How FinTech Firms Should Approach AI and Data
In a fast-evolving digital environment, AI and data are often treated as magic bullets for innovation. But during this episode of FinTech Focus TV, recorded live at the AI in Capital Markets Summit in New York, Toby is joined by Julia Bardmesser, CEO of Data4Real LLC, who offers a sharp and experience-led assessment of how artificial intelligence and data strategy are really being used in today’s financial services ecosystem. This isn’t a conversation about theory or speculation; it’s a practical guide to what is, and isn’t, working when it comes to AI adoption in FinTech.
Julia has built a career around helping companies translate data into value, working both as a strategic advisor and an academic, currently teaching business leaders at NYU. Her insights throughout the episode consistently bring the focus back to impact: what’s the point of your AI and data efforts, and how are they supporting your broader business goals?
For a FinTech recruitment business like Harrington Starr, conversations like this offer critical context for how hiring trends align with strategic transformation in the sector.
From Theory to Practice: The Realities of AI Projects in FinTech
Much of the discussion revolves around the growing gap between the widespread hype around AI and the reality of what FinTech firms are achieving. Julia shares that many companies are still stuck in the early stages of implementation, often pursuing AI projects simply to say they are using AI, rather than to drive genuine business outcomes.
This surface-level adoption prevents firms from moving beyond the proof-of-concept stage and into real production environments, where AI can deliver meaningful benefits. The challenge lies in moving projects forward with clarity and scale, something Julia has seen lacking in many implementations across the financial services industry.
Her commentary reflects a major pain point facing hiring managers in FinTech today: the difficulty of sourcing talent who can not only implement AI technology but also connect it to core business drivers. The demand for professionals with both technical expertise and strategic thinking continues to grow across Harrington Starr’s client base in New York, London, and beyond.
Defining Success: Aligning AI with Business Value
Julia outlines a key foundation that separates successful data and AI initiatives from those that falter: the ability to identify and measure business value. Without a clearly defined goal, AI projects risk becoming expensive, aimless endeavours. Rather than focusing exclusively on traditional ROI metrics, which are often difficult to calculate for data-centric projects, she advocates for identifying softer, strategic outcomes.
These might include improvements in customer retention, enhancements to customer experience, or the ability to expand market reach. Julia explains that these are the types of outcomes where data and AI have proven to be most effective. However, they require a clear strategy from the outset, one that puts business needs before technology.
This point is especially relevant for FinTech businesses that are scaling or undergoing transformation. As digital maturity increases, hiring decisions increasingly hinge on the ability of individuals to interpret data, ask the right questions, and align tools with broader organisational goals.
Scalability and the Enterprise Challenge
The conversation also touches on the issue of scalability, another major barrier preventing many AI projects in financial services from reaching full deployment. Julia points out that even when companies successfully build a solution that works in one part of the business, they often fail to replicate or scale it across other areas.
This is typically due to limitations in data infrastructure. To build solutions that work at an enterprise level, companies must have access to data that is clean, consistent, and connected across systems. In many cases, this level of data readiness is missing, creating friction that can stall progress.
Julia highlights the importance of identifying use cases that can solve more than one business problem, an approach that offers greater return on the initial investment. This strategic mindset is crucial for firms that want to make AI a core part of their operational framework, rather than a standalone experiment.
In the FinTech recruitment space, this reflects a growing need for candidates who can architect scalable data solutions and have experience working across different departments. Roles such as Enterprise Data Architects and Strategic AI Leads are in growing demand, especially in firms looking to achieve long-term transformation.
Teaching Future Leaders: Turning Expertise into Education
One of the key projects Julia is working on is a book based on the course she teaches at NYU. Although the book doesn’t yet have a final title, it will serve as a textbook for a curriculum she developed to help business leaders understand how data and AI can bring real value to an organisation.
Rather than focusing on technical specifics, the course and book are designed to educate leaders on how to apply data thinking to business strategy. It’s about giving them the tools to assess where data can have the most impact, and how to build the capabilities needed to realise that value.
This practical, business-first approach stands in contrast to many traditional data science programmes, which often focus on theory and tools. Julia’s work is about applying data concepts in the real world, an approach that resonates with many FinTech professionals navigating the complexities of digital transformation.
For recruitment specialists at Harrington Starr, this educational perspective offers useful guidance when identifying candidates who can lead data initiatives, not just execute them. Increasingly, clients want leaders who understand both the technical and organisational sides of AI adoption.
The Importance of Data Capabilities
Julia discusses the concept of “data capabilities” in detail, a term that encompasses the operational and governance structures that must be in place for data to be useful. Having data, she explains, is not enough. It must be usable, accurate, and connected. Firms need to be able to identify what data they have, how to access it, and what they are permitted to do with it.
Without these capabilities, organisations will struggle to extract any meaningful value from their data assets. This is particularly true in FinTech, where companies are often dealing with complex, high-volume datasets that require careful management.
For hiring managers, this insight underscores the importance of bringing in professionals who understand data governance, data quality, and infrastructure development. At Harrington Starr, we frequently place specialists who can build the frameworks required to make data projects successful, particularly in firms transitioning from legacy systems to more modern, cloud-based solutions.
Data Strategy Must Start with Business Strategy
One of Julia’s most important messages is that a data strategy must be rooted in business strategy. It’s not enough to build a data warehouse or establish governance policies and call it a strategy. Instead, firms need to begin by clearly defining what they want to achieve from a business perspective, whether that’s growth, customer retention, or improved margins.
Once those goals are clear, data and AI initiatives should be aligned to support them. This ensures that projects are outcome-focused and more likely to deliver long-term value.
In our work as a FinTech staffing agency, we often advise clients on aligning their hiring needs with their strategic priorities. If a company’s goal is customer growth, it needs data analysts who can uncover patterns in user behaviour. If efficiency is the aim, they may need experts in process automation. Julia’s approach reinforces this connection between vision and capability.
Cultural Readiness Over Technical Investment
Throughout the conversation, Julia returns to the idea that technology is not the main barrier to successful AI implementation, culture is. Many firms underestimate how much change is required at an organisational level to embrace data-driven thinking. From executive teams to operational staff, the shift to data-centric decision-making involves new behaviours, new expectations, and a new mindset.
Cultural readiness often means breaking down silos, promoting transparency, and encouraging experimentation. It also involves creating an environment where people are open to changing how they work, rather than simply adding new tools to old processes.
This cultural challenge also influences hiring. As FinTech recruiters, we often work with clients to assess cultural fit as part of their hiring process. Finding someone who can drive change internally is just as important as technical ability, especially when the goal is transformation at scale.
GenAI and the Drive for Productivity
Julia also explores the use of generative AI (GenAI) and how it is currently delivering the most immediate benefits in terms of productivity. Unlike other AI applications, GenAI does not require extensive data maturity to be effective. It can be used for tasks such as writing content, summarising meetings, or even documenting old code, functions that don’t rely heavily on data infrastructure but still create value.
These use cases help teams save time and focus on more strategic work, but Julia makes it clear that they don’t always translate into cost reductions. Especially in firms that haven’t built data-centric operating models from the ground up, integrating GenAI into workflows can become expensive and inefficient if not properly planned.
In terms of hiring, this highlights the need for roles that focus on productivity enablement. Whether through process improvement, GenAI implementation, or training, companies are seeking professionals who can help teams work smarter, not harder.
Beyond Cost Savings: Rethinking the AI Narrative
A recurring theme in the episode is the need to shift the narrative around AI. Julia rejects the idea that AI is primarily about reducing headcount or cutting costs. Instead, she positions AI as a way to enhance human work, making employees more effective and allowing them to spend more time on meaningful, high-value activities.
Rather than viewing AI as a threat to jobs, she sees it as a tool for empowerment. This is especially relevant in FinTech, where the pressure to do more with less is constant. Julia suggests that productivity gains, not efficiency metrics, should be the focus.
For hiring managers, this shift in perspective creates a new set of criteria when assessing candidates. The most valuable hires are not just those who can automate tasks, but those who can think critically, use tools effectively, and collaborate across teams to improve performance.
The Mission of Data4Real LLC
Julia closes the episode by sharing more about her consultancy, Data4Real LLC. Her mission is to help organisations, ranging from finance to biotech to nonprofits, identify how data and AI can support their specific business goals. She works with companies to develop strategies that are both business-led and implementation-ready.
Rather than waiting years for results, she focuses on helping clients prioritise their actions to deliver impact quickly. This involves understanding what to do, why to do it, and in what order, an approach that mirrors how many FinTechs are now structuring their digital transformation roadmaps.
This kind of work is increasingly in demand across the financial services space, and it directly informs how Harrington Starr supports its clients. By understanding the phases of digital maturity, we’re better positioned to help firms hire the right talent at the right time.
This episode of FinTech Focus TV with Julia Bardmesser offers a deeply practical, grounded view of AI and data in financial services. For FinTech businesses navigating transformation, her insights are a valuable reminder that success depends not just on technology, but on culture, clarity, and capability.
At Harrington Starr, we help FinTech companies build high-performing teams that deliver real business outcomes. Whether you're hiring AI leaders, data specialists, or transformation experts, our recruitment expertise ensures you’re investing in people who align with your strategic goals.
