth of new opportunities in the ‘always-on’ economy, but risk falling by the wayside in terms of greater automation and efficiency. That said, any deployment must be balanced against regulatory expectations and tightening deadlines.
In 2026, the financial landscape across the United States will continue to experience structural uncertainty that defies traditional market cycles. The ‘wait and see’ ethos has evaporated, and 2026 will be a pivotal year for action and execution.
Major US exchanges aim to make 2026 the year of extended trading, moving toward 23 hours a day, five days a week. More than a change in exchange schedules, 23/5 trading represents a structural inflection point in the convergence of traditional finance (TradFi) and decentralized finance (DeFi). The New York Stock Exchange’s recent announcement of a blockchain-based platform for 24/7 trading of tokenized securities underscores how digital infrastructure is becoming foundational to modern market design.
In parallel, the UK and EU transitions to T+1 settlement serves as a critical modernization waypoint. US practitioners are already well aware that, while not fully real-time, T+1 nonetheless materially compresses post-trade risk, funding windows, and operational latency. As the UK and EU converge on T+1, global firms are pushed toward always-on, event-driven post-trade architectures that naturally align with extended hours and make tokenized, blockchain-based settlement a practical next step rather than a conceptual leap.
By late 2026, harmonized extended trading hours are expected to become the US market standard, with knock-on implications globally. Firms will be required to redesign operating models across multiple time zones, while managing tighter settlement cycles in Europe and near-continuous trading in the US. This shift is inextricably linked to digital assets, as the infrastructure needed to support extended hours increasingly mirrors the always-on, atomic settlement logic of blockchain networks.
Digital assets meet 23/5 trading hours
Firms will be required to redesign operating models across multiple time zones, while managing tighter settlement cycles in Europe and near-continuous trading in the US.
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Beyond adaptation: the era of situational awareness
Those that embed real-time intelligence into decision-making, governance and execution will be positioned to respond decisively.
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Through 2026, market volatility – driven in part by factors such as shifting regulation, trade policy and political cycles – will push firms toward a new operating imperative: continuous situational awareness.
Regulatory frameworks are increasingly fluid, government initiatives are frequently revised or paused, and macro policy signals can change direction with little notice. In this environment, long-range planning alone is insufficient. Many institutions will focus on maintaining operational stability while preserving the agility to pivot rapidly as external conditions evolve.
Situational awareness – defined as real-time visibility into both internal operations and external market, regulatory and geopolitical signals – will become a core competitive capability. Firms will need dynamic playbooks that can be activated in response to sudden regulatory reversals, new infrastructure requirements or market disruptions.
Those that embed real-time intelligence into decision-making, governance and execution will be positioned to respond decisively. Organizations that rely on static plans or reactive monitoring risk falling behind, as change accelerates and uncertainty becomes the norm.
As 2026 gathers momentum, one point is clear: AI agents have arrived. The industry is moving beyond chat-based copilots toward task-specific agents embedded in enterprise workflows. These agents are able to plan, execute, and escalate multi-step processes across end-to-end operational activities, including trade capture support, allocations, confirmations and affirmations, reconciliations and breaks management, client onboarding, surveillance, reporting, and reference data.
However, in US capital markets the winning model will not be ‘full autonomy’, but rather controlled autonomy: agents executing bounded work, with deterministic controls enforcing the non-negotiables, and humans remaining accountable at defined decision points.
Near-continuous operations compress the margin for error. Exceptions do not wait for markets to open, and regulatory obligations do not pause after hours. Agents can provide always-on capacity, but they must be designed with clear accountability boundaries.
In practice, this means agents can triage alerts, draft outputs, reconcile discrepancies and assemble evidence, while still subject to human authorization before making irreversible actions, when policy thresholds are crossed or when inputs are ambiguous.
In 2026, governance also becomes operational. Controls cannot sit ‘alongside’ delivery; they must ship with the use case. Role-based permissions, segregation of duties, audit trails, deterministic fallbacks and continuous monitoring collectively make agentic workflows defensible under scrutiny and scalable across functions.
This urgency is reinforced by upcoming regulation. Firms should prepare for the application of the EU AI Act’s requirements to certain non-high-risk obligations from August 2026, while remaining alert to potential shifts in US federal AI regulation and supervisory expectations.
Ultimately, controlled autonomy is not a constraint on innovation. It is the mechanism that allows institutions to scale agents safely, meet tightening regulatory expectations, and operate effectively in an always-on capital markets environment.
Controlled autonomy: the 2026 operating model for Agentic AI
The industry is moving beyond chat-based copilots toward task-specific agents embedded in enterprise workflows.
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As a result, firms are increasingly treating data governance, lineage and observability as core components of AI risk management, on par with model validation and control frameworks.
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By the first half of 2026, we expect to see a catalog of validated AI use cases that have moved beyond the pilot phase. This marks a fundamental shift from experimentation to industrialization, where AI value is increasingly determined by data quality, lineage, and control rather than model sophistication alone. The most immediate impact will be around cost-containment. For example, AI is being used to automate the software development lifecycle (SDLC) as well as in areas such as product data, reconciliation, data management and reporting. As these use cases scale, firms are discovering that fragmented, poorly governed data is now the primary constraint on AI effectiveness, elevating data architecture and metadata management to first-order design considerations.
One practical application is Capco’s BA Genie, an agentic AI solution that augments the role of Business Analysts. By consuming unstructured inputs such as meeting recordings and project documentation, BA Genie generates comprehensive requirements and testing artifacts – epics, features, user stories, and test cases – while optimizing for quality and compliance.
The effectiveness of this approach is directly tied to the availability of trusted source data, consistent taxonomies, and well-defined controls over inputs and outputs. Potential hallucinations are addressed through a multi-agent approach in which collaborative agent workflows cross-validate and reconcile outputs; separately, a human-in-the-loop review step provides final validation to ensure validity.
This reflects a broader industry trend in which AI accuracy is increasingly enforced through data validation, cross-referencing and provenance rather than relying solely on model tuning. The result is a reliable way to reduce repetitive manual effort, tighten traceability between requirements and tests, and accelerate delivery of higher-quality, auditable deliverables. Critically, this traceability also creates structured data exhaust that can be reused to further train, benchmark, and govern AI systems over time.
The move toward automation is no longer optional. In 2026, firms will focus on building a system of AI agents, allowing them to leapfrog the use of AI for automating individual processes. This represents a shift in fundamentals from task-level automation to data-orchestrated decisioning, where agents coordinate through shared, continuously updated data states.
This will be critical for managing the increased exposure to market volatility inherent in longer trading windows, while surveillance and trade reporting must be always-on to meet new regulatory session tagging requirements (such as NSCC Tag 336 and 715 for session ID and clearing dates). Always-on AI places new demands on real-time, high-integrity data pipelines, as gaps or latency in source data can propagate risk at machine speed.
The priority for 2026 is ensuring that new AI-enabled tools are performing to expectations, while also operating within the strict constraints that prevent AI hallucinations in critical financial workflows. As a result, firms are increasingly treating data governance, lineage and observability as core components of AI risk management, on par with model validation and control frameworks.
The convergence of extended trading hours, digital assets and Agentic AI means that firms must convert the innovative thinking and pilot exercises of 2025 into production-grade operations. Success will depend on a firm’s ability to adopt automated, always-on operations and compliance within a fast-evolving regulatory landscape.
The nimbleness of organizations will prove just as significant as the initial rate of technology adoption. Whether it is securing a supply chain against emerging threats or integrating digital and traditional financial transactions into a single view, institutions must ensure that their systems can pivot at short notice to the demands of the marketplace, regulators and clients. The good news is that by harnessing AI to the needs of the business, organizations will be better placed to survive and flourish in the months and years to come
Conclusion: the year of the agile institution
© Capco 2026, A Wipro Company
The resourcing strategy for capital markets will be characterized by a hybrid process that balances in-house security with fintech agility. While organizations will continue to purchase certain technical solutions, the core management of AI models and sensitive data will likely remain in-house to ensure privacy and regulatory control. This is creating a new demand for ‘AI-forward’ teams that can grow their skillsets beyond the remit of more traditionally defined teams.
Upskilling is the watchword for the coming year. Employees in middle-to-back-office roles may not be replaced, but their jobs will become streamlined as they learn to oversee AI tools. This requires a new governance framework to evaluate how these tools are supervised and how their performance is measured against established institutional parameters. The most successful firms will be those that empower their workforce to become orchestrators of these new technologies rather than passive users.
The human-agent workforce: a hybrid resourcing strategy
Employees in middle-to-back-office roles may not be replaced, but their jobs will become streamlined as they learn to oversee AI tools.
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Operational reality: identifying AI value drivers
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Viewed through a digital asset and Ethereum lens, clear synergies emerge:
Tokenized securities
By 2026, tokenized equities and bonds are expected to scale across both permissioned infrastructures (e.g. digital asset/DAML) and public networks (e.g. Ethereum), combining regulatory control with programmability and interoperability. T+1 in the EU drives straight-through processing and intraday funding discipline, laying the groundwork for on-chain issuance and settlement
Collateral mobility
As markets extend trading hours, real-time tokenized collateral becomes essential to avoid liquidity fragmentation across regions. T+1 materially increases the velocity and precision required for collateral movement. Here, digital assets’ institutional-grade workflows and liquidity reach converge to enable margining, cross-venue reuse, and intraday optimization
Booking models and operating alignment
Compressed settlement in Europe and extended trading in the US force firms to rethink legal entity structures, trade booking and surveillance models, supporting continuous coverage and alignment with Asian client activity
Marc Biro
Managing Principal marc.biro@capco.com
Matt Rodgers
Managing Principal matthew.rodgers@capco.com
Success will depend on a firm’s ability to adopt automated, always-on operations and compliance within a fast-evolving regulatory landscape.
Wealth Management
Originally published in Traders Magazine
