What Happens When AI Stops Being an Experiment

The Future of AI in Capital Markets Starts with Moving Beyond Experimentation

Artificial intelligence has dominated conversations across financial services over the last few years. Every technology conference, board meeting and product roadmap seems to include AI as a strategic priority, yet the financial technology industry is rapidly reaching a new stage in its adoption journey. The discussion is no longer centred on whether firms should embrace AI. Instead, the focus has shifted towards a much more complex challenge: how to turn promising AI experiments into secure, scalable, production-ready solutions that deliver measurable business outcomes.

That was the central theme of this episode of FinTech Focus TV, where Toby Babb welcomed Tej Sidhu, President and Chief Product Officer at Genesis Global, alongside David Perkins, Executive Vice President, Sales and Strategic Growth. Together, they explored how artificial intelligence is reshaping capital markets technology, why governance is becoming the defining issue for financial institutions, and how organisations can modernise legacy systems without creating new operational risks. Throughout the discussion, they highlighted why successful AI adoption depends not simply on having access to powerful models, but on building the frameworks, controls and infrastructure that allow those models to operate safely within highly regulated financial environments. 

For firms operating across investment banking, asset management, trading technology and broader financial services, these conversations are becoming increasingly relevant. They also have important implications for the future of FinTech recruitment, as organisations look to hire software engineers, product managers, AI specialists and technology leaders capable of delivering large-scale digital transformation.

AI in Capital Markets Is Entering a New Phase

The conversation begins with one of the biggest topics affecting financial technology today: artificial intelligence.

Rather than focusing on AI as another technology trend, Tej explains why he believes the current moment represents one of the most exciting periods in his three decades working across financial markets technology. Unlike previous waves of innovation, today's AI capabilities allow organisations to tackle challenges that previously felt impossible to solve.

For financial institutions, these include reducing operational costs, simplifying increasingly complex technology estates, improving compliance, strengthening security and reducing vendor sprawl. What makes today's environment different is that AI now offers practical ways of addressing these longstanding business problems rather than simply automating isolated tasks.

Toby agrees, highlighting that innovation across the industry has accelerated dramatically. More importantly, organisations are beginning to move beyond discussing AI in theory and are actively looking for ways to solve genuine commercial problems through technology.

David adds another perspective by explaining that AI has quickly become essential to how technology firms position themselves within the market. Organisations without a credible AI strategy increasingly risk falling behind both commercially and competitively. However, he makes an important distinction between businesses that genuinely integrate AI into their products and those that simply market themselves as AI-enabled without meaningful capabilities underneath. According to David, customers and investors are becoming increasingly capable of recognising the difference.

Financial Technology Recruitment Will Be Driven by AI Delivery

One of the strongest themes throughout the discussion is that organisations no longer need convincing that AI matters.

Instead, they need people capable of delivering it.

This has significant implications for the future of financial technology recruitment. As AI becomes embedded within software development, financial institutions require professionals who understand not only machine learning and automation, but also software engineering, governance, enterprise architecture and regulatory requirements.

For recruitment businesses specialising in financial technology, this changing landscape is already influencing hiring priorities. Demand continues to grow for experienced software engineers, product managers, enterprise architects, AI specialists and technology leaders who can bridge the gap between innovation and implementation.

The conversation reinforces that successful AI adoption is becoming less about purchasing software and more about assembling teams capable of delivering transformation programmes safely and effectively.

Why Moving AI from Experimentation to Production Matters

Perhaps the defining insight from the episode comes when Tej describes the biggest misconception surrounding AI adoption.

He explains that the industry is not suffering from a shortage of AI models, pilots or proof-of-concept projects.

Instead, organisations are struggling with something far more difficult.

They need to move those successful experiments into governed, scalable production environments.

For Genesis Global, that transition has become the company's primary focus. Rather than building AI demonstrations, the organisation helps financial institutions operationalise artificial intelligence within regulated capital markets environments.

This distinction is crucial.

Developing an AI prototype has become relatively straightforward thanks to advances in large language models and coding assistants. Deploying those solutions into live trading environments while maintaining governance, resilience, auditability and security is an entirely different challenge.

For capital markets firms operating under strict regulatory oversight, these production requirements determine whether AI creates competitive advantage or introduces unacceptable operational risk. 

From Selling Software to Delivering Outcomes

Another major theme explored throughout the conversation is how customer expectations are changing.

Historically, enterprise software companies focused on selling licences.

Increasingly, clients are purchasing outcomes instead.

Tej explains that Genesis Global was founded with a different philosophy from many traditional software vendors. Rather than locking customers into proprietary systems, the company focused on identifying common design patterns across capital markets applications before building reusable microservices that accelerate software delivery.

This approach becomes even more valuable within an AI-driven environment.

Instead of treating AI as another standalone product, Genesis uses it to accelerate the delivery of complete business solutions.

The discussion illustrates how organisations are beginning to evaluate technology partners differently. Success is becoming less about owning proprietary software and more about helping clients solve measurable business problems while maintaining flexibility for the future.

This represents an important evolution across the broader financial technology industry, where businesses increasingly expect technology partners to become long-term transformation partners rather than software suppliers.

AI Governance Is Becoming the Biggest Competitive Advantage

While public discussion often focuses on AI's creative capabilities, the episode repeatedly returns to governance.

Tej explains that AI-assisted coding has rapidly become a reality across both business and technology teams. Product managers can now build surprisingly sophisticated applications themselves, while software engineers are using AI coding tools to accelerate development dramatically.

However, increased development speed creates an entirely new challenge.

If organisations can produce software significantly faster than before, how do they prevent duplication, inconsistency and uncontrolled growth across their technology estates?

Financial institutions cannot simply allow hundreds of independently developed AI applications to emerge without oversight.

Instead, they require governance frameworks that maintain consistency while allowing innovation to continue.

Genesis positions itself within this challenge by providing the controls, standards and development frameworks that enable organisations to adopt AI safely without sacrificing speed or flexibility.

This emphasis on governance reflects a wider trend emerging throughout capital markets technology. Competitive advantage increasingly depends not only on adopting AI quickly, but on deploying it responsibly within highly regulated operating environments. 

Vibe Coding Changes Software Development, But Not Responsibility

One of the most fascinating discussions centres around the emergence of "vibe coding."

Tej explains how business users can now build surprisingly capable applications over a weekend before presenting them to internal technology teams.

While this dramatically increases innovation, it also changes expectations across organisations.

Technology teams are increasingly asked why production deployment still takes months when prototypes can now be generated almost instantly.

The answer lies in everything surrounding the code itself.

As David explains, writing code represents only part of software delivery. Security, compliance, resilience, deployment, maintenance and governance remain essential components of enterprise software engineering, particularly within financial markets.

This distinction reinforces one of the episode's core messages.

Artificial intelligence is making software creation dramatically easier.

Building secure, scalable and production-ready financial technology remains a specialist discipline requiring experienced engineers, product leaders and governance professionals.

As AI continues transforming financial services, organisations that recognise this difference will be far better positioned to unlock its full potential.

Legacy Technology Remains One of Financial Services' Biggest Challenges

While artificial intelligence has become the headline topic across financial services, the conversation makes it clear that many of the industry's biggest obstacles remain deeply rooted in legacy technology. For large banks, brokers and market infrastructure providers, years of acquisitions, evolving regulation and changing business priorities have resulted in increasingly complex technology estates that are expensive to maintain and difficult to modernise.

Tej explains that many institutions still operate hundreds of tactical applications alongside large enterprise platforms. Some of these systems are business critical, while others have simply accumulated over time, creating operational risk, duplicated functionality and rising technology costs. Rather than replacing every system outright, Genesis Global helps firms modernise these environments by introducing contemporary interfaces and frameworks that allow legacy applications to participate in modern AI-driven workflows. 

The discussion also highlights a broader challenge facing financial institutions. Technology spending continues to rise faster than revenue growth, forcing leadership teams to rethink where investment should be focused. Instead of adding yet another platform, organisations are increasingly looking to simplify their technology estates, reduce vendor dependence and remove unnecessary operational complexity. AI presents a unique opportunity to accelerate this process, but only when deployed with the appropriate governance and architectural foundations.

For organisations investing in digital transformation, this reinforces the growing demand for experienced software engineers, enterprise architects, cloud specialists and technology leaders. As a specialist FinTech recruitment business, Harrington Starr continues to see firms across capital markets seeking professionals capable of modernising complex technology environments while supporting long-term business growth.

Why AI Needs Structure Rather Than Unlimited Freedom

One of the most insightful moments in the discussion comes when Tej addresses a common misconception surrounding AI-assisted development.

Many organisations assume that because AI can generate software more quickly, it should simply be allowed to build applications autonomously.

Instead, he argues that successful AI depends entirely on providing structure.

Rather than allowing AI models to generate unrestricted code, Genesis provides established frameworks, reusable components and architectural standards that guide development towards secure, scalable outcomes. AI becomes dramatically more valuable when operating inside carefully designed guardrails instead of creating entirely new systems from scratch.

David expands on this concept by introducing Genesis Create, explaining how the platform enables organisations to leverage existing libraries of capital markets microservices. Instead of asking an AI model to write every line of code for a complex trading platform, the model assembles proven building blocks before generating only the additional functionality required.

The result is software that reaches production more quickly while maintaining consistency, security and maintainability.

This represents a significant evolution in enterprise software development. Rather than replacing experienced engineers, AI becomes a productivity multiplier that allows technology teams to focus on higher-value work while relying on established frameworks for the underlying infrastructure. 

AI in Financial Services Must Balance Innovation with Regulation

Throughout the episode, regulation remains a recurring theme.

Unlike many other industries, financial services cannot afford to prioritise speed over control. Trading platforms, risk systems and market infrastructure operate within some of the world's most heavily regulated environments, meaning every technology decision carries significant operational and regulatory implications.

David shares an interesting perspective from conversations with regulators, explaining that governance responsibilities increasingly extend beyond technology teams. Under regulatory frameworks such as the UK's Senior Managers Regime, accountability ultimately rests with senior leadership responsible for ensuring firms operate safely and effectively.

This creates an important challenge for organisations embracing AI-assisted software development.

If product managers and business teams begin creating applications independently using AI tools, organisations must still maintain clear oversight regarding where code originates, how it is validated and who remains accountable once those applications reach production.

The discussion makes it clear that governance cannot become an afterthought.

Instead, it must be embedded throughout the entire development lifecycle.

For financial institutions, this is becoming one of the defining characteristics separating successful AI strategies from unsuccessful ones. Firms capable of combining innovation with governance will be significantly better positioned than those focusing solely on development speed. 

Prioritising the Right AI Opportunities

As AI capabilities continue to expand, another challenge emerges.

There are now almost limitless opportunities to apply artificial intelligence across financial services.

The real question is no longer whether organisations can deploy AI.

It is deciding where they should.

Tej explains that many firms now have access to sophisticated AI models, numerous proof-of-concept projects and growing internal enthusiasm. However, attempting to automate every process simultaneously risks creating fragmented solutions that deliver little strategic value.

Instead, organisations should identify the initiatives capable of delivering the greatest commercial impact.

That might involve reducing operational risk, simplifying legacy technology, improving developer productivity or accelerating product delivery. Whatever the objective, success depends on choosing projects that genuinely move the business forward rather than simply demonstrating technical capability.

This disciplined approach reflects the wider maturity of AI adoption across financial technology.

The conversation suggests the industry is entering a phase where strategic prioritisation will become just as important as technical innovation.

AI Will Transform Financial Technology Recruitment

As the discussion moves towards the future, Toby highlights how dramatically technology leadership has evolved over the past two decades.

Technology departments were once viewed primarily as operational support functions.

Today, technology strategy increasingly defines competitive advantage.

Artificial intelligence is accelerating that transition even further.

As AI becomes embedded throughout software engineering, product management and enterprise transformation, financial institutions require professionals capable of combining technical expertise with commercial understanding.

Demand continues to grow for CTOs, CIOs, Heads of Engineering, Product Managers, Enterprise Architects, Cloud Engineers, AI Specialists and Software Engineers who understand both financial markets and modern technology delivery.

For organisations hiring across capital markets, financial technology, trading technology, payments, wealth management and investment banking, attracting this talent will remain a critical competitive differentiator.

This changing landscape reinforces the importance of specialist financial technology recruitment partners capable of understanding both technical capability and the commercial objectives driving transformation programmes.

The Future Belongs to Firms That Can Operationalise AI

Towards the conclusion of the episode, the discussion shifts away from individual technologies and instead focuses on long-term industry change.

Tej reflects on the enormous scale of technology investment across financial services and the opportunity AI presents to make institutions more efficient, reduce operational costs and improve the movement of capital throughout the global economy.

Rather than viewing AI as a replacement for people, he describes it as a tool that enables experienced professionals to solve larger and more complex problems than ever before. Product specialists, software engineers and architects remain central to success because they provide the judgement, frameworks and business understanding that AI still lacks.

David reinforces this by explaining that organisations adopting AI most successfully are those integrating it into broader transformation strategies rather than treating it as an isolated initiative. Whether helping private equity-backed firms accelerate product delivery, enabling banks to consolidate technology platforms or supporting regulated institutions through modernisation programmes, AI delivers the greatest value when aligned with clear commercial objectives.

Perhaps the most memorable message from the episode is that AI itself is no longer the differentiator.

Access to large language models has become increasingly widespread.

Competitive advantage now comes from knowing how to deploy those capabilities securely, govern them effectively and deliver measurable business outcomes.

What This Means for the Financial Technology Industry

This episode of FinTech Focus TV provides a thoughtful and practical discussion about where artificial intelligence is genuinely creating value across capital markets. Rather than focusing on speculative predictions, Toby Babb, David Perkins and Tej Sidhu explore the operational realities of implementing AI within one of the world's most demanding technology environments. From governance and software engineering to product management, regulatory oversight and enterprise transformation, the conversation demonstrates that the future of AI depends less on experimentation and more on execution. 

For technology leaders, the message is clear: successful AI adoption requires strategy, governance and the right people. For professionals working across financial technology, capital markets, software engineering, digital transformation, product management and AI, the discussion offers valuable insight into the skills and leadership capabilities shaping the next generation of financial services.

As organisations continue investing in AI, demand for exceptional technology talent will only increase. At Harrington Starr, we work with firms across the global financial technology ecosystem to connect businesses with the software engineers, AI specialists, product leaders, cloud engineers, cyber security professionals and transformation experts driving this evolution. Conversations like this demonstrate that while technology continues to change at remarkable speed, success still depends on the people capable of turning innovation into real-world outcomes.