AI Implementation in Banking: Explorers, Implementers, and Leaders

Artificial Intelligence in Banking: Where We Are and Where We're Headed

Artificial Intelligence (#AI) is transforming the financial industry worldwide, but its adoption in banking has been slower than in other sectors. While some banks have strategically integrated AI, others are still in the exploration phase. Our experience at WAU working with financial institutions on custom software development has identified three levels of AI adoption in banking: Explorers, Implementers, and Leaders.

Each group has a different vision, strategy, and level of readiness to harness AI’s full potential.

Explorers: Still in the Testing Phase

These banks are in the early stages of AI adoption. Even though AI is already reshaping the financial world, it still evaluates its potential without a clear strategy. For them, AI is not a strategic priority but an experiment.

Strategy and Readiness

• No well-defined roadmap, leading to isolated projects with little long-term impact.

• Minimal investment: budgets for AI are often repurposed from other projects.

• Lack of proper infrastructure, limiting the integration of advanced solutions.

Application Areas Explorers tend to focus on low-risk, low-impact initiatives such as:

• Basic chatbots for common inquiries.

• Document digitization with OCR, with limited intelligent automation.

Talent and Capabilities

• Teams lack AI specialists, and senior leadership lacks strong understanding.

• Minimal internal AI training, slowing progress.

Fact: According to the Digital Banking 2024 report, 68% of banks are still in this initial phase.

Implementers: Using AI Tactically

Implementers have moved beyond experimentation and are applying AI in key areas with clear ROI. However, their efforts remain fragmented and aren’t yet part of a cohesive enterprise strategy.

Strategy and Readiness

• AI is deployed in specific business units but without a global vision.

• A mix of legacy systems and cloud solutions makes scalability difficult.

• Decentralized AI governance poses integration challenges.

Application Areas AI is already making an impact in core processes such as:

• Fraud detection: AI models analyze real-time data to identify suspicious patterns.

• Customer service: Advanced chatbots understand user intent and deliver more personalized responses.

Talent and Capabilities

• Heavy reliance on external vendors limits innovation.

• Basic AI training focuses more on tuning existing models than building advanced capabilities.

Key Insight: According to IBM's Institute for Business Value, 78% of banks in 2024 will still use AI tactically without an integrated strategy.

Leaders: Driving Transformation with Vision

These banks have embedded AI into their organizational DNA. They see AI not as optional but as a strategic pillar for the future.

Strategy and Readiness

• Clear vision and strong AI governance model.

• Investments in advanced infrastructure such as cloud platforms and optimized data architectures.

• Example: Capital One fully migrated to AWS in 2020, enabling seamless AI scalability.

Application Areas Leaders tackle complex challenges using AI across multiple fronts:

• Hyper-personalization: Real-time data analysis to deliver tailored financial products.

• Automated decisions: AI models assess risk and approve or deny credit within seconds.

Talent and Capabilities

• In-house AI experts, investments in R&D, and partnerships with universities and innovation hubs.

• Filing patents, contributing to open-source, and staying on the cutting edge of technology.

Success Story: JPMC reported between $1 billion and $1.5 billion in value generated from AI-powered operations.

Conclusion: How to Move from Explorer to Leader

The journey to AI in banking isn’t just about technology but vision, strategy, and execution. At WAU, we’ve seen that the banks successfully evolving in this transformation share three key elements:

✅ Visionary leadership committed to AI as a driver of change.

✅ Strategic investments in infrastructure and talent.

✅ Integration of AI into the organizational culture—not just isolated projects.

Banks that understand AI as a competitive advantage—not just a trend—will lead the industry's future. The real question isn’t whether to adopt it but when and how to do so effectively.