Hardly any boardroom discussion in banking today takes place without mentioning artificial intelligence. Expectations are high: more efficient processes, better customer experiences, smarter decisions. But how far along are banks really? And where does the gap between ambition and execution lie? A look at the recent Cofinpro study “AI in Banking” reveals: the potential is widely acknowledged – execution remains the real challenge.

AI in Banking: Relevance Is Clear, Impact Often Isn’t

Artificial intelligence in banking is no longer a future topic. It has become a strategic priority. According to the study, the majority of banks are actively engaged with AI – through pilots, departmental initiatives, or overarching programs.

At the same time, a familiar pattern emerges: many initiatives remain isolated. AI is tested, but not scaled. Use cases exist, yet fail to deliver tangible business impact.

The key question is no longer “Why AI?”, but “How does AI actually create value?”

Common AI Use Cases in Banking – and Their Limitations

Automation and Efficiency Gains

AI adoption is strongest in well-defined, data-driven processes:

• Document classification and processing
• Automated credit pre-assessments
• Fraud detection and anomaly analysis

These use cases generate measurable efficiency gains – but often remain limited to individual process steps.

Customer Interaction and Marketing

AI is also increasingly used in marketing and sales:

• Chatbots and virtual assistants
• Next-best-offer models
• Personalized customer communication

However, the study shows that many banks stop short of realizing the full potential. Personalization often ends at basic segmentation instead of delivering truly data-driven, individual experiences.

Why Many AI Initiatives in Banking Fail to Scale

1. Lack of an End-to-End Perspective

AI is frequently treated as a technology project rather than an integral part of the value chain. Without process, system, and decision integration, its impact remains limited.

2. Data Quality Beats Model Sophistication

One of the study’s key insights: the biggest bottleneck isn’t the algorithm – it’s the data. Fragmented data landscapes, unclear ownership, and regulatory uncertainty slow progress significantly.

3. Organization and Governance as Bottlenecks

Many banks employ skilled data scientists but lack clear AI governance:

• Who prioritizes AI use cases?
• Who owns business impact?
• How are regulatory requirements embedded systematically?

Without clear answers, AI remains experimental instead of becoming a true management instrument.

AI Strategy in Banking: From Experimentation to Impact

AI Needs Business Ownership

Banks that succeed anchor AI where value is created: in the business units. Technology teams enable – they don’t define the use case logic.

Scale What Matters, Not Everything

A clear trend from the study: banks with measurable AI success focus on a limited number of strategically relevant use cases – and scale them consistently:

• clearly defined KPIs
• production-ready architectures
• continuous optimization

Marketing as an Underestimated Lever

Especially in banking marketing, AI offers significant untapped potential:

• improved lead qualification
• data-driven campaign orchestration
• consistent omnichannel customer journeys

Here, AI doesn’t just reduce costs – it enables growth when implemented correctly.

Practical Insight: Making AI in Marketing Measurable

A real-world example:
Instead of using AI solely for campaign optimization, leading banks connect marketing AI directly to CRM, sales, and product data. The result:

• more relevant customer communication
• shorter time-to-conversion
• transparent ROI for every initiative

The difference lies not in the model itself, but in the integration of data, processes, and clear objectives.

Conclusion: AI Won’t Decide Efficiency – It Will Decide Competitiveness

The Cofinpro study sends a clear message: banks understand the strategic relevance of AI. But bridging the gap between awareness and impact requires focus and discipline.

AI in banking is not a self-running system.
Its value emerges only when strategy, organization, and technology work together – with a relentless focus on measurable business outcomes.

For decision-makers, this means: fewer experiments, more clarity. Less tool discussion, more impact across the value chain.

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