Why banks are still struggling to scale AI

Banks face increased pressure to deliver modern, AI-enabled services

Banks are in a pivotal moment. Customers now expect faster, smarter, and more relevant experiences, especially in corporate and investment banking. More than half of them want real-time services; nearly half seek personalization; and 40% are hungry for innovative products. Yet fewer than 20% feel those expectations are being met. That’s a clear disconnect between what customers want and what most traditional banks are delivering.

This gap is even more significant given that growth is losing momentum. Capgemini’s latest data projects annual revenue expansion across corporate and investment banking will slow, from 6.5% in 2022–2024 to 5.4% over the next five years. For institutions accustomed to steady progress, this deceleration demands a new approach. Technology alone won’t fix this. It takes a mindset ready to use technology for speed, agility, and customization at scale.

Executives should see this as a call to action, not a warning. Meeting rising expectations isn’t about incremental upgrades. It’s about reengineering how banks interact with clients. That requires shifting focus from reactive service models to proactive intelligence, using data and AI to anticipate customer needs instead of merely responding to them. Leaders who understand this will set the next standard for financial performance in a competitive and fast-moving market.

Legacy technology and stringent compliance requirements are straining

Banks sit on complex technology stacks built over decades. These legacy systems consume most of the IT budget simply to stay operational. Meanwhile, compliance costs and cybersecurity expectations continue to grow, further draining resources from innovation. In contrast, fintechs and newer non-bank players, built on lightweight and modern infrastructures, are scaling quickly and setting new benchmarks in customer experience.

Kartik Ramakrishnan, CEO of Capgemini’s Financial Services Strategic Business Unit, summed it up clearly: aging technology, tougher cybersecurity mandates, and expanding data and compliance demands are squeezing margins and blocking new investments. When technology is working against you rather than for you, transformation cannot happen at pace. The cost of maintaining old systems becomes the cost of lost opportunity.

For decision-makers, the tradeoff between stability and innovation is no longer optional, it’s strategic. Continuing to pour funds into outdated systems only delays progress and hurts competitiveness. Modernization efforts, though costly at first, free up long-term capacity for growth and flexibility. Executives must plan with a sense of urgency and precision: invest in modular, scalable digital infrastructures now to prevent being left behind later. The institutions that take that leap today will define the future standard of operational resilience and technological leadership in banking.

Modernizing technology foundations and redesigning operating models are essential

To truly scale AI, banks must go beyond surface-level upgrades. They need to rebuild the backbone of their operations, technology architecture, data infrastructure, and decision-making models. Embedding large language models and other AI systems will allow banking platforms to interpret data in real time, enhance customer interactions, and improve risk-based decisions. These systems thrive on clean, structured data and adaptable operations, both areas where older banking systems fall short.

The transformation isn’t just technical, it’s organizational. Leaders must create flexible operating models that support fast iteration and strategic experimentation. This means connecting AI initiatives directly to business results, not running them as isolated projects. Success depends on building a clear structure for how data flows across departments and ensuring technology teams work closely with business units to design systems that directly impact performance and customer engagement.

Executives should approach this as a long-term modernization effort. Scaling AI sustainably requires a solid foundation of data governance, automation-ready infrastructure, and cross-functional collaboration. Those who establish these fundamentals early will be equipped to unlock faster execution, sharper insights, and greater adaptability in a landscape where speed and intelligence define success.

Developing an internal, AI-capable workforce is critical

Banks are investing heavily in AI talent, but most are hiring externally instead of building capabilities from within. According to Capgemini’s findings, 40% of banks prioritize hiring external AI specialists, while only 23% focus on internal reskilling. This imbalance suggests that many organizations are missing a long-term opportunity, creating an AI-fluent workforce that understands both advanced technology and the institution’s operational needs.

Reskilling existing teams builds institutional strength. Employees who combine knowledge of legacy systems and frontline operations with new technical expertise can accelerate digital transformation from within. By empowering these individuals, leaders can create smoother integration between human and machine-driven processes while maintaining alignment with the bank’s business strategy and regulatory commitments.

Executives should see internal reskilling as a decisive investment in future readiness. External talent brings fresh perspective, but internal development ensures sustainability. A balanced strategy that leverages both, external innovation and internal growth, builds a culture of continuous learning and change adoption. In this environment, teams evolve with technology instead of trailing behind it. For leadership, that’s the path to a workforce capable of delivering on the promise of AI, not just deploying it.

Robust AI governance and strategic ecosystem partnerships are crucial

As banks accelerate AI adoption, governance becomes non‑negotiable. Nearly two‑thirds of banking executives cite high regulatory compliance costs as a significant challenge, according to Capgemini’s report. In this environment, implementing AI responsibly means embedding governance frameworks early, covering transparency, risk management, data privacy, and accountability. Without this structure, innovation can’t scale safely or sustainably.

Strong AI governance is not about slowing progress; it’s about creating a controlled foundation for intelligent automation and decision-making. Banks that define clear oversight on data use, algorithmic fairness, and regulatory alignment will move faster with less uncertainty. Governance ensures AI strategies can withstand scrutiny from both regulators and clients, reinforcing long‑term trust in digital operations.

Beyond internal controls, success will depend on building a network of trusted partners. Collaboration with regulated cloud providers, FinTechs, and data specialists expands innovation capacity while maintaining compliance discipline. Chedru‑Refeuil, quoted in the Capgemini report, stated that “to succeed, CIBs must adopt a disciplined approach: creating enterprise‑grade platforms and cultivating an ecosystem of trusted partners.” This vision calls for banks to integrate external expertise without compromising security or control.

For executives, the goal is clear, combine disciplined governance with strategic partnerships to scale responsibly. It’s an opportunity to set industry standards rather than follow them. Those who take this direction will build adaptive, compliant AI systems that position their institutions for leadership in a new era of data‑driven finance.

Key takeaways for leaders

• Customer expectations demand faster AI-driven innovation: Executives should prioritize real-time, personalized, and innovative offerings powered by AI to close widening expectation gaps and counter slowing revenue growth projected to fall from 6.5% to 5.4% in coming years.
• Legacy systems are consuming budgets and stifling growth: Leaders must redirect investments from maintaining outdated infrastructure to modern digital platforms, freeing capital for innovation and increasing competitive agility against faster-moving fintechs.
• Modernization is essential for scalable AI adoption: Decision-makers should rebuild technology foundations and streamline operating models to enable real-time data processing and AI-enabled decision-making that directly enhances customer and business outcomes.
• AI success depends on an internally reskilled workforce: Leaders should balance external hiring with internal reskilling, building teams that understand both technology and institutional operations to sustain long-term innovation and cultural alignment.
• Strong governance and trusted partnerships drive responsible AI growth: Executives must embed robust governance frameworks early and form strategic partner ecosystems to scale AI securely, manage regulatory demands effectively, and sustain trust in digital transformation.