How Capital Markets Are Scaling AI
Artificial intelligence has become one of the defining topics across financial services and financial technology. While many organisations have spent the last few years experimenting with AI tools and identifying potential use cases, the conversation is now changing. The question is no longer whether AI can create value. Instead, financial institutions are asking how they can scale AI effectively, safely and sustainably across their organisations.
In this episode of FinTech Focus TV recorded at TradingTech Summit 2026, host Ian Bailey sits down with Andre Nedelcoux, Global VP Technology, Financial Services and Insurance at Intellias, to explore how capital markets firms are moving beyond AI experimentation and towards enterprise-wide adoption. Recorded at TradingTech Summit, the discussion examines the challenges, opportunities and strategic considerations shaping AI implementation across the financial markets landscape.
For financial services leaders and technology professionals, the conversation provides valuable insight into how artificial intelligence is transforming workflows, modernising infrastructure and reshaping the future of work within capital markets.
The Industry Has Moved Beyond the AI Experimentation Stage
One of the key themes discussed throughout the episode is the significant shift in attitudes towards artificial intelligence across financial markets.
Andre explains that the industry has largely moved beyond the early phase of AI adoption, where organisations were still questioning whether the technology could provide meaningful business value. Today, most firms understand that AI has practical applications and can deliver measurable benefits across multiple business functions.
As a result, the conversation has evolved. Rather than asking whether AI works, organisations are now focused on understanding how they can scale successful AI initiatives across entire businesses.
This transition marks an important stage in the maturity of AI adoption within financial services. Initial pilot programmes and proof-of-concept projects have demonstrated potential value, but scaling AI requires a very different level of planning, governance and technical capability.
For many financial institutions, the challenge is no longer identifying use cases. The challenge is building the infrastructure and organisational capability necessary to support AI at enterprise scale.
Why Enterprise AI Platforms Matter
A major topic discussed during the conversation is the growing importance of enterprise AI platforms.
Andre highlights that organisations seeking to implement AI across multiple business lines must establish common frameworks and capabilities that allow AI systems to operate safely and effectively within regulated environments.
This includes ensuring AI systems remain observable, resilient and compliant with industry regulations. Financial institutions must understand how AI models are behaving, how decisions are being made and whether those decisions remain aligned with regulatory requirements.
The importance of governance becomes even greater as organisations begin deploying autonomous AI agents.
These systems are increasingly capable of carrying out complex tasks independently, making it essential for firms to maintain oversight and control. Organisations need confidence that AI agents are using trusted internal data sources, applying approved business logic and operating within established guardrails.
Without these capabilities, scaling AI becomes significantly more difficult.
According to Andre, enterprise AI platforms provide the foundation needed to support widespread adoption. They create consistency across the organisation while enabling multiple teams and business units to leverage AI without introducing unnecessary risk.
As artificial intelligence continues to become embedded within financial services operations, enterprise AI capabilities are likely to become a critical component of long-term technology strategy.
Owning the Feedback Loop
Another important concept discussed during the episode is the idea of owning the feedback loop.
As AI systems become increasingly sophisticated, organisations need mechanisms that allow them to monitor performance continuously and improve outcomes over time.
Andre explains that enterprise AI environments generate valuable data about how systems perform in production. This visibility enables organisations to identify issues, measure effectiveness and optimise performance on an ongoing basis.
Rather than treating AI as a one-time implementation project, successful organisations view it as a continuously evolving capability.
This mindset reflects broader trends across financial technology and software engineering, where iterative improvement has become essential for maintaining competitive advantage.
For financial institutions, the ability to learn from AI performance and refine systems accordingly may ultimately become one of the most important differentiators between organisations that achieve meaningful AI adoption and those that struggle to move beyond isolated pilots.
AI in Trading Workflows and Capital Markets Operations
The discussion also explores some of the practical ways AI is currently being used within capital markets.
One area receiving significant attention is the integration of AI into trading workflows.
Rather than replacing traders entirely, AI is increasingly being used to automate repetitive tasks, improve efficiency and enhance decision-making processes.
By embedding artificial intelligence into existing workflows, firms can streamline operations while allowing employees to focus on higher-value activities.
This reflects a broader trend occurring across financial services technology, where AI is being deployed to augment human expertise rather than simply automate jobs.
As capital markets become more complex and data-intensive, the ability to process information rapidly and extract meaningful insights becomes increasingly valuable.
Artificial intelligence provides firms with new tools to manage this complexity and improve operational effectiveness.
For employers hiring technology professionals within trading technology, quantitative finance and capital markets infrastructure, demand for AI-related skills is likely to continue increasing as adoption accelerates.
The Rise of Autonomous AI Agents
While workflow automation represents one stage of AI maturity, Andre also discusses the growing role of autonomous AI agents.
These systems go beyond simple automation by performing more sophisticated activities independently.
In some cases, autonomous agents can handle tasks that would be difficult or impractical for humans due to complexity, scale or speed requirements.
This development represents one of the most exciting areas of artificial intelligence within financial markets.
However, it also reinforces the need for strong governance frameworks and enterprise AI controls.
The more autonomy organisations grant to AI systems, the more important it becomes to ensure those systems operate safely, reliably and in accordance with regulatory requirements.
As the technology continues to evolve, autonomous agents may play an increasingly important role across trading operations, data management, compliance functions and business process optimisation.
AI as a Modernisation Tool
One of the most compelling insights from the conversation centres on AI's role as a technology modernisation tool.
Many financial institutions continue to rely on legacy systems that were never designed for the demands of today's markets.
Modern financial markets increasingly operate around the clock, creating pressure for organisations to support 24/7 services, faster innovation cycles and greater operational flexibility.
According to Andre, AI is helping firms modernise legacy environments at a scale and pace that would previously have been difficult to achieve.
Rather than relying solely on traditional software development approaches, organisations can leverage AI to accelerate transformation initiatives and reduce barriers to change.
This is particularly important within capital markets, where replacing legacy infrastructure has historically been costly, complex and resource-intensive.
Artificial intelligence offers a new pathway for modernisation, enabling firms to transform systems more efficiently while maintaining business continuity.
For technology leaders, this represents a significant opportunity to address long-standing infrastructure challenges while positioning organisations for future growth.
Using AI to Address Regulatory Compliance Challenges
Regulatory compliance remains one of the most important priorities across financial services.
As regulations continue to evolve, organisations face increasing pressure to manage data quality, maintain transparency and demonstrate compliance across complex operational environments.
Andre explains how AI is being used to tackle some of these challenges directly.
Areas such as data lineage, data modelling and data quality management can be enhanced through AI-powered solutions that automate traditionally labour-intensive processes.
Rather than viewing regulation as a barrier to innovation, firms are increasingly exploring how AI can support compliance objectives while improving efficiency.
This represents a particularly important development for financial institutions operating within heavily regulated markets.
The ability to automate compliance-related activities could help organisations reduce operational risk while improving overall effectiveness.
As adoption grows, regulatory technology and AI-driven compliance solutions are likely to become increasingly important areas of investment across financial services.
The Changing Business Model of Technology Services
Beyond discussing AI adoption within financial institutions, Andre also reflects on how artificial intelligence is transforming the technology services industry itself.
Historically, many consulting and services businesses have operated using models based largely on billable hours and workforce scale.
Artificial intelligence is challenging these traditional approaches.
As AI increases productivity, organisations can deliver larger outcomes with smaller teams.
This creates opportunities for technology providers to shift their focus away from resource-based pricing models and towards outcome-based delivery.
Andre describes how Intellias is actively embracing this transformation, focusing on achieving greater value for clients through AI-powered delivery models.
This evolution reflects broader changes occurring across the technology sector.
Organisations are increasingly evaluating partners based on their ability to deliver measurable business outcomes rather than simply providing additional personnel.
For recruitment professionals, this shift may also influence future hiring trends as demand evolves towards AI-enabled capabilities, strategic expertise and specialised knowledge.
The Growing Importance of AI Talent in Financial Services
Throughout the conversation, one theme remains consistent: the pace of change is accelerating.
Andre notes that developments which previously occurred over months now happen within weeks or even days.
This rapid evolution creates challenges for organisations attempting to keep pace with emerging technologies while maintaining operational stability.
It also has significant implications for hiring.
As AI adoption accelerates, demand for professionals with expertise in artificial intelligence, machine learning, data engineering, software development, cloud infrastructure and enterprise technology is likely to continue growing.
Financial institutions seeking to scale AI successfully will require access to highly skilled technology talent capable of building, governing and optimising AI systems.
For employers, attracting and retaining these professionals may become a critical competitive advantage.
For candidates, the rise of AI presents exciting opportunities to contribute to some of the most transformative projects currently taking place within financial technology and capital markets.
From a FinTech recruitment perspective, organisations that combine strong technology strategies with compelling employee value propositions are likely to be best positioned to secure the talent required for long-term success.
The Future of AI in Capital Markets
As the conversation concludes, Andre emphasises that organisations can engage with AI regardless of where they are on their journey.
Some firms are only beginning to explore artificial intelligence and need support establishing foundations. Others have already achieved significant maturity and are looking for new ideas, fresh perspectives and advanced capabilities.
What remains clear is that AI is no longer a future concept within financial services.
It is already influencing how organisations operate, modernise infrastructure, manage compliance, serve customers and compete within increasingly complex markets.
The challenge now is not whether to adopt AI, but how to implement it effectively at scale.
For capital markets firms, success will depend on building the right foundations, investing in appropriate governance frameworks and developing the skills required to unlock long-term value.
As artificial intelligence continues to reshape financial technology, organisations that can combine innovation with control will be best positioned to lead the next phase of transformation.
The discussion between Ian Bailey and Andre Nedelcoux provides a valuable snapshot of this evolving landscape. It highlights both the opportunities and responsibilities associated with enterprise AI adoption and offers practical insight into how leading organisations are approaching one of the most significant technology shifts facing financial services today.
