Business professionals collaborating on a financial strategy during an office meeting

Five hallmarks of effective AI strategies in banking

Authors

11 minute read 06 Apr 2026

A holistic enterprise AI strategy, centered around five core attributes, can help banks fulfill the transformative potential of AI.

In brief
  • Boards and C-suites across the banking industry are increasingly focused on generating tangible value from AI investments.
  • First-generation deployments have produced efficiency improvements and productivity gains, but the transformative possibilities have not yet been realized.
  • Effective senior leadership, strong governance models and cultural change are necessary for banks to succeed in the age of AI.

From innovation across the value chain and major gains in operational efficiency to more personalized experiences and stronger fraud detection, banks have boldly moved to embed AI across the business. Yet C‑suites and boards across industries are increasingly asking when these substantial investments will deliver the breakthrough value they expect.

As AI continues its rapid advance, today’s cutting-edge capabilities will be commoditized tomorrow. All types of competitors — incumbents, FinTechs, startups and tech platforms — are aggressively adopting AI. As banking C-suites and boards shape their AI strategies, they are seeking answers to pressing questions — from ethical and regulatory matters to product implications, competitive shifts, organizational readiness and talent gaps. The range of these issues helps explain why only 40% of banks consider themselves AI leaders, and even fewer feel they are effectively managing customer trust or AI reliability, according to the EY 2025 AI Confidence Pulse research, a survey of 50 global banks.

To be clear, banks have made significant progress with AI, with deployments across their businesses and involving tens of thousands of users at the largest institutions. “The challenge isn’t a lack of AI use cases, but narrowing the options to those that generate real value, measured against financial returns and strategic goals,” said Beatriz Sanz Sáiz, EY Global AI Sector Leader.

The challenge isn’t a lack of AI use cases, but narrowing the options to those that generate real value, measured against financial returns and strategic goals.

Beatriz Sanz Sáiz

EY Global AI Sector Leader

While some banks report that they are realizing benefits, maturity is uneven. According to the EY October 2025 Responsible AI Pulse Survey, eight in 10 banks have seen improvements in efficiency and productivity — the primary focus of many early AI use cases. However, just over half (53%) of banking survey respondents have reported revenue gains.1

Similarly, the latest Evident AI Banking Index2 shows that a small group of global banks are decisively pulling ahead in enterprise AI maturity, with top performers improving more than twice as fast year over year as the rest of the market — widening the gap with laggards.

2.3x
the rate at which the top 10 banks are maturing their AI capabilities relative to their peers.

Some institutions are experimenting with bolder applications and encouraging business leaders to imagine the possibilities if they had fleets of AI agents at their disposal. That’s as it should be. But, for a bank to fully embed AI within core operations and move beyond the basic benefits of efficiency and productivity, banks will also need to refine their cultures and modify their operating models, in tandem with augmenting their technology environments and updating their risk management models.

1

Chapter 1

Core attributes of effective AI strategies

To improve returns on their AI investments, banking leaders need to ensure the right strategies are in place.

EY technologists and business advisors believe that designing a strategic AI framework around five core attributes can unlock superior returns on AI investments. Individually, each attribute provides value through impactful use cases in risk, compliance, finance, tax and customer-facing operations. Collectively, these attributes give senior leaders and boards confidence that their institution is on the right track to achieve superior results from AI.

1. Transformative, holistic and coordinated

Reflecting AI’s power and potential, a bold and aspirational vision is essential and should guide all AI deployments across the organization — both at the enterprise level and within specific product lines or business units. The EY Global CEO Outlook shows that CEOs remain confident in their ability to drive growth and transformation through AI, even as economic and geopolitical uncertainty persists. In fact, nearly nine in 10 CEOs expect revenue growth and productivity gains to underpin profitability in 2026, driven by continued investment in transformation across the enterprise.

This confidence underscores why lingering regulatory uncertainty should not deter banks from thinking big about AI‑driven transformation and innovation. It means encouraging and equipping product development teams to explore new configurations, features and segmentation models. Strategists can use AI to explore how new business models and entirely new offerings will play out in different market conditions.

The enterprise AI vision should define common needs and objectives across key functions, with an eye toward centralizing core infrastructure and capabilities that can be extended and reused across the business. Finding the right balance of top-down guidance and bottom-up creativity can be challenging. An overly democratic approach to AI deployments can lead to fragmented use cases, duplicative tools, inconsistent data standards and gaps in governance. “Without a platform-based approach, banks risk creating 15 versions of the same AI function — just like they once used 15,000 spreadsheets,” explained Sameer Gupta, EY Global Financial Services AI Leader.

Without a platform-based approach, banks risk creating 15 versions of the same AI function — just like they once used 15,000 spreadsheets.

Sameer Gupta

EY Global Financial Services AI Leader

However, excessive centralization may inhibit the development of innovative solutions and discourage input from front-line staff. AI strategies should clarify where local experimentation is appropriate and outline the non-negotiable policies (e.g., data privacy and transparency in model design) that are best administered centrally. Enterprise-level AI agent libraries (e.g., reusable assets for fraud detection, forecasting and other tasks) can streamline the development of tailored AI applications, with consistent logic across use cases avoiding the sprawl of decentralized pilots. AI agents can pull from integrated finance, tax and risk data and automate regulatory filings, while ensuring data lineage and auditability. They can also automate and streamline stress testing, liquidity risk modeling and client onboarding. In fact, AI-enabled automation of document collection, client risk profiling and anomaly detection is helping to reduce processing times and improve the overall customer experience.

The most successful banks accelerate adoption by establishing key capabilities as a platform through which users can access AI tools. Rather than one-off projects, AI platforms can be developed and managed based on a unifying strategic vision, integrated architectures, and ongoing collaboration between business and technology leaders.

2. Risk-informed and robustly governed

Enterprise AI platforms are also the foundation for effective governance, which remains a significant challenge. The May 2025 EY Responsible AI Pulse Survey found that 52% of banks cite governance as their top challenge in adopting AI, and only 44% reported focusing investment on technology to support ethical AI adoption.

However, effective AI governance is also critical to realizing value from AI investments. The October 2025 EY Responsible AI Pulse Survey shows that banks with formal AI oversight committees and real‑time monitoring are significantly more likely to achieve revenue growth, cost savings and improvements in employee satisfaction from AI deployments.

Conversely, gaps in governance and controls may cause AI-based processes and experiences to erode customer trust and increase regulatory risk. Virtually all (98%) of the respondents in our October 2025 Responsible AI Pulse Survey reported that their organizations had suffered financial losses from AI-related risks. Hallucinations, poor data quality, bias and failures in data protection remain significant challenges to scaling AI across core operations.

98%
of respondents reported that their organizations had suffered financial losses from AI-related risks.

A governance watchtower approach — including automated controls testing, human oversight, standards for explainability and continuous model validation — has emerged as a leading practice. AI agents can pull from integrated finance, tax and risk data and automate regulatory filings, while supporting data lineage and auditability. “A strong AI strategy means clear ownership of data security, vendor management and governance, so AI is built and run the same way across the bank,” said Gupta. “Standardization is the way to scale responsibly.”

Governance models should be flexible for adjustments over time and to account for the complex ecosystems — featuring external partners and vendors, software-as-a-service (SaaS) and other tech platforms — that are increasingly the norm in banking. They should also include specific metrics for value creation, which can provide visibility into risk-adjusted returns. These are a few of the ways that strong governance can instill the confidence that’s essential for scaling AI.

3. Business-led and focused on strategic issues

Too many banks still treat AI as a technology matter rather than a strategic investment decision. “AI isn’t a backlog of use cases; it’s a capital allocation decision. 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,” observed Preetham Peddanagari, EY UK&I Chief Technology Officer.

AI isn’t a backlog of use cases; it’s a capital allocation decision. 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.

Preetham Peddanagari

EY UK&I Chief Technology Officer

How EY can help

AI investments should be directed toward banks’ greatest challenges and most compelling opportunities — those areas where proprietary data and differentiating capabilities can generate alpha. It’s understandable that first-generation use cases would aim to grab low-hanging fruit (e.g., automating manual back-office processes or chatbots handling basic service inquiries). Operational excellence remains a worthy goal.
 

Ultimately, enterprise AI should be designed to drive stronger business outcomes (e.g., revenue growth, risk reduction, product innovation and customer engagement), not just operational efficiency.
 

Senior executives can use ROI-based road-mapping linked to earnings before interest, taxes, depreciation and amortization (EBITDA) impact and functional KPIs to define strategic priorities. Such an approach will help overcome leaders’ reservations about their ability to prioritize the right use cases, which is an issue for 44% of banks, according to the 2025 EY AI Confidence Pulse study.

44%
of banks struggle to prioritize AI use cases.

At many banks, AI projects have been primarily IT-led. A better model involves IT leaders collaborating with their peers in the business to find the best path forward. Collaborations like these have led to some of the most impressive AI deployments we’ve seen in the market. Some banks are using digital twins in treasury services to simulate cash flow scenarios and optimize intraday liquidity. Others have automated the full order-to-cash lifecycle in commercial banking, with AI agents handling credit risk assessment, contract onboarding, invoice generation and collections.

4. Human-centered

Technology alone won’t deliver the AI returns banks are looking for. Skilled AI teams, human-machine collaboration and change management hold the key to success. Training and upskilling efforts will teach workers how to get better at their jobs using AI, especially when they are linked to holistic workforce transformation programs. Furthermore, a collaborative EY/MIT report3 found that more than three‑quarters of executives now view agentic AI as a coworker, a shift that fundamentally changes how workflows and governance models should be designed.

Consider how junior analysts will review initial AI outputs in building deal books, while senior underwriters will validate basic credit decisions made by AI and use AI-driven analytics to find anomalies in loan portfolios. Commercial bankers will use copilots to consolidate customer interaction history, assess the overall risk posture and flag opportunities to boost engagement with individual customers. These human-in-the-loop processes can help mitigate hallucinations and build trust in AI outcomes, beyond simply boosting productivity.

Banks will also create entirely new roles (e.g., prompt engineers, AI workflow designers, bot whisperers and agent wranglers) as AI becomes more prominent across operations. These steps will shorten the path to value and reduce risk along the way. Clear communication, visible leadership and other proven practices for organizational change management can instill an AI-positive mindset within the culture. “The biggest barrier to scaling AI isn’t the algorithm — it’s the change management,” said Gupta. “Training teams, reworking processes and putting in the right governance often takes twice the effort of building the model itself.”

EY and other research offer reasons for optimism. The EY US Agentic AI Workforce Survey (via EY.com US) found that 84% of desk-based employees are enthusiastic about working with AI agents. However, 56% worry about job security, presenting a paradox that banking leaders will have to address.

5. Futuristic and designed for the long term

While there is urgency for banks to accelerate their AI journeys, leaders must recognize the length of the game they are playing with AI and consider how AI (and agentic AI) will reshape the future of banking. This means looking over the horizon and preparing to pivot as technology advances and new capabilities create new possibilities. It’s certainly not too soon to start thinking about how AI will interact with digital assets, tokenization, quantum computing and other next-generation technologies. Again, today’s breakthrough innovations will be tomorrow’s table stakes when it comes to AI.

The long-term planning should be reflected in the design of AI technical infrastructure, with an emphasis on modularity, reusability and scalability. As vendors (from startups to SaaS platforms) mature their AI capabilities, it’s likely that baseline functionality will be embedded in their offerings, removing the need for proprietary development by banks. Though cloud environments are vital, regulated banks are also exploring other options for the most sensitive use cases (e.g., using proprietary large language models in on-premises tech environments). Hybrid approaches like these will likely become more common in the future, as banks seek to balance risk and return, and enable innovation within compliant processes. Banks must also continually evaluate their vendor relationships and be ready to change course as their needs and priorities evolve and new types of solutions emerge.  

2

Chapter 2

Recommended actions for AI strategies

The right approach starts with balance between forward-looking innovation goals, risk management rigor and an unwavering commitment to responsible and ethical usage.

So, what actions can C-suite leaders and directors immediately prioritize to define a viable enterprise strategy for AI?

  1. Set the tone from the top: Move beyond fragmented pilots by presenting a clear vision for the entire business, with direct links to the core strategic goals and financial targets. Encourage bold and creative thinking, and motivate business leaders and innovation teams to reimagine the possible. Support these bold ambitions with clear definitions of enterprise data standards for data security, vendor management, model governance and other areas.

  2. Set the right measures for success: Establish a disciplined ROI-based roadmap that links AI investments directly to EBITDA, P&L and other bottom-line impacts, as well as business-oriented targets (e.g., risk reduction and customer engagement).

  3. Establish a “watchtower” for governance: Clarify oversight responsibilities at the board and executive levels, and design a multilayered governance model that addresses the entire AI lifecycle, with participation from technology, risk, legal and compliance functions, as well as the business. Create standards for explainability and reliability, and implement automated monitoring and reporting.

  4. Prioritize data lineage and quality: Deploy AI tools to identify data quality issues, automate lineage tracking and establish tracking mechanisms for all data (including third-party data) used in large language models and AI applications.

  5. Link AI to business-led transformation: Shift accountability for outcomes from IT to business leaders, particularly relative to product development, client service and other market-facing processes, as well as first-line risks. Encourage business leaders to think long term — we remain in the early stages of AI adoption in banking.

  6. Engage regulators: Designate resources to participate in the regulatory process, particularly in industry efforts to shape standards for data security, consumer-facing applications and ethical usage.

  7. Invest in people and culture to accelerate adoption: Begin upskilling programs for frontline bankers, analysts and managers to build AI fluency. Create a comprehensive inventory of current skills gaps and future needs. Deploy proven change management techniques to drive adoption at scale, and empower middle management to promote AI-friendly and AI-curious cultures that allow for responsible experimentation.

On the path to translating AI investment into outsized returns, banks need to devise ambitious AI strategies centered on innovation and business needs, and then execute them based on disciplined governance and a fully engaged and empowered workforce.


Summary

Enhancing AI ROI requires leadership, vision and cultural change — not just technical experience. As the options and possibilities continue to expand, the C-suite and boards will play a critical role in defining robust governance models, clarifying business ownership and empowering their people.

Related articles

Five questions banks must ask to turn technology spend into value

Banks are increasing technology spend, but long-term value remains hard to unlock. Learn how banks can improve return on investment.

Nigel Moden + 1

Five ways banks can scale Global Capability Centers for AI value

Get ready for Global Capability Centers to transform your banking operations.

Nigel Moden + 1

How can reimagining today's workforce help banks shape their future?

By focusing on four bold imperatives, banks can differentiate in the competition for top talent and drive higher returns on people investments.

Nigel Moden + 1

    About this article

    Authors

    Related topics
    Financial services
    Banking & Capital Markets
    Risk and regulatory transformation
    Banking transformation

    EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.