Five hallmarks of effective AI strategies in banking
Banks have deployed AI across their operations, but only 40% consider themselves AI leaders. The gap between investment and results reflects a fundamental problem: most institutions treat AI as a technology issue rather than a strategic one.
Eight in 10 banks report efficiency gains from AI. Only 53% report revenue growth. That disparity matters for product development teams tasked with building AI-driven solutions - it signals that first-generation use cases are working, but transformative applications remain elusive.
The top 10 banks globally are maturing their AI capabilities 2.3 times faster than their peers, according to the Evident AI Banking Index. This widening gap suggests that strategy and execution discipline, not technology alone, determine winners.
1. Transformative, holistic and coordinated
Banks need a bold vision for AI that guides all deployments across the enterprise. Without it, product teams create duplicative tools, inconsistent data standards and fragmented use cases.
The risk is real. "Without a platform-based approach, banks risk creating 15 versions of the same AI function - just like they once used 15,000 spreadsheets," according to an EY financial services leader.
The solution involves balancing top-down guidance with bottom-up creativity. Define non-negotiable policies centrally - data privacy, model transparency, governance standards. Allow local experimentation within those guardrails. Enterprise-level AI agent libraries can streamline development while maintaining consistent logic across use cases.
Successful banks establish AI capabilities as platforms rather than one-off projects. Users access AI tools through integrated architectures managed by business and technology leaders working together.
2. Risk-informed and robustly governed
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 financial stakes are high. Ninety-eight percent of banks surveyed reported financial losses from AI-related risks - hallucinations, poor data quality, bias and data protection failures.
A "watchtower" governance approach uses automated controls testing, human oversight and continuous model validation. It requires clear ownership across data security, vendor management and model governance. This standardization is how banks scale responsibly.
Governance models must remain flexible. They should include specific metrics for value creation and account for complex ecosystems featuring external vendors, SaaS platforms and third-party data sources.
3. Business-led and focused on strategic issues
Too many banks still let IT drive AI decisions. That's backwards. AI is a capital allocation decision, not a backlog of use cases.
"Fund the few bets you can govern and measure end to end, and shut down everything that can't prove both value and trust at scale," according to an EY technology leader.
First-generation use cases chased low-hanging fruit - automating back-office work, handling basic service inquiries. Those deliver operational efficiency. Enterprise AI should drive stronger business outcomes: revenue growth, risk reduction, product innovation and customer engagement.
Product development teams should use ROI-based roadmapping linked to EBITDA impact and functional KPIs. Forty-four percent of banks struggle to prioritize use cases. Disciplined financial discipline solves that problem.
The best deployments pair IT expertise with business leadership. Some banks use digital twins to simulate cash flow scenarios and optimize liquidity. Others automated the full order-to-cash lifecycle with AI agents handling credit assessment, contract onboarding and collections.
4. Human-centered
Technology alone won't deliver returns. Skilled teams, human-machine collaboration and change management are decisive.
Seventy-five percent of executives now view agentic AI as a coworker, a shift that fundamentally changes workflow and governance design. Junior analysts review AI outputs before senior underwriters validate decisions. Commercial bankers use copilots to consolidate customer history and flag engagement opportunities. These human-in-the-loop processes mitigate hallucinations and build trust.
Banks will create entirely new roles: prompt engineers, AI workflow designers, bot whisperers and agent wranglers. These steps shorten the path to value and reduce risk.
Change management is critical. "The biggest barrier to scaling AI isn't the algorithm - it's the change management," according to an EY leader. "Training teams, reworking processes and putting in the right governance often takes twice the effort of building the model itself."
Eighty-four percent of desk-based employees are enthusiastic about working with AI agents. But 56% worry about job security. Banking leaders must address this paradox directly through clear communication and visible leadership.
5. Futuristic and designed for the long term
Banks face urgency to accelerate AI adoption. They also need to think years ahead. Today's breakthrough innovations become tomorrow's table stakes.
Technical infrastructure should emphasize modularity, reusability and scalability. As vendors mature their AI capabilities, baseline functionality will move into their standard offerings, reducing the need for proprietary development.
Cloud environments remain vital, but regulated banks are exploring hybrid approaches for sensitive use cases - using proprietary large language models in on-premises environments. This balance between risk and innovation will likely become standard practice.
Banks must continually evaluate vendor relationships and be ready to change course as needs evolve and new solutions emerge.
Seven actions for the C-suite
- Set the tone from the top: Present a clear vision linked to core strategic goals and financial targets. Support bold thinking with clear definitions of enterprise data standards, data security, vendor management and model governance.
- Set the right measures for success: Establish a disciplined ROI-based roadmap linking AI investments directly to EBITDA, P&L and business-oriented targets like risk reduction and customer engagement.
- Establish a "watchtower" for governance: Clarify oversight responsibilities at board and executive levels. Design a multilayered governance model addressing the entire AI lifecycle with participation from technology, risk, legal, compliance and business functions.
- Prioritize data lineage and quality: Deploy AI tools to identify data quality issues, automate lineage tracking and establish tracking mechanisms for all data used in large language models and AI applications.
- Link AI to business-led transformation: Shift accountability for outcomes from IT to business leaders, particularly for product development and client service. Encourage long-term thinking - banking remains in early stages of AI adoption.
- Engage regulators: Designate resources to participate in the regulatory process, particularly industry efforts to shape standards for data security, consumer-facing applications and ethical usage.
- Invest in people and culture: Begin upskilling programs for frontline bankers, analysts and managers. Create an inventory of current skills gaps and future needs. Deploy proven change management techniques and empower middle management to promote AI-friendly cultures that allow responsible experimentation.
For product development teams, the message is clear: AI strategy determines outcomes more than AI capability. Strategic prioritization, disciplined governance and human-centered design separate leaders from laggards.
Consider the AI Learning Path for Product Managers to develop skills in AI product strategy, roadmap planning and innovation automation - the core competencies this article describes.
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