Over the past several years, Generative AI has rapidly moved to the center of enterprise AI strategies, investment priorities, and transformation initiatives. Organizations across industries are racing to identify GenAI use cases, deploy enterprise platforms, and integrate generative capabilities across business functions. Yet as adoption accelerates, a critical question is emerging: is Generative AI always the right solution for the business problem being addressed – or are organizations overlooking more targeted, purpose-built AI solutions that may deliver greater accuracy, control, efficiency, and measurable business value?
For many enterprise use cases, point-solution AI - designed to address a specific business process, operational challenge, or defined outcome - may provide advantages over broader Generative AI approaches. At the same time, GenAI offers unprecedented flexibility, scalability, and opportunities to transform how employees work, customers engage, and organizations create value. As enterprises move beyond experimentation and AI hype toward scaled implementation and measurable ROI, leaders must become increasingly disciplined about determining which AI approach best aligns with the problem, risk profile, data environment, operating model, and desired business outcome.
Topics of discussion will include, but not be limited to:
- How enterprises are determining when Generative AI, point-solution AI, traditional AI/ML, or a combination of approaches provides the strongest fit for a specific business challenge and desired outcome
- Where purpose-built AI solutions may outperform broader GenAI approaches in areas including accuracy, reliability, governance, security, cost, and operational effectiveness
- Whether the rapid enterprise push toward GenAI is causing organizations to prioritize technology trends over clearly defined business problems, measurable value, and ROI
- How business and technology leaders can establish more disciplined frameworks for evaluating AI use cases, selecting the appropriate technologies, and measuring whether AI investments are delivering meaningful business outcomes
Muhammad Mohsin, Lead Product Manager, Treasury Solutions, VISA
Sarvesh Gupta, Senior Principal Engineer & Data Architect, ORACLE
Praveen Cherakkara, Technical Program & Engineering Leader, WHATABURGER
Ruchi Gupta, Director, GRC, IT Services & Support, Business Resilience, AI Transformation, SPRING HEALTH
Moderator: Angela McKeirnan, Director, Global Product Lifecycle Management and Master Data, SOLENIS
