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From AI curiosity to AI capability: A data driven path for financial institutions 

April 30, 2026 · Authored by Jordan Anderson, Phil Schmoyer and Mark Wuchte

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  • Artificial Intelligence
  • Bank Compliance
  • Community Banks
  • Credit Unions
  • Digital Solutions

Artificial intelligence (AI) has rapidly evolved from emerging technology to a strategic imperative for organizations across most industries. Many financial institution leaders are eager to adopt AI, but few have the organizational fluency, policies, procedures, data foundation and governance framework required to deploy it safely, responsibly and in a way that can drive real profitability. The challenge is no longer awareness, it’s execution. Successful AI programs are not built on hype alone. Instead, they are built on data readiness and discipline. Institutions must determine where to start, how to scale responsibly and how to balance innovation with the realities of operating in a highly regulated environment.

What is driving financial institutions to adopt AI?

Financial institutions are facing pressure from every direction, and several converging forces are accelerating AI adoption.

Customer expectations continue to rise, shaped by seamless, personalized digital experiences in other industries. At the same time, fraud risks are becoming more sophisticated as digital transactions increase. Regulatory complexity remains high, and competition from financial technology (fintech) and large technology players is intensifying pressure on margins and speed to market. Against this backdrop, AI offers a compelling opportunity. Institutions can leverage it to enhance decision-making, improve operational efficiency, strengthen fraud detection and deliver more personalized customer experiences.

Ultimately, the value of AI comes from both top-line and bottom-line impact. On one side, institutions can use AI to create new revenue opportunities and deliver more personalized products and services. On the other, they can also streamline operations, improve productivity and reduce the cost of serving customers. The biggest opportunity, however, comes from not simply layering AI on top of existing workflows, and instead redesigning operations around AI-enabled ways of working.

How does AI implementation typically unfold?

Most financial institutions are still in the early stages of AI adoption – testing use cases, running pilots and exploring potential applications. While this experimentation is necessary, it often falls short of delivering sustained impact. Leading institutions are approaching AI implementation as a structured progression:

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Jordan Anderson

Director

Baker Tilly Professional Phil Schmoyer

Phil Schmoyer

Principal

Baker Tilly Professional

Mark Wuchte

Principal

Related sections

  • Artificial Intelligence
  • Bank Compliance
  • Community Banks
  • Credit Unions
  • Digital Solutions
  • Digital Strategy
  • Financial Institutions
  • Financial Services
  • Fintech
  • Mortgage Center of Excellence
  • Risk Advisory
  • Strategy & Management Consulting
Digital Strategy
  • Financial Institutions
  • Financial Services
  • Fintech
  • Mortgage Center of Excellence
  • Risk Advisory
  • Strategy & Management Consulting
    1. Foundation and readiness: This stage is where institutions assess where they are today, identify gaps and define their goals. It includes evaluating data quality, infrastructure, talent, governance and change management capabilities. You cannot build AI capability on a cracked data foundation. This stage is about making bold bets on infrastructure before the glamorous use cases.
    2. Pilot and prove: At this point, institutions test targeted use cases through proofs of concept or minimum viable products. The goal is to prove value on a manageable scale, learn quickly from any missteps and refine the approach before committing to a broader rollout.
    3. Scale and integration: Here, promising pilots are moved into production and integrated into operational workflows. This stage requires stronger pipelines, clearer governance practices and better model monitoring to ensure performance remains consistent over time.
    4. Transformation: In this more advanced state, institutions are not using AI to improve existing work. They are building new products, delivering real-time decisions and embedding AI across both the customer experience and the employee experience.

    A common misstep is skipping the foundational stage. Institutions that move too quickly into pilots without addressing underlying gaps, particularly in data, often struggle to scale.

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    How important is data in determining the success of an AI program?

    Across every stage of AI maturity, one factor consistently determines success: data.

    AI systems are only as effective as the data that powers them. If data is siloed, inconsistent, inaccessible or poorly governed, the resulting models will be limited at best and risky at worst. For many institutions, the biggest obstacle is not a lack of AI ambition; it is fragmented data environments. Core systems, subledgers, document repositories and business applications often do not connect cleanly. That makes it difficult to build a reliable picture of the customer or generate meaningful insights.

    High-performing institutions treat data as a strategic asset rather than a byproduct of operations. That shift is crucial. They invest in:

    • Improving data quality and consistency
    • Centralizing and integrating key data sources
    • Establishing governance and traceability
    • Enabling broader, secure access to trusted data

    Better data enables stronger fraud detection, better anti-money laundering (AML) outcomes, more effective personalization and smarter product recommendations. It also supports the kind of natural-language access to information that many institutions now want from AI tools.

    What foundational capabilities are needed to support safe, transparent and scalable AI adoption?

    Many AI initiatives struggle to produce return on investment (ROI). Often, the problem is not the technology itself, but rather the absence of clear production criteria and value metrics. A pilot should not just be an experiment for experimentation’s sake. Institutions need to define in advance what success looks like, what performance thresholds must be met and how value will be measured. Whether the goal is reducing processing time or improving fraud detection accuracy, clear metrics are essential to guide decision-making and justify further investment.

    Beyond this, moving from pilot to production is not just a technical exercise. It requires institutions to think about how models will be monitored, retrained and governed over time. This is especially important because AI changes the environment around it. As employees and customers interact with AI-enabled systems, behaviors shift. That can change the underlying data and create drift in model performance. Institutions therefore need mechanisms for version control, observability and re-training. Leading institutions do not treat model deployment as the finish line. Instead, they treat it as the beginning of an ongoing feedback loop.

    Finally, even the best AI solution will fail to deliver value if employees do not trust it, understand it or use it consistently. That is why change management, AI literacy and internal support structures matter so much. It is important to think carefully about the human side of AI adoption, because people need training and teams need clarity on when and how to use AI. Leaders need to communicate why the technology matters and how it fits into the institution’s broader strategy.

    In many cases, that “last mile” of adoption is what determines whether an AI investment creates a real return.

    What are some practical frameworks for effective AI governance?

    As AI adoption expands, so too does the importance of governance. For financial institutions, innovation cannot come at the expense of risk management and regulatory compliance. It is important to note that many existing regulatory frameworks already apply to AI. If a model influences decision-making, it falls within established model risk management expectations, including documentation, independent validation and ongoing monitoring. Key considerations include:

    • Model governance: Ensuring transparency, validation and performance monitoring
    • Data privacy: Managing personally identifiable information through techniques such as de-identification or synthetic data
    • Third-party risk: Understanding and governing AI embedded within vendor solutions
    • Fair lending and bias: Ensuring decisions remain explainable and equitable

    The practical challenge is that governance often lags behind innovation. Many institutions have oversight structures built for a smaller or less complex operating model. As AI-use expands, that mismatch can become a source of examiner concern. Good governance begins with a simple but essential question: Do you know what AI is already being used within your institution? That includes not just internally developed models, but also AI embedded in vendor solutions and core platforms. Institutions should maintain a comprehensive inventory of AI use cases – both internally developed and vendor-provided – to support audit and regulatory readiness.

    Looking ahead: The AI-enabled financial institution

    As AI capabilities mature, the future of financial services will increasingly be defined by real-time, data-driven experiences. Institutions are already moving toward:

    • Faster credit and lending decisions
    • Personalized product recommendations delivered in real time
    • AI-augmented customer service and advisory models
    • Continuous feedback loops that refine models and improve outcomes

    While AI presents significant opportunities to streamline operations and unlock new revenue streams, successful implementation depends on a deep understanding of both the technology and its broader implications. Financial institutions must invest in robust training programs, establish clear ethical guidelines and build reliable data infrastructures to ensure that AI solutions are not only effective but also secure and compliant with regulatory requirements. Without these foundational elements, organizations risk falling short of their business objectives or encountering unforeseen challenges that could undermine trust and profitability.

    As this area continues to change, Baker Tilly’s digital advisory and risk advisory teams have AI resources that can help you navigate this landscape and support you from strategy development through implementation, including model design, data and AI governance, workflow automation and organizational readiness programs.

    Got questions? Schedule some time to chat with one of our specialists.

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    From AI curiosity to AI capability webinar

    Below you will find the presentation and recording from our webinar, From AI curiosity to AI capability: A data-driven path for financial institutions. For more information on the subject, and to learn more about how we can assist your organization with its AI journey, refer to our financial institutions and artificial intelligence webpages.

    For more information, contact our specialists