AI’s Inflection Point: The Moment Capital Markets Must Act or Be Left Behind

Richard Walker
Jun 19
9 min read

Why Leading Firms Are Accelerating AI Strategies to Capture Competitive Advantage, Navigate Regulations, and Drive Measurable ROI

AI Transformation in Capital Markets

Artificial Intelligence (AI) is dramatically transforming capital markets, reshaping everything from trading and risk management to customer engagement. Adoption of AI technologies by financial institutions has reached a tipping point: over 71% of organizations report using AI in their finance operations in some form, a figure expected to rise to 83% within three years technologyforyou.org. This rapid uptake is fueled by the promise of efficiency and competitive advantage. Global market projections reflect this trend – the AI in finance market is forecast to grow from about $38 billion in 2024 to over $190 billion by 2030, at an annual growth rate of roughly 30% marketsandmarkets.com. Such growth underscores AI’s emergence as a critical frontier technology in financial services.

High Adoption, High Hopes – and Measured Returns

Financial institutions are moving beyond expertation; many are seeing tangible benefits from AI deployments. New research by KPMG International kpmg.com found that among nearly 3,000 organizations surveyed worldwide, a majority are investing in AI across various finance functions. Critically, 57% of “AI leader” firms say the ROI from AI is exceeding their expectations, compared to 29% of other firms. These leading organizations credit AI with delivering better data quality, faster insights, lower costs, and improved operational effectiveness. Common use cases include automating financial reporting, treasury and risk management, and even tax compliance, indicating that AI’s footprint is widening across back-office and middle-office processes.

However, the gains are not universal. A recent Boston Consulting Group (BCG) executive survey highlights a more measured picture of returns on investment. According to BCG, the median ROI from AI initiatives in finance is only about 10%, and roughly one-third of finance leaders report little to no gains so far bcg.com. In other words, while some firms are achieving breakout success with AI, others struggle to move beyond pilot projects. The contrast between KPMG’s “AI leaders” and the rest suggests a growing gap: companies with the right implementation tactics and talent are pulling ahead, while others face challenges with data quality, integration, and scaling of AI solutions. Notably, unclear ROI was cited as a deterrent by about a quarter of firms in one industry survey allvuesystems.com, reflecting caution among those still seeking a concrete business case for AI. These findings underscore that simply adopting AI is not enough – execution and strategy are key. High-ROI teams tend to focus on high-value use cases, embed AI into broader transformations, and invest in skills and collaboration, whereas lower performers often lack these success factors bcg.com.

Real-World Impact: Efficiency Gains and New Capabilities

Despite varied ROI outcomes, clear examples have emerged of AI delivering competitive advantage in capital markets. Major banks and asset managers are leveraging AI to enhance client service, boost revenues, and manage risk in real time. For instance, JPMorgan Chase reported that its AI-driven advisory tools have “supercharged” the speed at which wealth managers can service clients during volatile markets reuters.com. The bank’s private wealth advisors now find relevant information up to 95% faster, spending far less time on manual research and more time engaging with clients.

According to JPMorgan, these tools helped handle an onslaught of client inquiries during recent market turbulence and were instrumental in increasing gross sales by 20% year-on-year from 2023 to 2024 reuters.com. Mary Callahan Erdoes, the CEO of JPMorgan’s asset and wealth management unit, noted that AI systems rapidly analyzed clients’ portfolios and preferences, allowing advisors to tailor investment advice quickly even amid chaos . Importantly, the productivity gains aren’t just theoretical – JPMorgan projects that its new AI “Coach” application will enable advisors to expand their client loads by 50% in the next 3–5 years, with AI handling much of the preparatory research and routine queries This illustrates how AI can effectively increase human capacity in capital markets roles.

Other leading firms have made similar moves. Goldman Sachs is rolling out a generative AI assistant to support its bankers and traders, while Morgan Stanley developed an AI-powered chatbot (built with OpenAI technology) to help financial advisors answer client questions by tapping a vast knowledge base. In trading and investment management, AI-driven algorithms and machine learning models are being used to detect market patterns and optimize strategies – often in ways humans cannot. For example, exchange operators and proprietary trading firms employ AI for ultra-fast market-making and anomaly detection in order flow, improving liquidity and efficiency. Even in mid-office functions like compliance and fraud detection, AI systems can scan transactions or communications far more quickly and accurately than traditional methods, identifying risks that would be hard to spot otherwise.

These use cases demonstrate that AI’s impact in capital markets spans front-office revenue generation, client experience, and back-office risk mitigation.

That said, the human factor remains critical. Industry leaders emphasize that AI augments rather than replaces talent. AI can automate rote tasks and surface insights, but skilled professionals are needed to interpret results, make judgments, and maintain client trust. As JPMorgan’s experience shows, combining AI speed with human advice can enhance service – but it also requires training staff to work effectively with AI tools. Notably, JPMorgan invested $17 billion in technology last year and has put generative AI capabilities on over 200,000 employee desktops (with more than half of employees using them multiple times daily). The firm’s leadership framed this as “democratizing AI” internally to empower its workforce, rather than limiting these tools to a small team. Across the industry, we see a growing emphasis on upskilling employees and fostering a culture where man and machine collaborate. AI can handle data-crunching and pattern-recognition at scale; humans provide context, creativity, and ethical oversight.

Regulatory Landscape: Governance and Guidelines Evolve

The rapid rise of AI in financial markets has prompted regulators and policymakers to respond with new guidance to ensure these technologies are used responsibly. In the United States, the Department of the Treasury has taken a lead in assessing AI’s implications for the financial sector. In December 2024, the U.S. Treasury released an official report on the “Uses, Opportunities, and Risks of Artificial Intelligence in Financial Services,” which built on a public Request for Information earlier in the year home.treasury.gov. The Treasury’s report highlights the increasing use of AI across finance – from credit underwriting to trading – and recognizes AI’s potential to broaden access to services and improve efficiency. However, it also underscores new risks, such as model bias, data privacy issues, and reliance on third-party AI providers Notably, the report calls for a coordinated approach to AI governance. It recommends continued collaboration among U.S. and international regulators to develop consistent, robust standards for AI in finance, and to identify any gaps in existing regulatory frameworks It also urges financial firms to review AI use cases for legal compliance and fairness before deployment, and to periodically reassess those use cases as the technology and rules evolve. In essence, U.S. regulators are signaling that the same principles that underpin financial regulation – safety, soundness, consumer protection – must be rigorously upheld as AI is adopted. We can expect further supervisory guidance on areas like model risk management for AI, validation and testing requirements, and oversight of third-party tech providers.

In Europe, regulatory efforts are advancing on multiple fronts to ensure sound AI governance in capital markets. The European Securities and Markets Authority (ESMA), which oversees securities regulators across the EU, issued its first formal guidance on AI for investment firms in mid-2024 morganlewis.com. ESMA’s public statement (released in May 2024) provides initial guidelines for firms using AI in the provision of retail investment services. This guidance emphasizes that when brokers or asset managers employ AI – whether for client profiling, robo-advisory, algorithmic trading, or even AI-driven customer support – they must still meet all their obligations under MiFID II, the EU’s core investor protection directive In practice, that means firms should have appropriate governance and oversight of AI models, ensure transparency to clients about the use of AI, and safeguard against conflicts of interest or biases that could harm clients. ESMA stressed that management boards of financial firms are “pivotal” in maintaining control over AI tools – they need an understanding of how AI is used in their business and must foster a culture of accountability and risk management around these technologies. Additionally, firms should ensure AI outputs are explainable and that staff (and clients) know when they are interacting with AI vs human advice This early guidance foreshadows more formal rules to come, as the EU finalizes broader AI regulations. Notably, the EU’s Artificial Intelligence Act – a landmark law setting comprehensive requirements on AI systems based on risk levels – is on the horizon, and financial services will be part of that regime once it comes into force.

European regulators are also addressing operational and resilience risks associated with digital transformation. A major new regulation, the Digital Operational Resilience Act (DORA), comes into effect for EU financial entities on 17 January 2025. DORA establishes a harmonized framework to ensure banks, investment firms, insurance companies, and other financial institutions can withstand and respond to ICT-related disruptions – including issues that might arise from heavy reliance on AI and other technologies mayerbrown.com. The law compels firms to implement stringent ICT risk management practices, conduct regular resilience testing (e.g. cyber penetration tests), report major cyber incidents, and manage risks from third-party tech providers under regulatory oversight. In essence, DORA mandates that as financial companies digitize (and adopt AI, cloud, etc.), they must fortify their technology governance. This includes having robust continuity plans, board-level accountability for cyber/tech risk, and contractual controls on outsourcing – all of which apply to AI vendors and cloud services supporting AI models. Regulators like ESMA, together with the other European Supervisory Authorities, will oversee compliance with DORA’s requirements on an ongoing basis. The introduction of DORA signals that operational resilience is now a core part of financial regulation, right alongside capital or liquidity requirements. Firms deploying AI in critical processes will need to evaluate how those systems fit into their ICT risk frameworks and ensure they are resilient (for example, having fallback arrangements if an AI service fails or is corrupted).

Conclusion

AI’s transformative impact on capital markets is undeniable – it is enabling faster decision-making, unlocking new insights from data, and creating more personalized financial services. The capital markets ecosystem is entering a new era where human expertise and machine intelligence work in tandem. Early adopters have demonstrated tangible benefits: higher productivity, improved client outcomes, and even revenue growth driven by AI-assisted strategies. Yet the journey is just beginning. Many organizations are still navigating challenges around data quality, model transparency, talent shortages, and integration of AI into legacy systems. The ROI on AI will continue to vary widely, rewarding those who pair technology investments with thoughtful change management and strategic focus.

Executives driving AI transformation must therefore combine ambition with diligence. This means not only experimenting with cutting-edge AI solutions but also establishing the governance structures to use them responsibly. As we have seen, regulators are closely watching this space and proactively shaping guidelines to ensure that innovation does not come at the expense of market integrity or customer protection. Compliance with emerging rules – from U.S. Treasury’s AI risk management expectations to ESMA’s guidance and DORA’s resilience standards – should be viewed as part and parcel of an AI strategy. These frameworks ultimately promote trust in AI systems, which is essential for their long-term adoption in finance.

In summary, AI offers tremendous opportunities in capital markets: more efficient operations, better risk-adjusted returns, and enhanced client experiences. The firms that succeed in this transformation will be those that invest not only in technology, but also in people, processes, and controls around that technology. By grounding AI initiatives in clear business value and robust oversight, capital markets players can turn AI from a shiny new tool into a sustainable source of competitive advantage. The transformation is well underway – and with prudent stewardship, AI will help capital markets become more transparent, efficient, and inclusive in the years ahead.