Banks and AI: The Challenge of Becoming AI-Native

AI: A Transformation Beyond Simple Adoption

Artificial intelligence (AI) will not merely transform businesses through its implementation. It is the overhaul of operational modes that will make it a lever for change. However, transformations rarely fail at the outset; rather, they falter in the middle of the process when organizations attempt to scale.

In a previous article, the concept of an AI-native bank was defined: a bank where every decision, process, and customer interaction is guided by AI. Since then, one question has persisted: how do we achieve this goal? Before exploring this question, it is crucial to recognize that the idea of AI-native organizations remains largely a promise. The potential of AI is enormous, but the long-term economics and risk profile of AI-driven companies are still emerging. Some initiatives will deliver extraordinary value, while others will fail to scale.

Despite this uncertainty, one thing is becoming increasingly clear. The opportunity is too great to ignore. Across all sectors, AI is beginning to redefine how businesses operate. Tech companies are integrating AI into decision-making. Digital platforms are automating complex processes. New entrants are building organizations designed around AI from day one.

A Plateau Plan to Guide Transformation

Transformations do not progress linearly. Organizations go through plateaus, each marking a stage of evolution: adoption, foundation, and value creation. Understanding these plateaus helps leaders identify their position and the necessary actions.

Plateau 1 – Exploration and Foundation

Organizations often begin their AI journey with experimentation, exploring use cases such as document processing and internal productivity copilots. The goal is learning.

• Minimum Scope: A targeted set of use cases, a top-down cost-reduction objective to start integrating value, AI governance aimed at compliance with the AI Act, key internal and external risks, and growing literacy. Testing the scalability of data and AI platforms and other foundations.
• Blacklist: Stop any use case without a business owner and contribution to the value case – ensure strategic alignment. Halt any shadow AI – regain control.
• Success: Pilots deliver measurable improvements, teams gain confidence in AI, employees begin using AI, management recognizes strategic potential, core capabilities are tested, and their areas for improvement are clear.

Typical Bottlenecks: Experimentation quickly exposes structural issues such as fragmented data leading to the use of unregulated AI data, unclear roles and responsibilities delaying decisions and misaligning priorities, and limited AI literacy hindering true adoption of AI use cases.

KPIs to Define and Track: Number of high-impact use cases in production, time to production, percentage of the total population related to the use case using the AI use case, number of AI-related risks identified, incidents and ethics, number of lessons learned implemented.

Leadership Question to Answer: Where do we see real added value from AI – and which experiments should become strategic priorities?

Plateau 2 – Strategic Verticalization

The second plateau begins when organizations stop asking, “Where can we still experiment with AI?” and start asking, “Where can AI fundamentally transform our business?”. Investment focuses on a few high-impact areas. In the banking sector, these often include:

• Customer service
• Financial crime and KYC
• Credit and investments
• Operations

Scope of this Plateau: 3 to 5 holistic value areas to deploy AI end-to-end across disciplines, a modernized data and AI platform focused on AI-ready data (e.g., investing in knowledge graphs and vector databases), explicit governance with safeguards focused on acceleration and control of what matters.

Blacklist: Stop batch decision-making (risk and overnight fraud) and manual case processing – move to real-time to leverage the benefits of AI and dare to stop the “old way of working.” Freeze all unrelated use cases – focus top-down on value areas – and concentrate your talents and experts where it matters. Stop an experimentation framework and require that AI systems be monitored and promote sharing of learnings.

What Success Looks Like: The entire journey related to value areas redesigned around AI, faster decision cycles, improved customer experience, significant cost reduction.

Typical Bottlenecks: Constraints of the central AI and data platform hindering rapid deployments, unclear AI governance, clear on paper but not utilized in practice, lack of alignment between business and technology priorities hindering speed.

Leadership Question to Answer: What are the few areas we should transform with AI – and are we sufficiently focusing our investments there?

Plateau 3 – Scaling AI Across the Enterprise

Once AI becomes critical in several areas, the organization must evolve its capability to deliver AI at scale. AI becomes a repeatable enterprise capability. It is also important to solidify the foundation, and with that, focus on transforming leadership and the workforce. Employees must learn to work with AI systems, and leaders must become extremely bold in driving change.

What Success Looks Like: AI solutions move quickly from development to production, AI systems are continuously monitored and improved, AI-ready data products are reused across teams, AI becomes an integral part of daily operations.

Blacklist: Operational model based on processes and systems – organize around AI, product and discipline-centered silos – organize around AI.

Typical Bottlenecks: Workforce resistance, unclear and unformalized risk appetite, uncontrolled “citizen AI” experimentation leading to more risks.

Leadership Question to Answer: Can our organization reliably and responsibly deliver and scale AI solutions?

Plateau 4 – AI-Native Operations

At the final plateau, AI becomes integrated into the way of operating. Customer journeys are orchestrated through intelligent workflows. Decisions are supported or automated in real-time. Employees increasingly work alongside AI systems rather than making routine decisions themselves.

What Success Looks Like: AI integrated into core operations, faster decision cycles, adaptive processes driven by data and AI, structural value creation.

The business becomes driven by AI-based decisions rather than systems.

Leadership Question to Answer: How should our operating model evolve if AI becomes the central decision-making engine of the bank?

What Previous Transformations Teach Us

The idea of an AI-native organization may seem unprecedented. But many companies have already navigated through major transformations. From them, one fundamental lesson has been learned: technology is not the true change. Just like the shift to AI-native, it is not merely a technological deployment. It is an organizational transformation – and history shows that such transformations only succeed when leadership considers them a fundamental change.

Research on organizational change, notably the work of John P. Kotter, consistently shows that successful transformations follow several principles: creating a sense of urgency, aligning leadership, removing structural barriers, generating early wins, and embedding new ways of working into the organization. These lessons are evident in major corporate transformations.

IBM reinvented itself in the 1990s under Lou Gerstner; the company did not simply adopt new technologies. It reorganized around services, broke internal silos, and forced leadership alignment around a new operational model.

More recently, Microsoft's cloud transformation under Satya Nadella required redefining strategy, changing culture, and aligning the entire organization around a new platform model.

And Netflix's evolution into a data-driven company necessitated integrating analytics and algorithms into fundamental decision-making across the enterprise.

The lesson from these transformations is clear: technology does not transform organizations. Leadership decisions do. Transformation through AI will require the same discipline – but the challenge may be even greater.

The Transformation Playbook

Unlike previous transformations, AI has the potential to redefine decision-making horizontally and vertically across the organization: risk assessment, customer interaction, operational workflows, and strategic insights. This means that AI cannot remain a set of local initiatives within individual departments. It must become a high-level strategic priority for the entire institution, as it crosses departments and disciplines. Therefore, I can consolidate a short but powerful transformation playbook based on best practices.

1. Create a true strategic urgency by being bold and pausing other initiatives.

Executive leadership must explicitly prioritize AI transformation. If AI is competing with dozens of other initiatives, it will remain incremental and never make a breakthrough, as it will disrupt fundamental processes. If other change initiatives are not deprioritized, you cannot change your entire credit risk management process. Therefore, true transformation requires...