Stephan J Aguirre, senior fixed income trader, Janus Henderson
As we look ahead to 2026, the trading landscape is rapidly being redefined by technology. Artificial intelligence and quantitative systems are no longer optional – they are central to alpha generation and execution. By leveraging machine learning and predictive analytics, trading desks can uncover patterns invisible to traditional models, enabling faster, smarter decisions.
Next-generation computing, including quantum capabilities, will push systematic strategies to new heights. These tools allow us to solve complex optimisation problems in real time, enhancing portfolio construction, trade execution, and risk management for superior outcomes.
Blockchain-driven tokenisation is opening new asset classes and venues, creating liquidity opportunities that were unimaginable a few years ago. For clients, this means access to innovative markets with greater transparency, efficiency, and liquidity.
To support these advances, infrastructure renovation is critical. Leveraging an integrated OMS and EMS, optimising cloud architecture, and increasing computer power ensures speed and resilience – key ingredients for competitive execution.
Finally, governance and regulation will shape adoption. Our team is committed to aligning cutting-edge strategies with evolving frameworks, ensuring compliance while delivering performance.
Technology is not just transforming trading – it’s redefining what’s possible. Our desk is ready to harness these innovations, embracing the value that execution supersedes intention, for the benefit of all our clients.
Justin Peterson, chief technology officer, Tradeweb
We’re entering what could be described as the ‘electronic trading 2.0’ era – where technologies like AI and distributed ledger technology are redefining how markets operate.
AI not only supports execution, but also transforms how traders interact with data and insights, from intelligent chat interfaces to predictive analytics that provide more natural and intuitive access to information.
Alongside distributed ledger technology, these innovations represent the two pillars of the next phase of electronic markets – driving greater efficiency, transparency, and productivity across the trading lifecycle as we move into next year.
Jens Hachmeister, head of issuer services and new digital markets, Clearstream
Writing, research, summarise: we all installed at least one new AI app on our phone in the last months. And the market for AI technologies is expected to grow from already impressive $244 billion to over $800 billion in 2030.
Next year will see an acceleration of operational deployment of AI in the institutional finance space, fundamentally changing intra- and cross-organisational processes. Be it product offerings with AI-as-a-service or in-house support in operational tasks. But first, we need to learn how to walk in order to run: there is still significant data harmonisation needed to build a solid foundation to achieve this deep personalisation, transparency and data insight.
Concurrently, digital asset adoption will mature substantially. The adoption of platforms like D7 DLT will be crucial for bringing liquidity to tokenised assets with financial market infrastructures beginning to digitise their vast assets under custody. A core strategic principle will be ensuring interconnectivity between TradFi and DeFi to allow for a seamless multi-platform ecosystem experience.
These advancements should never happen in isolation or only for a great headline; they have to tackle the industry’s most pressing challenges, including margin compression, market harmonisation, and the operational demands of the transition to T+1.
Michiel Verhoeven, chief executive, Xceptor
In 2026, AI adoption will continue to dominate headlines. Yet, without a clear strategy, institutions risk creating a money pit. Firms should ask themselves: “what are our pain points and how can we achieve the necessary outcomes with AI?”. Without the right infrastructure, data management, and clarity of purpose, technology is not a magic fix.
For example, AI can drastically reduce time spent extracting information from complex tax documents, and agentic AI could augment productivity further by proactively solving problems and triggering downstream actions. But without addressing inconsistent data formats, firms will face more errors and time spent correcting them, or even financial and non-compliance risks.
Issues with data reliability, quality, and flow are common causes of failed AI projects – flawed data creates flawed AI. Firms must invest in platforms that normalise, enrich, and validate data across fragmented systems before scaling AI projects.
Scott Frisby, head of strategy for Europe, Elavon
After months of proof-of-concept announcements from major payments processors, 2026 will see agentic commerce move from theoretical promise to practical implementation.
AI agents will begin handling complex shopping tasks such as researching products, curating selections, and even negotiating prices on consumers’ behalf. The true breakthrough lies in bot-to-bot transactions, where merchants’ AI systems interact directly with consumers’ agents to complete purchases.
This fundamentally improves both sides of the transaction: consumers save time while gaining highly personalised shopping experiences, whilst merchants gain unprecedented insight into purchase intent and consumer preferences. However, widespread adoption hinges on resolving critical operational and regulatory challenges around liability, fraud prevention, and compensation models for large language models.
The payments industry is working double-time to establish these frameworks, from developing new fraud detection protocols through to creating standards for agent authentication. 2026 will be a very interesting year as these developments take hold.