The Truth About Banking’s AI Challenges

Artificial Intelligence (AI) is redefining competitive advantage in the banking industry. While frontrunners are already seeing measurable gains from AI, many institutions remain stuck in pilots. What’s holding them back? This article outlines the key barriers and how banks can overcome them to unlock AI’s full potential.

AI has moved beyond experimentation in banking, with over half of CIOs optimistic about its enterprise potential. Yet, as Gartner’s 2024 report on finance AI adoption reveals, many banks and financial institutions find the journey slower and more complex than expected.

Despite early wins in automation and personalization, scaling AI across the enterprise is hindered by fragmented data, evolving governance, and unclear return on investment (ROI). To unlock AI’s full potential, banks must align AI initiatives with business goals, embed governance early, and invest in cross-functional talent strategies.

Banks need a sense of urgency. Leading analysts from Gartner and McKinsey warn that the margin for error is shrinking. Gartner projects that by 2025, 30% of generative AI initiatives will fail due to poor data quality.

McKinsey echoes this concern, emphasizing that poor data input significantly undermines the performance and reliability of AI models. Without addressing foundational issues like data integrity and business alignment, even the most ambitious AI projects are likely to fall short.

Overcoming these challenges requires more than patching existing weaknesses. True success in banking lies in empowering teams or squads to harness AI’s expanding capabilities, align them with business objectives, and redesign workflows to drive measurable gains in efficiency, compliance, and customer experience.

Banks and financial institutions that take this approach are well-positioned to overcome common barriers, and unlock AI’s full potential as a sustainable competitive advantage.

Navigating the Barriers to AI Scaling

Several challenges hinder the journey from AI pilot projects to enterprise-wide adoption. First and foremost this includes data silos, in which disparate systems and poorly defined metadata reduce AI model accuracy and scalability.

Regulatory constraints also present challenges. Banks must balance innovation with stringent privacy and security requirements. It is critical that banks pursue AI operatives with data governance and compliance in mind.

And there is the talent shortage to consider. A skilled workforce is essential to effectively manage AI processes. The demand for specialists in data engineering, machine learning, and prompt engineering currently outpaces supply.

When these challenges compound, banks also face pilot fatigue. Without early, tangible returns on investment, securing stakeholder buy-in becomes increasingly challenging.

Harnessing AI's Transformative Potential

Despite these obstacles, AI offers unparalleled opportunities to enhance business processes. AI has the power to accelerate data retrieval, reducing the time employees spend searching for information, delivering critical insights in seconds.

AI models also excel at pattern recognition and anomaly detection, identifying irregularities in data to flag issues, improve risk management and prevent fraud.

Beyond data analysis, AI is transforming everyday tasks, notably in content creation, by which AI can automate tasks like drafting reports and generate summaries. This allows bank employees to focus on additional value-add, strategic work.

To foster improved customer experiences, virtual agents and chatbots are also improving vastly in their ability to handle routine queries and communicate in natural languages.

Core Applications of GenAI

To better understand the practical implications, let’s review three primary use cases where generative AI delivers significant value:

1. Q&A Bots for Knowledge Bases: These bots allow users to interact with client knowledge bases securely, providing controlled answers and referencing key documents. For example, Raiffeisenbank CZ reduced back-office support time by 75% using a GPT-based AI Assistant on Microsoft Azure, adhering to strict banking regulations while enabling accurate, real-time query responses.
2. Examination Tools: These tools process documents of variable structures, comparing them, identifying key information, and extracting data in a structured format. In practice, one German private bank deployed an AI Contract Review Assistant for DORA compliance, improving processing speed by 70% and reducing negotiation timelines from weeks to minutes.
3. AI Agents: Combining and chaining multiple AI tools, agents execute tasks and streamline well-defined processes. These agents can be grasped as virtual workers, capable of replacing entire tasks previously performed by humans, without the need for supervision. Agentic AI is rapidly evolving from concept to reality. Gartner identifies it as the top strategic technology trend for 2025, forecasting that by 2028, 33% of enterprise applications will include Agentic AI — up from less than 1% today. In banking, ING demonstrates how agentic AI can enhance customer service by deploying a generative AI agent to resolve customer requests, with a projected impact on 37 million customers across ten markets.

A Practical Roadmap to AI Scaling

To overcome the aforementioned obstacles and scale AI effectively, banks and financial institutions must consider the following steps:

1. Unify Data and Improve Metadata: Integrate data across systems and define consistent metadata standards to ensure AI models operate with complete, reliable information.
2. Start Small and Scale Strategically: Begin with well-defined, repetitive tasks that demonstrate quick returns, using these successes to build momentum for more complex initiatives.
3. Upskill and Partner: Invest in internal training programs and leverage external expertise to bridge the AI talent gap, accelerating progress without overburdening internal teams.
4. Embed Governance from Day One: Incorporate regulatory requirements into AI project planning and continuously monitor for updates to avoid disruptions.
5. Focus on ROI and KPIs: Define clear, measurable outcomes for every AI initiative, using metrics like time saved, error reduction, or cost savings to provide tangible evidence of value.

Moving Beyond Experimentation: Scaling AI in Banking

The path to enterprise-wide AI adoption is neither simple nor linear — but it is achievable. For banks and financial institutions, the opportunity extends far beyond automation. With the right foundation in data, governance, and strategic alignment, AI can drive sustainable growth, operational resilience, and enhanced customer experiences. The time to move beyond experimentation is now — those who master AI at scale will shape the future of financial services.

Author: Marek Paus, IT Engineer at Profinit, an Amdocs company