Five attributes that separate high-performing banks from AI laggards
Only 40% of banks consider themselves AI leaders. The gap between top performers and the rest is widening fast - the best banks are maturing their AI capabilities 2.3 times faster than peers, according to EY research on 50 global banks.
Banks have deployed AI across their operations and reached tens of thousands of users at large institutions. Yet most still struggle to move beyond efficiency gains. Eight in 10 banks report improvements in productivity. Only 53% report revenue growth.
The problem isn't a shortage of AI use cases. It's choosing which ones generate real value and scaling them responsibly. For product development leaders, this means understanding how effective AI strategies actually work in practice.
1. Build a coordinated vision, not fragmented pilots
Banks that succeed treat AI as a company-wide strategy, not a collection of isolated projects. A clear vision should guide deployments across product lines and business units alike.
The risk of decentralization is real. Without a platform-based approach, banks end up building 15 versions of the same AI function - just as they once maintained 15,000 spreadsheets. Fragmentation creates duplicate tools, inconsistent data standards, and gaps in governance.
The answer is balance. Establish what must be centralized - data privacy, model transparency, core infrastructure - and clarify where local teams can experiment. Enterprise AI agent libraries for fraud detection, forecasting, and other tasks let teams build customized applications while maintaining consistent logic across use cases.
For product teams, this means advocating for platform-based access to AI tools rather than one-off projects. It also means pushing for reusable components that accelerate development and reduce redundant work.
2. Governance determines whether AI creates or destroys value
Governance is the top challenge cited by 52% of banks. Yet banks with formal AI oversight committees and real-time monitoring are significantly more likely to achieve revenue growth and cost savings.
The stakes are high. Nearly all surveyed banks (98%) reported financial losses from AI-related risks - hallucinations, poor data quality, bias, and data protection failures.
A governance watchtower approach works: automated controls testing, human oversight, standards for explainability, and continuous model validation. This infrastructure should track data lineage and support auditability, especially critical for regulatory filings and risk assessments.
Product teams need to embed governance thinking early. That means designing for explainability, documenting data sources, and building monitoring into features rather than bolting it on later.
3. Treat AI as a capital allocation decision, not a technology backlog
Too many banks still let IT departments drive AI priorities. The better approach: business leaders make the call on which bets to fund.
This matters for product development. Early AI use cases focused on low-hanging fruit - automating back-office work, basic chatbots. That's fine for operational gains. But to drive revenue growth and competitive advantage, AI should target areas where proprietary data and differentiation matter most.
Use ROI-based roadmapping tied to earnings and functional KPIs. Fund only projects you can measure end-to-end and that prove both value and trustworthiness at scale. Shut down everything else.
For product leaders, this means building a business case before development begins, not after. Link every AI feature to specific financial or strategic outcomes - customer acquisition, retention, risk reduction, or product innovation.
4. Humans and machines work best together
Skilled AI teams and human-machine collaboration determine success more than raw technology. Training programs that teach workers how to use AI effectively matter more than the algorithm itself.
The research shows a paradox: 84% of desk-based employees are enthusiastic about working with AI agents, yet 56% worry about job security. Banks must address this tension directly through transparent communication and clear career paths.
New roles are emerging - prompt engineers, AI workflow designers, bot wranglers. These positions bridge the gap between AI capabilities and business outcomes.
Consider how human-in-the-loop processes work in practice. Junior analysts review initial AI outputs for deal books. Senior underwriters validate AI credit decisions and hunt for anomalies in loan portfolios. Commercial bankers use AI copilots to consolidate customer history and flag engagement opportunities. These workflows mitigate hallucinations and build trust.
Product teams should design features assuming humans will review and validate AI outputs, not replace them. Train users on when to trust AI recommendations and when to override them.
5. Plan for the long game
Today's breakthrough innovations become tomorrow's table stakes. Banks must design AI infrastructure for modularity, reusability, and scalability from the start.
As vendors mature their offerings, baseline AI functionality will shift from proprietary development to off-the-shelf software. Some banks are already exploring hybrid approaches - cloud platforms for most work, on-premises models for sensitive use cases - to balance risk and innovation.
Regulated banks will need flexibility to pivot as technology advances and new capabilities emerge. Vendor relationships should be evaluated continuously. The infrastructure built today should accommodate quantum computing, tokenization, and digital assets without major overhauls.
For product development, this means architecting systems that can swap out underlying AI models and vendors without breaking the user experience. Avoid lock-in to specific platforms or vendors.
What product leaders should do now
Start by defining success in business terms, not technical terms. Link every AI initiative to revenue, cost reduction, risk mitigation, or customer engagement metrics.
Second, involve risk, legal, and compliance teams early. Governance isn't a constraint - it's what allows you to scale confidently. Design for auditability and explainability from day one.
Third, invest in your team's AI fluency. Identify skill gaps and build upskilling programs for analysts, managers, and product managers. Change management takes twice the effort of building the model itself.
Finally, think long-term. Banks remain in the early stages of AI adoption. The platforms and capabilities you build now should accommodate the next generation of AI, not just today's use cases.
The banks pulling ahead aren't the ones with the most AI pilots. They're the ones executing disciplined strategies with clear ownership, measurable outcomes, and fully engaged teams.
For product development professionals, an AI learning path for product managers covers strategy, innovation, and product analytics essential for implementing effective AI strategies. Understanding generative AI and LLM capabilities also helps product teams design features that leverage these technologies responsibly.
Your membership also unlocks:
