Executives from financial institutions and related firms innovating in artificial intelligence recently convened at Bain & Company’s Global AI in Financial Services Summit in London. Participants reflected on how the industry is tackling AI-infused transformation at scale. The following synthesis captures the collective learnings, challenges, and priorities shared by participants.

As AI spreads throughout the financial services industry, it is starting to reshape every aspect of how institutions create and deliver value. Most executives understand that financial institutions a decade from now will look markedly different than they do today. AI, especially agentic AI, is not simply a tool for efficiency; it also acts to accelerate technological change, compelling firms to rethink how they operate.

At the same time, companies feel constraints—caught between investor pressure for rapid efficiency gains, the reality of tight budgets, and the need to invest in foundational capabilities. Yet even from this tight spot, companies can chart a path that leads to real transformation through focused investment, partnerships, and deliberate sequencing. Leaders at the Summit agreed that the essential ingredients of AI transformation are no longer mysterious; the challenge lies in execution.

We’re all technology companies now

Tomorrow’s financial institutions will operate within ecosystems deeply connected by technology. Ultimately, they will become technology companies whose core expertise is finance. The boundaries between banks, fintechs, and technology firms are dissolving, replaced by networks of collaboration and shared infrastructure. Incumbent firms will increasingly act as integrators and orchestrators of value, enabling connections rather than owning every element of the customer experience.

Customer interactions look set for profound changes as well. Conversational and agentic AI experiences—where advice, transactions, and support unfold naturally through dialogue and embedded automation—will become the norm. AI will make these interactions richer and more personal, blending text, voice, and visuals to respond to an individual’s context. This shift will only accelerate as younger, digitally native customers gain wealth and expect seamless, humanlike engagement.

Automation will also transform the operational backbone of finance. Routine activities will be largely automated, improving accuracy and speed while freeing human advisers to focus where empathy, expertise, and trust matter most. The human role in finance thus will shift from fewer routine tasks to more relationship-driven engagement. As a result, customers should expect to receive faster, easier service even for complex issues.

Yet these same technologies create new vulnerabilities. Generative AI and deepfakes empower customers and criminals. Financial institutions must invest in intelligent security to protect their data, systems, and customers. AI’s role here applies to threat detection, behavioral analytics, and identity verification. Maintaining trust will require not just secure infrastructure but also digital stewardship and education.

Key components to harness the AI revolution

So, what’s next? To stay relevant and competitive, financial institutions must begin now to reinvent entire business domains and value chain elements, not just optimize individual use cases or tasks. The firms that start early—thoughtfully, deliberately, and at scale—will define success in the age of AI. Participants identified a handful of areas to focus on when planning for success.

Strategic narrative and roadmap. A bold ambition, based on a clear strategic narrative, will mobilize the organization and chart a course to value. Investments should be sequenced to deliver progressive benefits while building lasting capabilities and learning how to embed AI successfully into the business. Reinvention should follow business priorities, be owned by domains and supported by shared capabilities, and progress along a self-funding journey that balances short-term gains with long-term capabilities.

Customer experience. Customer engagement will gravitate to conversational, embedded finance. Therefore, financial services institutions need deeper insight into customer context and behavior to design experiences that are adaptive and seamless. Delivering these experiences will increasingly rely on a mix of humans, digital systems, and AI agents, learning from and collaborating with one another. As automation handles routine tasks, employees will shift toward relationship-driven and higher-value activities.

Domain redesign. Agentic AI enables a 10- to 100-fold acceleration of processes and the reimagining of entire domains. The most fruitful focus will recompose work as new, end-to-end experiences delivered by integrated teams of humans and AI agents. Summit participants expressed a fundamental tension: While most of the organization will be affected by AI, redesigning a few domains will create most of the value. These priorities must align with the organization’s strategic narrative and risk appetite.

Talent and organization. Transformation requires a shift toward an AI-native organization. This entails building an operating model in which AI-augmented humans work alongside human-augmented AI agents. In turn, that model will depend on designing and testing AI with a deep understanding of human behavior. Business and technology functions must partner closely to accelerate change, share learning, and continuously improve AI-enabled processes. Several Summit participants favored a “hub-and-spoke” model for embedding scarce AI talent into domains, supported by central governance and capability-building hubs.

Data as a strategic asset. Data has already become a raw material for competitive advantage. But how to create genuine business ownership of data and mine its value as a strategic asset? As foundational AI models become commoditized, a competitive edge will flow from proprietary data products that capture enterprise-specific knowledge. Organizations must be able to assemble valuable data—internal and external, structured and unstructured—on the fly, as virtual data products to power AI applications and insights. As one executive emphasized, data should be treated as a product and not a byproduct.

Technology re-architecture. If agentic AI is to scale naturally, the surrounding technology should be modernized, which requires deliberate investment. Financial institutions must adopt modular, cloud-based architectures that host AI agents and automate control processes while managing coexistence between legacy and new systems. AI also is revolutionizing software development, demanding an end-to-end redesign of the delivery life cycle.

A company’s level of competitiveness will depend on making smart build-vs.-buy decisions and connecting seamlessly across ecosystems. Financial institutions will increasingly deploy AI as agents embedded within customer and staff workflows, and partnerships with fintechs, hyperscalers, and specialists will be essential for sustained innovation.

Governance, risk, and compliance. Distributed intelligence must be governed to balance risk and reward. Participants discussed lessons from experience, such as integrating AI agents like new team members and extending controls built for thousands of staff to thousands of AI agents. Organizations must set new risk-return boundaries for data, performance, and compliance, along with designing lines of defense that operate within AI systems as well as at the enterprise level. Controls must be automated to work at the speed and scale of AI, with continued emphasis on transparency, accountability, and trust. Participants identified four particularly difficult challenges: ensuring accuracy of AI systems and processes, managing vendor landscape risks, protecting data security and privacy, and retaining human accountability for outcomes.

Mergers and acquisitions for scale and capability. Strategic acquisitions will remain a lever for securing technology, talent, and scale to compete effectively in an AI-driven market. They can also accelerate transformation capabilities, enabling new partnerships that keep pace with technological disruption.

Preparation for future disruption. Participants acknowledged the need to plan for the coming waves of disruption. Institutions must prepare for the potential impact of general intelligence breakthroughs; stay alert to the disruptive effects of quantum technology on cybersecurity, AI, and broader tech systems; and be ready to close the digital–human divide as humanoid robotics emerges. This will require enough forward thinking, innovation, and adaptability to anticipate and prepare for these shifts rather than react to them.

Determining the right sequence

Most financial institutions understand why transformation is essential and what it should entail. The real challenge lies in how to execute at an enterprise scale. In particular, the right sequencing matters, balancing foundational investment with tangible value, maintaining control while accelerating learning, and aligning leadership vision with delivery reality.

Leading firms will move decisively from incremental improvement to fundamental redesign. They will treat AI transformation as a business endeavor, not a technology project. Some of the sequenced moves will be horizontal, such as mobilizing the organization through general-purpose AI tools or automating governance, risk, and compliance. Others will be vertical, redesigning domains such as customer service end to end, involving a blend of processes, AI agents, domain knowledge, and core technology.

The firms that succeed won’t be the ones chasing every breakthrough. Instead, they will choreograph change, domain by domain, with precision, discipline, and a sharp eye on where AI creates real advantage.