UPCOMING EVENTS
ALL EVENTS ARE IN EASTERN (EST) TIME ZONE.
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:
Muhammad Mohsin, Lead Product Manager, Treasury Solutions, VISA
Ruchi Gupta, Director, GRC, IT Services & Support, Business Resilience, AI Transformation, SPRING HEALTH
Sarvesh Gupta, Senior Principal Engineer & Data Architect, ORACLE
Moderator: Angela McKeirnan, Director, Global Product Lifecycle Management and Master Data, SOLENIS
As enterprises increasingly integrate AI-powered development tools into software engineering and DevOps workflows, the ability to generate, modify, and deploy code is undergoing a significant transformation. AI-assisted coding and automation are enabling development teams to accelerate code creation, improve individual productivity, and streamline certain aspects of the software development lifecycle. Yet as organizations produce code at unprecedented speeds, a critical question is emerging: Are AI-driven productivity gains actually accelerating enterprise software delivery, or are they creating new bottlenecks across testing, validation, security, and deployment?
For many enterprises, the growing volume of AI-generated code is placing additional pressure on existing DevOps processes, CI/CD pipelines, quality assurance practices, and software governance frameworks. While developers may be producing code faster, engineering and operations teams must still ensure that applications meet requirements for reliability, security, maintainability, and production readiness. At the same time, organizations face growing concerns about technical debt, code quality, fragmented development toolchains, and the operational risks associated with accelerating software delivery without sufficient oversight. As enterprises expand their use of AI across development and DevOps environments, technology leaders must become increasingly disciplined about balancing development speed with software quality, operational resilience, and measurable business outcomes.
Sandip Pandey, Lead Software Engineer (Android), Mobile Systems, Artificial Intelligence & Machine Learning, PAYPAL
Praveen Cherakkara, Technical Program & Engineering Leader, WHATABURGER
Rahul Chawla, Lead Business Intelligence Data Warehouse Engineer, GOOGLE
Nirmal Gupta, Chief Technology Officer, CRYPTIN
Moderator: Poornakala Sethuraman Ph.D., Artificial Intelligence Advisor & Officer, EMPRESS HEALTH AI & Former Vice President Data Science & Product Strategy, BANK OF AMERICA
