Mark Govoni, chief executive, Liquidnet
As we move into 2026, one of the most interesting shifts we’re going to see is how the smartest market participants use AI to shift from being overwhelmed by data to harnessing contextual, personalised, and decision-ready insights where routine, repeatable research will increasingly be automated.
That should free people to concentrate on what truly adds value: deep domain expertise and informed decision-making.
This evolution changes the competitive edge and 2026 should be the year that AI truly starts to power how clients and trading venues interact. Success will come from combining internal analytics with external expertise, embedding AI and alternative data into decision-making frameworks, and automating the everyday.
Jim Kwiatkowski, chief executive, LTX
With data science and AI adoption accelerating, buy-side trading desks are poised to integrate AI into their workflows more deeply over the next 12 to 24 months – transforming pre-trade decision-making and trade execution.
Electronic trading has already improved liquidity by tightening pricing and expanding access to two-way liquidity. However, challenges persist, with rising execution and data costs and in executing large trades efficiently.
To overcome these, firms will increasingly turn to data-driven strategies and the use of AI to enhance liquidity discovery, select counterparties and optimise pricing.
Nearly 85% of firms plan to increase AI use in corporate bond trading over the next year, up sharply from 57% in 2024. This surge signals a pivotal shift toward an AI-powered bond market.
As advanced analytics and machine learning become integral to trading, they promise to expand the universe of bonds that participants can analyse, uncover hidden liquidity, select likely counterparties, and lower transaction costs – marking the beginning of a new era in bond market efficiency and innovation.
Dharrini Bala Gadiyaram, global head of buy-side risk, treasury and compliance solutions, Bloomberg
As 2026 approaches, buy-side firms are moving from AI pilots to fully embedding AI across the investment lifecycle – research, portfolio construction, trading, risk, and compliance – to drive scaled efficiencies.
At the same time, regulatory shifts are reshaping opportunities in private markets. The SEC’s decision to lift restrictions on closed-end funds holding private funds could open new retail access to private investments, setting the stage for more growth next year. Yet, this expansion comes with caution – high-profile defaults this year underscored potential risks.
As growth and risk converge, maintaining a total portfolio view across public and private assets, supported by transparent and credible valuations, is more critical than ever.
Meanwhile, global dynamics are driving another key focus area: FX exposure. Asset managers with multi-currency portfolios have felt the strain of geopolitical volatility, heightening the importance of robust FX hedging.
This has evolved into a strategic advantage, fuelling demand for real-time exposure management, advanced analytics, and straight-through processing that minimises both time and risk.
Rupert Brown, chief technology officer, Evidology Systems
Cyber attacks will continue to grow in 2026 affecting all consumer supply chains as well as corporate and governmental ones across the globe – at some stage there will be a recognition that AI cannot act as a dynamic defence mechanism against these due to the (natural) lack of information sharing amongst victims and the attack techniques being too complex and unique to be discerned systematically.
This will mark a turning point in the trust and value of the AI marketplace, with significant knock-on startup consolidations and failures and a broader cooling of investor confidence.