AI in Banking: Lessons from Private Equity for Value Creation

As financial institutions globally grapple with the substantial investments poured into artificial intelligence (AI), a pivotal question emerges: where precisely does AI generate tangible value? The answer, as explored by leading experts, often lies not within the traditional tech sector, but surprisingly, in the pragmatic, efficiency-driven realm of private equity. These investment firms, which inherently treat capital deployment with a keen eye on returns, offer a compelling blueprint for how banks can transform their AI initiatives from experimental endeavors into indispensable engines of growth and operational excellence.

Private Equity's Pragmatic AI Investment Philosophy

Thomas H. Davenport, in a new analysis published by MIT Sloan Management Review alongside co-author Randy Bean, highlights that a clear indicator of AI's value-creation potential is the readiness of private equity firms to embed AI capabilities within their portfolio companies. This approach underscores a fundamental shift in perception: AI is not merely a technological upgrade but a strategic capital expenditure that must demonstrably produce a return on investment (ROI).

A compelling illustration of this philosophy is Apollo Global Management. Davenport notes that Apollo significantly amplified its focus on digital transformation, analytics, and AI by bolstering its portfolio operations team. This strategic move enabled the firm to meticulously identify "value pools"—specific areas and capabilities where digital technologies could be leveraged to enhance performance and boost overall company value. For private equity, AI is less about cutting-edge innovation for its own sake and more about a calculated investment designed to optimize and extract maximum value from assets, a mindset crucial for banks to adopt.

AI's Tangible Impact in Traditional Finance

This value-driven model is now profoundly influencing how established banks and payment networks approach AI adoption. The impact is quantifiable and impressive:

• JPMorgan Chase: The banking behemoth reported that its internal AI platforms generated approximately $1.5 billion in efficiency and risk-management gains within a year. Beyond internal optimization, AI also played a role in attracting new clients, even amid volatile market conditions, showcasing its dual power for both cost-saving and revenue generation.
• Mastercard: This global payment network attributes a significant 23% jump in its value-added services revenue—its fastest-growing segment—to its AI-driven fraud and analytics platforms. Furthermore, Mastercard's strategic acquisition of Recorded Future, valued at $2.65 billion, was aimed at expanding its predictive threat and data-intelligence capabilities across its vast network, further embedding AI into its core business strategy.

In these instances, AI is no longer confined to isolated pilot projects or experimental labs. Instead, it is intrinsically woven into the fabric of how these institutions conduct their core business, directly influencing profitability and strategic advantage. Davenport's assertion that AI delivers optimal value "when it operates in the flow of business, not outside it" perfectly encapsulates how these financial giants are integrating intelligence across their credit, risk, and payments infrastructure, transforming operations from the inside out.

The Evolution of Analytics: Beyond "What Happened"

Beyond simply measuring impact, decision-makers require continuous, real-time visibility into performance. This is where the concept of Vibe Analytics, as explained by MIT researcher Michael Schrage, offers a revolutionary perspective. While traditional analytics provide retrospective answers to "what happened" or "why did it happen," Vibe Analytics shifts the paradigm by asking, "What insights emerge if we explore together?" It fosters a dynamic, live dialogue between managers and data, transcending static reports with interactive questions and immediate responses.

Within financial services, this interactive approach is rapidly gaining traction. Treasury and trade-finance teams are actively piloting conversational copilots, enabling executives to pose direct, intuitive questions such as, "Which clients are driving settlement risk today?" or "What caused this morning's authorization spike?" These advanced tools convert complex analytics into actionable conversations, drastically compressing review cycles from days to mere minutes. Such capabilities are proving instrumental in informing intraday liquidity management, enhancing fraud mitigation strategies, and optimizing pricing adjustments, effectively transforming data into a potent, real-time management instrument.

Proving Value: The Michelin Case Study

Davenport and Bean cite Michelin as a prime example of this outcome-driven approach yielding significant success. The company's AI systems consistently deliver over €50 million in annual ROI, a figure that continues to grow by nearly 40% year over year. This remarkable performance is directly attributable to the fact that Michelin's AI initiatives are rigorously tied to clear, measurable business outcomes, demonstrating the profound financial benefits of a strategic, integrated AI deployment.

Shifting Paradigms: From Experimentation to ROI

The private equity sector has unequivocally demonstrated that AI generates substantial returns only when its economic rationale is explicit and its impact measurable. This pragmatic outlook is increasingly resonating across the corporate landscape. According to PYMNTS, only 26.7% of CFOs now plan to increase generative AI budgets in 2026, a significant drop from 53.3% just a year prior. This notable shift reflects a broader industry-wide pivot away from generalized AI experimentation towards a stringent focus on demonstrable outcomes and quantifiable returns. For banks, this signifies a crucial lesson: AI must be integrated with a clear strategy for value creation, treating it as a strategic asset that demands and delivers explicit economic benefits, rather than a mere technological novelty.