A Use-Case Driven Approach to AI

Presented by IBM

The adoption of artificial intelligence (AI) in federal agencies is often seen as a transformative leap forward, but Vanessa Hunt, General Manager of Technology for the U.S. Federal Market at IBM, emphasizes the importance of a measured, use-case-driven approach. In this method, agencies begin by identifying specific challenges or tasks where AI can make a tangible difference, rather than pursuing an "AI-first" strategy. This approach prioritizes practical, high-impact solutions that can be scaled organization-wide.

Hunt explains that IBM has seen considerable success with this method, particularly in application modernization, client experience, and digital labor. By focusing on targeted outcomes, IBM has enabled agencies to transform how they operate, ensuring that early wins with AI can be replicated across other areas. For example, IBM’s AI tools have been instrumental in automating repetitive administrative tasks, such as document processing and workflow management. This has freed up human resources for more complex, value-driven activities.

For the IRS, this approach is central to its modernization strategy. The agency has long relied on technology to manage its vast operations, but AI has opened new doors for innovation. By applying AI to clearly defined challenges—such as taxpayer service and compliance monitoring—the IRS can achieve measurable improvements that directly benefit the public. AI tools enable the agency to better allocate resources, streamline operations, and improve overall service delivery.

Hunt underscores the significance of starting small but thinking big. “A use-case-driven strategy allows agencies to focus on solving specific problems,” she notes. “When those solutions work, they can be scaled across the organization to drive transformation.” This approach ensures that agencies are not overwhelmed by the complexity of AI but instead build confidence and expertise over time.

The collaboration between the IRS and IBM highlights the power of this strategy. The IRS’s ongoing modernization efforts demonstrate how targeted AI applications can improve efficiency and effectiveness without unnecessary complexity. By addressing specific needs—such as improving data analytics for compliance or automating routine taxpayer interactions—the IRS ensures that its investments in AI yield tangible results.

This pragmatic approach contrasts sharply with the pitfalls of an AI-first mindset, where technology is applied broadly without clear objectives. Hunt warns that such strategies often result in wasted resources and limited impact. Instead, agencies should focus on areas where AI can deliver measurable value, such as modernizing procurement processes or enhancing taxpayer experiences.

The success of a use-case-driven strategy ultimately depends on collaboration and alignment. For the IRS, working with IBM has provided access to cutting-edge AI expertise and tools, enabling the agency to navigate its modernization journey with confidence. This partnership underscores the importance of leveraging private-sector innovation to tackle public-sector challenges.

As AI becomes an integral part of federal operations, the lessons from the IRS and IBM’s collaboration will serve as a roadmap for other agencies. By focusing on specific use cases, starting small, and scaling success, agencies can harness the power of AI to transform how they deliver services and achieve their missions.

This interview was part of the program AI for Agencies: Modernization and Taxpayer Service which explores the transformative role of artificial intelligence within federal agencies, with a focus on the IRS's efforts to modernize operations, enhance taxpayer experiences, and optimize workforce management. Hosted by Francis Rose and presented by IBM, the program features in-depth discussions with leaders from the IRS and IBM on AI’s strategic implementation, from use-case-driven approaches to its impact on application modernization and human resource management.