Article

AI at the tipping point: from pilots to platform thinking

Heather Barnes 3 Jun 2025

AI has moved beyond the hype cycle. For financial institutions, it is now a business-critical capability. The pace of change, competitive pressure, and customer expectation are converging to make AI an urgent leadership priority.

Today, leading banks, insurers, and asset managers are using AI not just to streamline operations, but to unlock new revenue, enhance risk intelligence, and build differentiated customer experiences. For executive leadership, the message is clear: AI is no longer a support function — it must sit at the heart of strategy. Institutions that truly embed AI, aligning it with commercial goals, regulatory demands, and ethical frameworks, will be positioned as market leaders.

The winners in the next era of financial services won’t be those who adopt AI, but those who integrate it across the enterprise to drive value, scale, and resilience.

AI at the core of strategy

The financial sector has always been rich in data. With today’s scale and diversity of information, spanning structured and unstructured sources, AI has evolved from a tool for automation to a core engine for growth, product innovation, and decision-making. Senior leadership at firms like JPMorgan Chase and Goldman Sachs are investing heavily in AI platforms that are yielding measurable results:

JPMorgan’s COiN platform uses machine learning to review legal documents – cutting down a 360,000-hour task to seconds.

Goldman Sachs employs NLP to analyse large volumes of market news in real time, supporting faster and more informed trading decisions.

Data and AI leaders must exercise strategic discipline when selecting products and platforms, focusing on commercial impact, ease of adoption, and well-defined use cases. Rather than chasing every shiny new tool, they should anchor decisions in value-driven outcomes and organizational readiness. The goal is not to adopt technology for its novelty, but to solve real problems effectively and sustainably.

Data infrastructure as a strategic asset

One of the most critical enablers of AI success is modern data infrastructure. Financial institutions are replacing fragmented legacy systems with cloud-native platforms like Snowflake and Databricks, enabling real-time data access and collaboration across business units. This shift is not simply about technology; it’s about speed to insight and speed to market. HSBC, for example, has migrated key analytics workloads to Google Cloud, accelerating the global deployment of AI-powered credit models resulting in lower infrastructure costs, improved agility, and a stronger foundation for continuous innovation.

At senior levels in the data infrastructure space, organizations need talent with a blend of deep technical expertise and strategic vision. These leaders must architect scalable, secure, and resilient data ecosystems while aligning infrastructure decisions with broader business goals. Strong leadership, cross-functional collaboration, and the ability to navigate evolving technologies and regulatory landscapes are essential to drive long-term value and innovation.

Rethinking risk and inclusion

AI is redefining how institutions assess and price credit risk. Advanced models now incorporate a wider set of inputs including behavioural signals and alternative data, leading to more accurate and inclusive risk assessments. This is particularly valuable in high-growth, underbanked markets where traditional models fall short. Fintech leaders like Nubank and ZestMoney demonstrate how AI can expand market reach responsibly, by underwriting previously excluded customers while maintaining robust risk controls.

Elevating the customer experience

Customer expectations are changing rapidly and AI is helping financial firms respond at scale: virtual assistants (like Bank of America’s Erica) have handled over 1.5bn interactions, from bill payments to credit tracking. These tools reduce operational costs while improving service quality and availability. In parallel, AI-driven recommendation engines are improving cross-sell and upsell performance by delivering hyper-personalized product offers.

Risk, compliance, and operational efficiency

AI is also transforming how firms manage regulatory obligations and operational risk. In compliance, firms are automating the monitoring of communications to detect insider trading, market manipulation, and policy violations. Goldman Sachs and Morgan Stanley are using NLP to scan millions of emails and voice transcripts, cutting review time and improving accuracy. AI is also being integrated into model governance and risk frameworks. MLOps tools now support automated documentation, version control, and drift monitoring, which are essential as regulatory scrutiny intensifies around explainability and fairness.

As AI systems become more integral to core business functions, senior leaders must ensure compliance with evolving legal standards, ethical guidelines, and data governance frameworks. They play a critical role in embedding responsible AI practices, mitigating bias, and establishing robust oversight mechanisms to prevent unintended consequences and protect organizational integrity.

The talent challenge

Hiring for roles like Chief AI Officer or Chief Data Officer presents a formidable challenge in today’s rapidly evolving landscape. Modern AI has only surged to prominence in recent years, and the demands on data leaders continue to escalate amid shifting regulations and constantly emerging platforms and capabilities.

Success starts with clarity. Organizations must define what they truly want to achieve with these roles—balancing technical expertise with strategic vision, and understanding the inevitable trade-offs between boardroom presence and hands-on execution.

Equally critical is structuring these roles for success. This means giving data and AI leaders real authority—the levers to deliver on their mandates—and ensuring their roles carry genuine influence. When these leaders have both vision and operational power, the outcomes are far more likely to meet the ambitions set for them.

Another key challenge in the AI talent landscape—across all levels—is the growing need for individuals who specialize in bias management and AI ethics. As artificial intelligence becomes more deeply integrated into business operations and decision-making processes, organizations must ensure that these technologies are used responsibly and do not inadvertently cause harm. This requires professionals who not only understand the technical aspects of AI but also have the ability to identify and mitigate potential ethical risks, such as algorithmic bias, privacy concerns, and unintended social consequences. The demand for such expertise is increasing, yet the talent pool remains relatively shallow, making it difficult for firms to build teams that can implement AI solutions in a controlled, transparent, and accountable manner.

Heather Barnes

Heather is a Partner in our global Technology and Digital practice, leading on senior technology, digital and data officer roles across the consumer, technology, life sciences, energy, and industrials space. A strong advocate of diversity, equality and inclusion, Heather has…

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