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This is a summary of an episode of Pioneers, an educational podcast on AI led by our founder. Join 3,000+ business leaders and AI enthusiasts and be the first to know when new episodes go live. Subscribe to our newsletter here.
TL;DR:
- Companies are prioritizing interna productivity-based AI use cases while navigating regulatory and operational challenges.
- Early adopters of customer-facing AI are poised to gain a competitive edge as expectations for seamless, AI-driven experiences grow.
- AI adoption struggles often stem from a lack of coalition-building between IT and business leaders, leading to poor alignment on goals and capabilities.
- Effective AI initiatives depend on robust data governance and architecture, as personalized experiences and advanced use cases require high-quality data.
- AI in banking and insurance is making strides in fraud detection, environmental scanning, and co-pilot solutions to enhance human-centric tasks.
Before we dive into the key takeaways from this episode, be sure to catch the full episode here:
Meet Tonjia - SVP and CSO at Associated Bank
Tonjia Coverdale, Senior Vice President and Chief Strategy Officer at Associated Bank, combines deep technical expertise with strategic leadership to drive innovation in banking operations and technology.
With a career spanning nearly three decades, Tonjia has held roles ranging from developer to CIO and CEO, working across industries, company sizes, and geographies. She has also contributed significantly to academia, holding leadership positions at universities.
At Associated Bank, she collaborates with technology leaders to integrate cutting-edge solutions like generative AI, aiming to balance efficiency with customer-centricity.
Tonjia’s vision centers on leveraging technology to empower teams, optimize processes, and create meaningful value in regulated environments.
The Growing Excitement Around Generative AI in Banking
The rapid emergence of generative AI is sparking unprecedented enthusiasm within the banking sector.
Tonjia highlights the transformative potential of AI to improve workflows, interactions, and production processes. However, despite the widespread excitement, many organizations remain unsure of where to begin and how to effectively deploy these technologies.
This hesitation stems from regulatory ambiguity and the challenge of identifying optimal use cases.
Early adopters in banking are experimenting with co-pilot and Agentic AI applications, prioritizing low-risk internal use cases while cautiously exploring customer-facing solutions.
Tonjia emphasizes that this excitement is just the beginning, with the full potential of AI yet to be unlocked as regulators gain clarity and businesses grow comfortable integrating these technologies into core operations.
Navigating the Challenges of AI Adoption in Regulated Industries
AI adoption in banking is complicated by stringent regulations and the need for robust risk management. Tonjia discusses how financial institutions are approaching AI cautiously, balancing innovation with compliance.
Many organizations focus on internal efficiency solutions to reduce risks associated with customer-facing applications. However, she notes that the foundational data and infrastructure needed for scalable AI solutions are often underdeveloped.
Additionally, regulatory bodies remain uncertain about how AI should be governed, leaving businesses hesitant to take bold steps.
Tonjia emphasizes the importance of collaboration among stakeholders, including IT, risk management, and business leaders, to navigate these challenges and establish sustainable AI initiatives that align with both organizational and regulatory requirements.
Why Internal Efficiency Isn’t Enough: The Push Toward Customer-Centric AI
Tonjia argues that while internal productivity gains are an important starting point for AI adoption, they alone are not enough to deliver meaningful ROI.
If you’d like to calculate AI ROI for your organization, try our free AI ROI calculator.
She envisions a future where AI fundamentally enhances customer experiences by enabling faster decision-making, improving service quality, and creating personalized interactions. She warns against companies becoming complacent with AI’s cost-cutting capabilities, as long-term value lies in revenue enablement and customer-centric innovations.
By leveraging AI for customer-facing applications, businesses can not only differentiate themselves in competitive markets but also meet rising consumer expectations for seamless, AI-powered experiences.
However, Tonjia stresses that success in this area requires organizations to move beyond experimental stages and invest in scalable, value-driven use cases.
Building Effective Coalitions: Aligning IT and Business Goals
Effective AI adoption requires strong collaboration between IT and business teams, Tonjia explains.
“Collaboration between IT and business is non-negotiable for AI success.” — Ankur Patel
She highlights the risks of unilateral decision-making, such as IT-driven projects that lack clear business use cases or business-led initiatives that fail due to inadequate technical support.
To bridge this gap, Tonjia advocates for creating coalitions that include representatives from IT, business, and even end-users to ensure alignment on objectives and capabilities.
This collaborative approach not only improves adoption rates but also enhances the operational value of AI solutions.
For long-term success, she suggests organizations establish standing committees to guide strategic initiatives and involve domain experts in use case development to foster inclusivity and drive sustainable impact.
Data as the Foundation: Overcoming Barriers to AI-Powered Personalization
Tonjia underscores that the greatest hurdle to achieving personalization at scale is fragmented and underutilized data architecture.
She explains that while organizations have amassed vast quantities of data, many lack the governance, integration, and quality standards needed to unlock AI’s potential.
Investing in data infrastructure is critical, as AI’s capabilities depend on free-flowing, high-quality data. She reflects on past investments in big data, which often delivered marginal benefits, and argues that the current AI era requires a renewed focus on data management.
Tonjia believes that companies that align their data strategies with AI initiatives will unlock groundbreaking personalization opportunities, paving the way for truly transformative customer experiences.
Emerging AI Use Cases in Banking and Insurance
AI applications in banking and insurance are evolving rapidly, with notable progress in areas like fraud detection, environmental scanning, and co-pilot solutions.
Tonjia highlights the growing adoption of front-office AI tools that assist with tasks such as market research and claims aggregation. However, back-office workflows remain a significant untapped opportunity, often hindered by complex processes and siloed data systems.
“Data and AI are inseparable; great data drives great AI outcomes.” — Tonjia Coverdale
Despite these challenges, AI is increasingly being used to enhance customer-facing interactions by synthesizing data and enabling more human-centric experiences.
For instance, AI-driven fraud detection in middle-office operations is becoming more sophisticated, allowing quicker identification of potential threats.
Tonjia sees these emerging use cases as the first step toward more advanced and impactful AI deployments across the industry.
Tonjia’s Vision for AI: From ROI to Transformative Impact
Tonjia envisions a future where AI drives transformative change rather than merely incremental improvements.
She advocates for redefining ROI metrics to include customer satisfaction, faster decision-making, and innovative value creation, beyond traditional cost-saving measures. She stresses that AI adoption should prioritize solving real business problems and delivering meaningful outcomes for both organizations and their customers.
Tonjia also emphasizes the importance of targeted AI solutions tailored to specific industries or functions, such as fraud detection or data classification.
These "point solutions," she argues, will serve as building blocks for broader adoption and help overcome regulatory challenges.
By focusing on these strategic areas, Tonjia believes organizations can harness AI to achieve both top-line growth and long-term competitive advantage.
