The AI Banking Spectrum: Explorers, Implementers, and Scalers

Artificial Intelligence (AI) is reshaping industries across the globe, but the banking sector has been a cautious adopter, lagging when compared to other financial intermediaries. While some banks have embraced AI with gusto, others are still testing the waters. Broadly speaking, banks can be categorized into three groups based on their AI adoption journey: Explorers, Implementers, and Scalers. Each category reflects a distinct mindset, strategy, and level of readiness for leveraging AI.

Explorers: The Hesitant Beginners

Explorers represent the banks that are still in the early stages of AI adoption. They’ve only recently begun evaluating AI’s potential- as late as 2023, often driven by industry trends rather than a clear vision. For these banks, AI is more of an experiment than a strategic imperative.

Strategy & Readiness

Explorers lack a cohesive strategy for AI. Their initiatives are myopic, focusing on short-term ROI justifications rather than long-term transformation. AI projects are often delegated to mid-level teams and operating in silos without strong executive buy-in or measurable goals.

Budget constraints further hinder progress. Instead of allocating dedicated resources for AI, these banks repurpose funds from existing projects on an ad-hoc basis. Critical components like data readiness and cloud infrastructure are either absent or poorly developed. Choices of tools and platforms is primarily off the shelf, based on cost and often run into integration challenges.

Areas of Focus

Explorers stick to low-risk, low-impact areas, like the ones mentioned below.

• Chatbots for basic query resolution without human intervention.
• Document digitization using Optical Character Recognition (OCR) systems.

These initiatives are often driven by hype rather than a clear understanding of value creation.

Competency

Talent is another weak spot for Explorers. Most senior executives lack a solid understanding of AI, and teams working on AI projects are often pulled from other departments with minimal training. Certifications and upskilling efforts are sporadic at best.

Reality Check: As per digital banking report 2024, around 68% of respondent banks are in the beginning stages of their AI journey.

Implementers: The Tactical Players

Implementers have moved beyond experimentation (PoCs and pilots) and are actively deploying AI in specific areas of their operations with clearly defined ROI. However, their efforts remain siloed and tactical rather than aimed at enterprise-wide adoption. They began experimenting with structured AI pilots as early as 2019, primarily focusing on customer-facing areas and, to some extent, improving operational efficiency. COVID pushed them to accelerate their AI efforts, across areas like chatbots and transaction monitoring systems to detect fraud. However, their AI investments are relatively modest, focused more on co-developing solutions. While they showcase the outcomes of their AI use cases, they aren’t ready to disclose the financial impacts yet.

Strategy & Readiness

While Implementers have defined strategies, these are localized to individual business units rather than being enterprise wide. The projects are siloed with minimal cross functional integration. They prioritize tactical efficiency gains over holistic transformation.

Their infrastructure is a mix of legacy systems and hybrid cloud solutions, which limits scalability. Unlike Scalers, Implementers struggle to achieve seamless integration across departments due to fragmented governance.

Areas of Focus

Implementers target moderate complexity use cases with demonstrable ROI, with emphasis on generative AI rather than agentic AI.

• Fraud Detection: AI models analyze large datasets in real time to identify patterns and anomalies that traditional rule-based systems might miss. More often, these bring down the rate of false positives. In addition, they possess the ability to analyse historic data and ear mark those transactions with the propensity to be classified as suspicious ones. However, they haven’t yet adopted advanced generative AI models to simulate new fraud scenarios proactively.
• Customer Service: AI-powered chatbots excel at understanding user intent to provide personalized responses and resolve common queries efficiently. While this reduces human intervention freeing up agent time that can be used for more critical tasks, Implementers lag behind Scalers in integrating advanced features like voice recognition or emotion detection.

Competency

Implementers rely heavily on external vendors for AI expertise, which creates dependency and limits innovation. They vie for talent and have high levels of attrition on account of the competition with scalers.  Internal training programs focus on basic skills like model tuning rather than advanced capabilities like generative reasoning or autonomous decision-making.

Moreover, leadership teams often lack relevant certifications or deep technical knowledge. Patent filings are minimal and typically incremental rather than groundbreaking.

The Numbers Game: According to IBM Institute for Business Value (IBM IBV) research, 78% banks in 2024, demonstrated tactical approach to leverage generative AI across the enterprise.

Scalers: The Visionary Leaders

Scalers represent the pinnacle of AI adoption in banking. These banks have embraced AI at an organizational level, integrating it into their core strategies and operations. They were quick to jump the AI wave as early as 2015, working on select low risk areas like contract intelligence and routine customer interactions. Although done with an intent to counter the effect of Fintechs, they are now in a good position with respect to their vision, ability to execute and realise cost benefits from AI initiatives. A key example of scaler – JPMC, reported $1 billion-1.5 billion in terms of the value assigned to their AI use cases. IBM Institute for Business Value (IBM IBV) research shows only 8% banks in 2024 were embracing Gen AI across the board.

Strategy & Readiness

Scalers have a clear vision for how AI fits into their long-term goals. Their strategies are enterprise-wide, with quantifiable objectives tied to scalability and impact. Dedicated leadership teams drive these initiatives with centralized governance mechanisms to ensure ethical and unbiased use of AI.

These banks invested early in foundational technologies like cloud infrastructure and data architectures. For example, Capital One began experimenting with public cloud platforms as early as 2013 and completed its migration from data centers to AWS in 2020, becoming the first US bank to report moving to public cloud. They also have focused investments in AI outfits or startups e.g., JPMC with clearyeye.ai or Citi with Glean.

Areas of Focus

Scalers tackle complex problems with broad coverage across multiple domains:

• Hyper-Personalization: By analyzing customer profiles, spending patterns, risk appetites, and investment objectives, Scalers create tailored financial products or adjust pricing dynamically based on individual needs. Understanding customer behavior in depth, they can transform communication based on customer preferences in terms of messaging, tonality, marketing channels, day/time etc.
• Real-Time Decisioning: Advanced analytics enable Scalers to approve or reject credit applications in minutes by leveraging both structured and unstructured data sets. This reduces loan defaults by identifying hidden risks early on. Data on the broader economy and hidden patterns on consumer data as well as transaction history can uncover probable challenges in loan repayment and thus predict nonpayment.

Competency

Scalers boast high-density talent pools – leadership as well as practitioners, comprising of data scientists, engineers, and GenAI specialists. They invest heavily in research labs, open-source contributions, patents, and partnerships with academia to stay ahead of the curve.

Upskilling programs are robust—some even partner with leading platforms like OpenAI or Microsoft for specialized training courses.

The Elite Few: Only about 8% of banks fall into this category as of 2024 (IBM Institute for Business Value). The top ten banks in Evident AI index are advancing AI adoption at twice the rate of the remaining 40.

What Sets Scalers Apart?

The difference between Scalers and the rest boils down to three key factors:

• Visionary Leadership: Scalers have leaders who understand the transformative potential of AI and champion its adoption across the organization.
• Early Investments: By prioritizing foundational technologies early on—like cloud migration—they have built scalable systems that can support advanced AI applications. In addition, they made the right investments in talent and innovation very early.
• Holistic Integration: Unlike Explorers or Implementers, Scalers integrate AI seamlessly across functions rather than limiting it to isolated use cases.

The Takeaway: A Call to Action

The journey from Explorer to Scaler isn’t just about adopting new technology; it’s about rethinking how banks operate at their core. Here’s what each category can learn:

• Explorers need to stop dabbling in low-impact projects and commit to building foundational capabilities like cloud infrastructure and data readiness.
• Implementers must break down silos and adopt enterprise-wide governance frameworks to scale their efforts effectively.
• Scalers should continue pushing boundaries by investing in moonshot projects that redefine what’s possible with AI.

In an era where nimble Fintechs threaten traditional banking models, staying stagnant is not an option. The future belongs to those who can harness the power of AI—not just tactically but strategically—to deliver unparalleled value to customers while driving operational excellence.

References:

State of AI in Banking : https://www.opentext.com/assets/documents/en-US/pdf/state-of-ai-in-banking-digital-banking-report-en.pdf

2025 Global Outlook for Banking and Financial markets: https://www.ibm.com/thought-leadership/institute-business-value/report/2025-banking-financial-markets-outlook

Evident AI index 2024: https://evidentinsights.com/ai-index/

https://hbr.org/sponsored/2021/02/how-capital-one-moved-its-data-analytics-to-the-cloud