As enterprises race to operationalize AI, the foundation of success lies not in algorithms - but in architecture. Modern organizations are discovering that scalable, trustworthy AI outcomes depend on structured, governed, and high-quality data ecosystems. The Medallion Architecture has emerged as a blueprint for achieving that vision - transforming fragmented data lakes into layered, intelligent pipelines that enable analytics, automation, and AI at scale.

At its core, the Medallion model organizes data into Bronze, Silver, and Gold tiers - from raw ingestion to refined insights - ensuring that each stage enhances data integrity, lineage, and accessibility. This approach allows enterprises to create a unified, reusable data foundation that supports everything from machine learning to real-time decision intelligence. By embedding governance and quality directly into data architecture, organizations can accelerate AI readiness while maintaining compliance, agility, and trust.

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

• Architecting multi-layered data ecosystems that scale for AI and analytics
• Applying Medallion principles to ensure quality, governance, and traceability
• Evolving from data lakes to AI-ready platforms through modular design
• Leveraging the Medallion approach to drive business agility and insight velocity

Rajesh Vayyala, Principal Data Architect, MAJOR DEBT COLLECTION COMPANY

Marty Samadder, Head of Enterprise Architecture, NOVELIS

Quantum computing represents more than a new generation of hardware - it marks a fundamental redefinition of how intelligence is computed. As the world moves beyond binary logic into the probabilistic realm of quantum mechanics, enterprises are beginning to explore how quantum algorithms can complement classical systems to solve previously intractable problems. The shift is not theoretical - rapid advancements in qubits, error correction, and algorithmic design are bringing quantum advantage closer to commercial reality.

At its core, quantum computing expands the boundaries of problem-solving, enabling hybrid architectures that merge classical precision with quantum scale. This convergence is poised to reshape high-performance computing, optimization, and cryptography - driving breakthroughs in everything from financial modeling to AI acceleration. The enterprises preparing for this hybrid era are not just adopting a new technology; they’re redefining what computation itself means.

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

• Understanding the fundamentals of quantum mechanics as applied to computation
• Exploring hybrid models that integrate classical and quantum architectures
• Assessing the potential business use cases and limitations of quantum computing
• Preparing data systems and infrastructure for the coming era of quantum advantage

John Jiang, Ph.D., Former Chief Research & Solution Architect - Cloud Big Data Architect & Data Scientist, HITACHI

In the BFSI (banking, financial services & insurance) sector, Chat AI is evolving from a front-end customer interaction tool to a core component of enterprise automation. Today’s financial institutions are deploying intelligent conversational systems that not only communicate - but connect securely to back-end platforms to access customer data, execute transactions, and deliver real-time, personalized insights.

Achieving this end-to-end integration requires more than APIs and models - it demands a tightly governed data and automation architecture. BFSI organizations must ensure every interaction is accurate, explainable, and compliant with evolving regulations around privacy, security, and auditability. Chat AI systems must retrieve and act on sensitive data only within approved controls, maintaining full traceability across data lineage, identity management, and risk frameworks.

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

• Designing Chat AI architectures that integrate securely with core banking, CRM, and data platforms
• Ensuring data privacy, lineage tracking, and regulatory compliance across all conversational interactions
• Automating end-to-end workflows - from inquiry to resolution - without compromising oversight or accuracy
• Building governance models that balance innovation with transparency, auditability, and customer trust

Adam Smykowski, Director of Agentic AI & Automations Solutions, VALLEY BANK

Pradeep Madhavankutty, Director - Artificial Intelligence Delivery & Product Development, STANDARD CHARTERED BANK

Suzanne LaLena, Senior Vice President, Automation Engineering, BNY

Anup Gupta, Associate Director, LTIMINDTREE

Moderator: Gaurav Basra, Chief Executive Officer, BASRA CONSULTING SERVICES

In a world of AI-driven automation and cloud-native platforms, APIs are no longer just connectors, they are critical control points for trust, security, and performance. Whether you’re scaling intelligent automation or building decision systems, your API layer determines how far you can go.

Furthermore, behind every trusted financial interaction lies an invisible architecture of resilience - APIs that authenticate, encrypt, and orchestrate data at millisecond speed while navigating layers of regulation and risk. Designing these systems demands a delicate balance - ensuring uncompromising security without sacrificing speed, scalability, or user experience. Modern FinTech and enterprise architects are building “trust by design” into their API ecosystems - uniting engineering rigor, governance, and performance optimization to deliver secure, compliant, and future-ready financial experiences.

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

• Architecture patterns and security-first practices that enable both innovation and integrity
• Embed zero-trust security into API design
• Balance speed with compliance in FinTech microservices
• Avoid common performance bottlenecks in high-volume transactions
• Implement governance and observability for API reliability at scale

Sibasis Padhi, Staff Software Engineer, WALMART GLOBAL TECH

A recent MIT report reveals that 95% of Generative AI pilots fail to deliver measurable ROI, often because they are treated as isolated experiments rather than integrated into business workflows. The study identifies a "GenAI Divide" where success is found in specific back-office tasks, while flashy, customer-facing projects fail.

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

• The "Boring" ROI Paradox -  ~50% of budgets go to low-ROI, flashy pilots, while the 5% success stories are in "boring" back-office automation
• The "Learning Gap" - Internal builds fail twice as often as vendor-led solutions, despite vendors having less business knowledge
• The "Data Swamp" - Enterprise data is often too messy or siloed for effective use, leading to inaccurate AI outputs
• The "Compliance Cliff" - Pilots fail in production because organizations apply legacy governance instead of specialized AI Trust, Risk, and Security Management strategies
• Change Management - Without proper change management, employees abandon tools due to a "Trust Gap" or fear of replacement

Marty Samadder, Head of Enterprise Architecture, NOVELIS

Adam Smykowski, Director of Agentic AI & Automations Solutions, VALLEY BANK

Karthik Josyula, Head of Data & AI Platforms - Data & Analytics, KOHLER

Poornakala Sethuraman, Ph.D., Vice President, Data Science & Product Strategy, BANK OF AMERICA

Artificial intelligence dominates boardroom conversations, yet only a small number of organizations have translated that excitement into consistent, day-to-day ways of working.

In this solo session, the speaker shares patterns observed across enterprises that are successfully adopting AI at scale: where they begin, how they structure ownership and governance, and the common friction points that stall progress. Using clear, real-world stories instead of technical deep dives, the talk highlights three practical shifts leaders can make to move AI from a strategic slide deck into visible impact across customer experience, operations, and innovation. 

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

• The common adoption patterns of enterprises that move from AI experimentation to real, repeatable business value
• How leaders set up ownership, operating models, and ways of working that make AI usable across teams
• The biggest organizational and cultural friction points that slow AI down, and how successful teams navigate them
• Three practical leadership shifts that help AI show up in customer experience, operations, and innovation (not just strategy decks)

Nithin Mohan, Artificial Intelligence & Supercomputing Leader, HEWLETT PACKARD ENTERPRISE

Enterprises are entering a new era of intelligent systems - one where AI agents no longer just execute pre-defined tasks but dynamically make decisions, adapt to changing contexts, and collaborate across data and operational ecosystems. As these autonomous capabilities mature, the challenge shifts from experimentation to orchestration: how to design, scale, and govern Agentic AI within enterprise boundaries while maintaining control, compliance, and trust.

Building an orchestrated Agentic AI ecosystem requires more than technology - it demands architecture discipline, governance frameworks, and visibility across decision chains. Success hinges on integrating data pipelines, model management, and feedback loops that allow agents to act responsibly and learn continuously without introducing systemic risk. Balancing autonomy with accountability becomes the defining leadership challenge of AI at scale.

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

• Enterprise-Scale Orchestration - How to design and manage agentic AI ecosystems that seamlessly integrate multi-agent systems with enterprise workflows, ensuring scalability and performance
• Governance & Compliance Frameworks - Building resilient guardrails, trust mechanisms, and audit-ready governance models to align with regulatory and ethical requirements
• From Experimentation to Impact - Shifting from pilot projects to production-grade adoption, driving measurable business outcomes and cultural readiness

Rajesh Sura, Head of Data Engineering & Analytics - North America Stores, AMAZON

This event is an "Open Network" Event & Can Be Attended by:

• IA FORUM Members
• IA FORUM Members’ “qualifying” NON-Member internal/external colleagues and friends - to be their “NON-Member VIP Guests”
• “Qualifying” senior-level executives that are NOT Members - and the IA FORUM has personally invited to attend an event
• Other “qualifying” senior-level executives that are NOT Members - and have learned about the events via word of mouth or LinkedIn
• Vendors can NOT attend IA FORUM events

Behind every successful AI and automation initiative lies a powerful - yet often invisible - data engineering foundation. For global enterprises, building this foundation means ensuring that data is accurate, accessible, secure, and architected for scalability across complex ecosystems.

Today’s data engineering leaders are enabling the enterprise of the future: one where data pipelines fuel real-time insights, AI models operate on trusted data, and governance is embedded into every layer of the data lifecycle. They’re tackling the hard questions - how to unify legacy and cloud environments, manage data quality at scale, optimize cost and performance, and ensure that the data powering AI is reliable and compliant.

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

• Building scalable data pipelines that enable AI, analytics, and automation across the enterprise
• Ensuring data quality, integrity, and governance in hybrid and multi-cloud environments
• Balancing agility, performance, and cost efficiency in modern data platforms
• Architecting trusted data foundations for enterprise-grade AI and decision intelligence

James Gowen, Ph.D., Global Head of Data Engineering Controls - Data Quality Platforms, CITI

Karthik Josyula, Head of Data & AI Platforms - Data & Analytics, KOHLER

Rahul Chawla, Lead Business Intelligence Data Warehouse Engineer, GOOGLE

Prasad Maderamitla, Principal Engineer, SALESFORCE

Enterprise IT today is the cornerstone of organizational growth, agility, and competitive advantage. As digital ecosystems expand, technology leaders are ensuring that IT strategies directly enable business transformation - driving smarter operations, enhanced customer experiences, and continuous innovation.

Forward-thinking IT executives are leading modernization initiatives that align infrastructure, data, and intelligent automation with enterprise objectives. They’re building adaptive operating models, strengthening resilience, and ensuring technology investments deliver measurable business impact. IT is no longer simply supporting the business - it is powering it.

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

• Redefining IT’s identity from service provider to business growth enabler
• Building the business case for modernization, agility, and intelligent investment
• Aligning technology strategy with C-suite priorities and measurable ROI
• Developing IT leadership and culture that foster innovation and resilience

Avinash Vaidya, Vice President of Information Technology, FRANKLIN TEMPLETON

Jerry Luftman, Ph.D., Founder, Professor & Managing Director, GLOBAL INSTITUTE OF IT MANAGEMENT & Former Chief Information Officer, IBM

Shailesh Kadam, Artificial Intelligence & Cloud Architect, SAKS GLOBAL

Ram Kumar Nimmakayala, AI & Data Strategist - Product Leader, AI, Machine Learning & Data, WESTERN GOVERNORS UNIVERSITY

Moderator/Speaker: Rahul Bhatia, Co-Founder & Vice President of Cloud Solutions, NUAAV

Join your peers for a virtual networking and social mixer power hour.

Moderator: Jules Miller, IA Insights & Community Liaison Officer, IA FORUM

Suzanne LaLena, Senior Vice President, Automation Engineering, BNY 

Poornakala Sethuraman, Ph.D., Vice President, Data Science & Product Strategy, BANK OF AMERICA 

Sindhu Vasudevan, Director of Software Engineering, TRUIST 

This event is an "Open Network" Event & Can Be Attended by:

• IA FORUM Members
• IA FORUM Members’ “qualifying” NON-Member internal/external colleagues and friends - to be their “NON-Member VIP Guests”
• “Qualifying” senior-level executives that are NOT Members - and the IA FORUM has personally invited to attend an event
• Other “qualifying” senior-level executives that are NOT Members - and have learned about the events via word of mouth or LinkedIn
• Vendors can NOT attend IA FORUM events

AI is fundamentally reshaping retail, moving beyond automation to create personalized and highly efficient customer journeys. From predictive merchandising and omni-channel engagement to real-time behavioral insights, AI is redefining how retailers understand, anticipate, and serve customers. Leading brands are integrating intelligent systems into every touchpoint - optimizing operations, deepening customer relationships, and unlocking new revenue streams. As retailers accelerate their digital transformation, AI is becoming the core enabler of loyalty, differentiation, and growth in an increasingly competitive market.

Attend this event to learn how retailers are leveraging AI to boost customer loyalty, satisfaction, and growth - and how AI can drive superior retail customer experiences.

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

• Hyper-Personalization - delivering tailored product recommendations and pricing in real-time
• Intelligent Operations - using predictive analytics and smart inventory to ensure product availability
• Future of Interaction - the role of AI-driven chatbots and in-store tools for instantaneous, 24/7 support

Pabitra Saikia, Vice President & Senior Change Delivery Lead, TRUIST

Uttam Kumar, Engineering Manager, AMERICAN EAGLE OUTFITTERS

Voice AI has evolved from a novelty interface to a strategic channel reshaping how enterprises connect with customers, employees, and partners. But as adoption accelerates, the true differentiator lies not in the technology itself - but in how organizations design, govern, and measure it.

The challenge is ensuring that Voice AI delivers consistent, human-centered experiences while achieving measurable business outcomes. That means architecting intelligent conversational systems that enhance CX, integrate seamlessly with enterprise data, and continually learn from every interaction. It also means defining clear success metrics - from customer sentiment and efficiency gains to ROI and long-term brand impact - and building governance frameworks to monitor and optimize performance at scale.

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

• Designing Voice AI experiences that strengthen customer engagement and satisfaction
• Defining KPIs and frameworks to measure ROI, adoption, and CX impact
• Integrating Voice AI with broader data, analytics, and automation strategies
• Governing and optimizing Voice AI initiatives for scalability, trust, and continuous improvement

SriHarsha Anand Pushkala, Director, Fraud Strategy & Analytics, ATLANTICUS

Paras Doshi, Director, Head of Data - Data Science & Data Engineering, OPENDOOR

Raghu Para, Artificial Intelligence Architect & Technology Leader, Data Engineer & Scientist, Multi-Platform Engineering, FORD MOTOR COMPANY

Moderator: Rahul Bhatia, Co-Founder & Vice President of Cloud Solutions, NUAAV

Join your fellow IA FORUM Advisory Board peers for a light-hearted and engaging networking and social mixer.

Moderator: Jules Miller, IA Insights & Community Liaison Officer, IA FORUM

As finance teams face increasing pressure to modernize, automate, and accelerate outcomes, intelligent automation (IA) leaders are often called upon to guide tool selection, architecture decisions, and implementation strategies - while balancing risk, control, and measurable value. At the same time, they are increasingly expected to serve as strategic advisors, helping finance leaders make informed technology decisions that align with real business needs rather than hype-driven solutions.

As a result, IA leaders must help finance stakeholders navigate an increasingly crowded automation landscape - moving beyond the assumption that AI is the correct tool for every use case - and instead select fit-for-purpose tools aligned to specific finance workflows, maturity levels, and business objectives. Doing so requires strong internal client partnerships, clear decision frameworks, and the ability to translate technical possibilities into practical, finance-relevant outcomes.

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

• Partnering with finance leaders to identify, prioritize, and validate high-value automation use cases
• Selecting fit-for-purpose IA tools for finance workflows
• Educating internal finance stakeholders on automation options beyond AI and managing the AI “shiny object” mentality
• Establishing governance, decision frameworks, and operating models to scale finance automation responsibly

Jay Dhar, Vice President - Agentic AI & Process Transformation, U.S. BANK

Gnana Silaparasetti, Principal Technical Architect, SALESFORCE

Poornakala Sethuraman, Ph.D., Vice President, Data Science & Product Strategy, BANK OF AMERICA

Nikhil Kassetty, Software Architect - Artificial Intelligence, INTUIT

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

In the race to accelerate AI development, organizations face an increasingly complex web of governance, risk, and technology decisions - each influencing the others in unseen ways. Too often these disciplines are managed as independent silos, yet their true impact emerges only when they converge. The challenge is understanding how interdependencies among AI governance models, regulatory risk, and rapid technological innovation can create ripple effects that shape enterprise resilience, public trust, and even the future trajectory of industries.

Much like the concept of quantum entanglement - where the state of one entity instantly affects another - governance, risk, and technology have become inextricably linked in the AI era. Decisions made in one domain can amplify vulnerabilities or accelerate breakthroughs across all others. Understanding these entanglements is now essential for leaders tasked with ensuring responsible AI growth, balancing innovation with accountability, and guiding enterprises toward sustainable, transparent outcomes.

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

• Understanding quantum entanglement as a lens for interdependent AI governance, risk, and technology systems
• Evaluating whether entanglement across these domains is beneficial, inevitable, or destabilizing
• Identifying enterprise controls and frameworks to guide or prevent systemic risk
• Examining how strategic decisions today influence long-term ROI, market trust, and organizational survival

Duffie Brunson, Research Sabbatical & Director, Enterprise Data Oversight, FANNIE MAE