PART 2 - PANEL DISCUSSION: POINT-SOLUTION AI VS. GENERATIVE AI - IS GENAI ALWAYS THE RIGHT ANSWER? (Saturday, October 24, 2026)

Sat, 24 Oct 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

Sarvesh Gupta, Senior Principal Engineer & Data Architect, ORACLE

Praveen Cherakkara, Technical Program & Engineering Leader, WHATABURGER

Moderator: Angela McKeirnan, Director, Global Product Lifecycle Management and Master Data, SOLENIS

PANEL DISCUSSION: BUILDING & MANAGING AI MODELS TO MONITOR, TEST, EVALUATE & GOVERN ENTERPRISE AI SYSTEMS (Friday, October 30, 2026)

Fri, 30 Oct 2026 18:00:00 GMT

As enterprises expand their use of AI models, applications, agents, and autonomous workflows, organizations are increasingly leveraging AI models to monitor, test, evaluate, and govern other AI systems. These supervisory AI models can automate quality assurance, detect errors and hallucinations, validate outputs, monitor performance, identify risks, and enforce governance policies.

However, building, deploying, and managing AI models responsible for overseeing other AI systems introduces significant architectural, engineering, operational, and business challenges. How can enterprises ensure these supervisory models are accurate, reliable, scalable, and cost-effective while maintaining appropriate human oversight and accountability?

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

Architecting and integrating supervisory AI models into enterprise AI environments
Building, training, testing, and managing AI models that oversee other AI systems
Automating quality assurance, output validation, error detection, and performance testing
Continuously monitoring AI applications, agents, and workflows for anomalies, model drift, and operational risks
Establishing AI-driven governance, security, compliance, and human oversight mechanisms

Michael Arulfo, Chief AI Officer, CADRE

Marty Samadder, Head of Global Enterprise Architecture, NOVELIS

Laxmikant Pukale, Director of Advanced Capabilities - AI & Intelligent Automation, USAA

Moderator: SriHarsha Pushkala, Director, Fraud Strategy & Analytics, ATLANTICUS

IA FORUM upcoming events

PART 2 - PANEL DISCUSSION: POINT-SOLUTION AI VS. GENERATIVE AI - IS GENAI ALWAYS THE RIGHT ANSWER? (Saturday, October 24, 2026)

PANEL DISCUSSION: BUILDING & MANAGING AI MODELS TO MONITOR, TEST, EVALUATE & GOVERN ENTERPRISE AI SYSTEMS (Friday, October 30, 2026)