Why Most AI Strategies in Banking Fail and How to Build a One That Scales

 Key Highlights:  

• Most AI strategies in banking stall because pilots aren’t linked to clear business goals, KPIs, or ownership. 
• Scaling AI is mainly a foundations problem, not a technology one: clean data, built-in governance, the right skills, and early involvement of risk and legal teams matter more than model performance. 
• Banks get real value when AI is applied to full business processes - like onboarding, lending, fraud, or compliance - and designed to work reliably within existing systems, controls, and teams. 

What to Expect from this Article 

I’ve spoken with over a dozen banking leaders last year, and many are genuinely excited about AI. But when we talk about concrete plans, most are still focused on small pilots or minor upgrades. What’s often called “strategy” is really a series of careful experiments that rarely lead to long-term advantage. 

A Boston Consulting Group (BCG) report shows the gap clearly: 44% of banks are still focused on isolated deployments. In contrast, just 27% are using AI to reimagine products, services or business models, the kind of moves that signal true strategic intent. 

What’s holding banks back? In this article, I’ll unpack why momentum often slows and what it takes to build an AI strategy in banking that’s focused, future-ready and built to deliver real impact. 

What Slows Down AI Adoption in Banking  

There may be many reasons for slow AI adoption in banking, but the most common ones are strategy gaps, process friction, talent shortages, ROI demands, and data quality. Let’s look at each one.  

Strategy Gaps 

When executing pilots, many banks don’t tie the desired results to clear KPIs and objectives, making it hard to judge success. If it’s not obvious what the pilot is meant to improve and no one is responsible for the outcome, it usually never moves past the testing stage. 

Process Friction 

Banks rely on rigid systems and rules, which are deeply embedded in the work process. When AI is introduced, those processes need to be adjusted, which takes time, patience, and culture change.  

Talent Shortages 

Roles like AI engineers, data scientists and UX specialists are now core to delivery, yet the hiring landscape remains tough, with 58% of banks admitting they lack technology skills and capabilities.  

ROI Demands 

The parts of AI that matter most for scale - clean data and governance - take time and money, but don’t show immediate payoff.  When they’re skipped in favor of short-term pilots, progress stalls as soon as teams try to scale.  

Data Quality 

Probably the most important piece of all. Our Engineering Director, Peter Ivanov, said it very well: “While it’s tempting to dive in with advanced algorithms, I’ve learned that starting with a solid data foundation and scalable infrastructure makes all the difference.” Without clean, well-governed data, AI outputs can’t be trusted, affecting performance, accuracy, and regulatory approval. 

How to Execute Winning AI Strategy in Banking? 5 Steps to Move from Pilots to Scale 

An effective AI strategy in banking means knowing exactly what to scale, where to apply it and how to make it repeatable. This 5-step plan helps you make the calls with more clarity, more confidence, and fewer costly detours. 

1. Focus on the Fundamentals 

Every successful plan starts with high-quality data at its core. The stakes are high: Gartner predicts that through 2026, organizations will cancel 60% of AI projects because the underlying data isn’t ready to support them. 

Clean, consistent and well-governed data is what makes models trustworthy, explainable and fit for production. That starts with shared standards, clear ownership and unified architecture. One golden rule: each business unit should take responsibility for the data it creates, so issues are fixed early, close to the source, instead of spreading across the system. 

To keep that quality over time, build governance into daily operations. Set clear data requirements for accuracy, completeness and timeliness, and ensure those checks are automated. This helps catch issues early, before they affect decisions, and gives your AI systems the stability they need to scale confidently.  

2. Look at pilots through a business perspective

Technical performance alone doesn’t justify scaling. The real question is whether it makes a noticeable difference to the business. To answer that, you need to be clear from the start about why you’re using AI in the first place, set up simple business KPIs, and make sure someone is responsible for the outcome. 

Next, review each initiative with a cross-functional group - product, risk, finance, and operations, and look beyond whether the model worked technically. The focus should be on commercial outcomes agreed upfront: did it improve results, reduce costs, or change decisions in a meaningful way? 

If that impact is hard to see, or if the results don’t hold up in real-world conditions, it’s better to slow down than to push ahead. Tighten the scope, test it with live data, or redirect effort to a use case with clearer value. Scaling only makes sense when the business case is solid and the results can be repeated at scale. 

3. Resist Isolated Pilots 

Many banks get stuck in the pilot phase because they focus on small tasks that look innovative but don’t scale – think about processes like summarizing documents or tagging emails. These projects may demonstrate capability, but they rarely unlock meaningful value. 

Real momentum comes when AI is applied to end-to-end processes where delays, manual checks, or risk exposure pile up, such as credit underwriting, compliance monitoring, fraud prevention, or customer onboarding. To spot these issues early, follow a real case from start to finish. Note where it waits for approvals, gets sent back for missing information, or requires manual checks across multiple tools. These are the points where AI can reduce friction.  

That’s the approach Accedia took with Castle Trust. Our team supported the integration of an AI-powered identity verification solution into their lending platform. The goal wasn’t to add another AI feature, but to strengthen the entire lending process. This upgrade improved fraud detection, supported KYC compliance, and reduced friction for customers, all as part of a broader modernization effort. Because it was designed around the full workflow, it scaled naturally and delivered lasting value. 

Explore More Successful AI Use Cases  

4. Build Regulatory-Ready AI 

Successful AI implementation in banking depends on involving risk and legal teams early, while models and processes are still taking shape. This helps align upfront on how decisions will be explained, tested, and approved, before the system is built and changes become costly. With this groundwork in place, decisions are easier to trace, updates move faster, and AI systems can keep evolving as regulations change. 

Citibank took this approach when introducing AI into transaction monitoring. By working closely with compliance teams, they built models that could be reviewed, adjusted, and kept aligned with changing requirements. This helped reduce false positives without increasing risk. It’s a reminder that solid AI implementation in banking isn’t about working around regulation, but about designing it in mind from the start.

5. Boost AI Culture  

Finally, scaling AI takes more than technical skills. It’s also about people feeling comfortable using it, questioning it, and knowing when not to rely on it. That starts with training that’s practical and role-specific - showing teams how AI fits into their daily work, where human judgment still matters, and how to spot issues like bias or unclear results. Hands-on sessions tied to real workflows work best. 

As adoption grows, banks also need to strengthen teams with roles like machine learning engineers or data scientists. Since building these capabilities in-house takes time, partnerships with external vendors can help bridge the gap, bringing in focused expertise, supporting delivery, and helping teams move faster while internal skills continue to develop. 

Here’s Your 7-Step Playbook for Choosing the Right IT Consulting Company 

At Accedia, we’ve learned that culture matters as much as technology. We invest in ongoing AI trainings, host regular tech talks on AI topics, and recently launched season 10 of our internal IDC initiative entirely focused on AI. These efforts help teams stay curious, ask sharper questions, and use AI in a responsible way. 

Gain a Competitive Advantage 

Pilots may be showing promise, but what leaders need now is the bigger picture: aligning people, data, and governance around an approach that can grow with the business. A strong AI strategy in banking does more than guide deployment - it turns AI into a capability the organization can rely on. So it’s worth asking: are you simply experimenting with tools, or building something that can scale across the bank? 

If you’re ready to move that conversation forward, now is a good time to take a closer look at where your AI efforts stand and what it would take to turn them into lasting impact. Get in touch with us, we will be happy to help you think through the next steps. 

This article was originally published by Dimitar Dimitrov, Managing Partner at Accedia, as a contribution to the Forbes Technology Council.