PANEL DISCUSSION: POINT-SOLUTION AI VS. GENERATIVE AI - IS GENERATIVE AI ALWAYS THE RIGHT ANSWER? (Friday, November 20, 2026)

Fri, 20 Nov 2026 18:00:00 GMT

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

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

PANEL DISCUSSION: AI-DRIVEN DEVELOPMENT & DEV-OPS: IS FASTER CODE CREATION CREATING NEW BOTTLENECKS IN SOFTWARE DELIVERY? (Saturday, November 21, 2026)

Sat, 21 Nov 2026 18:00:00 GMT

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.

Topics of discussion will include, but not be limited to:

How enterprises are evaluating whether AI-assisted development is delivering measurable improvements in end-to-end software delivery performance versus simply accelerating code creation
Strategies for addressing emerging bottlenecks across code reviews, automated testing, quality assurance, security validation, and CI/CD pipelines as AI-generated code volumes increase
How DevOps and platform engineering teams are adapting automation, governance frameworks, and deployment practices to manage AI-generated code while minimizing technical debt, security vulnerabilities, and operational risks
How engineering and technology leaders can balance development velocity, software quality, production reliability, and operational efficiency while establishing meaningful metrics for evaluating AI-driven DevOps investments

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

IA FORUM upcoming events

PANEL DISCUSSION: POINT-SOLUTION AI VS. GENERATIVE AI - IS GENERATIVE AI ALWAYS THE RIGHT ANSWER? (Friday, November 20, 2026)

PANEL DISCUSSION: AI-DRIVEN DEVELOPMENT & DEV-OPS: IS FASTER CODE CREATION CREATING NEW BOTTLENECKS IN SOFTWARE DELIVERY? (Saturday, November 21, 2026)