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

Enterprise technology leaders today are navigating an era of constant disruption - economic volatility, rapid technological change, evolving workplace expectations, ongoing organizational transformation, and re-orgs. Amid this uncertainty, the challenge is not just operational continuity, but maintaining engagement, motivation, and performance within teams that are being asked to do more with less.

In today’s world, you must inspire with clarity, communicate with intent, and sustain trust even amid uncertainty. You must connect purpose to performance - creating an environment where your teams remain agile, accountable, and motivated through constant change. Lead with empathy and conviction, and you’ll foster a culture of resilience and engagement that keeps people aligned and productive, no matter how unpredictable the landscape becomes.

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

• Reinforcing alignment, accountability, and transparency across distributed and hybrid teams
• Leading with authenticity and empathy to sustain motivation during change
• Balancing productivity expectations with employee well-being and burnout prevention
• Cultivating a culture of adaptability, resilience, and trust amid ongoing transformation

Janet Norton, Senior Manager, Data Engineering & IT Applications, OCTAPHARMA

Raj Mehta, Technology Leader & Structural Engineer, GLOBAL ENGINEERING FIRM

Bobbie Long, Senior Vice President, Global Engineering Lead, Digital Automation & Infrastructure Services, Quality, CITI

Sampath Madanu, Statistical Programming Associate Director, Programming Portfolio Lead, ASTRAZENECA

Moderator/Speaker: Angela McKeirnan, Director, Global Product Lifecycle Management & Master Data, SOLENIS

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

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

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

Shankar Krishnan, Product Management Leader - Bedrock GenAI Services, AMAZON WEB SERVICES

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

Enterprises are reimagining how knowledge is created, accessed, and shared - and Generative AI is becoming the new catalyst. Beyond chat bots and co-pilots, forward-thinking organizations are embedding large language models into their knowledge ecosystems to surface insights, accelerate onboarding, and connect institutional intelligence across silos.

For leaders, this innovation brings both promise and complexity. How do you build scalable knowledge architectures that combine structured and unstructured data sources? How do you ensure model accuracy, privacy, and compliance while maintaining contextual relevance? And how do you measure the productivity and decision-making gains that justify enterprise-wide investment?

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

• Architecting enterprise knowledge systems that integrate LLMs with existing data and content management platforms
• Ensuring accuracy, governance, and ethical guardrails in GenAI-generated insights
• Measuring the impact of GenAI on knowledge accessibility, productivity, and decision velocity
• Building feedback loops that continuously refine and contextualize enterprise knowledge sharing

Claire Grosjean, Senior Director, Business Process Management, TECHNOLOGY CREDIT UNION

Velan Thayumanavan, Principal Solution Architect, AT&T

Nikhil Kassetty, Software Engineer, AI & Technology, INTUIT

Aarohi Tripathi, Senior Data Engineer, MAJOR HEALTHCARE COMPANY

Moderator/Speaker: Uma Sankar Kopalle, FinTech Advisor, Innovator & Artificial Intelligence Evangelist

Every intelligent automation leader eventually reaches the same crossroads: a business unit demands a new capability - faster invoice processing, predictive customer insights, automated exception handling - and suddenly, the question arises: which intelligent automation tool is the right fit for their business case?

For one organization, expanding beyond RPA into AI-powered document processing can unlock new efficiencies but also expose hidden integration and governance challenges. For another, investing in a single enterprise automation platform can simplify oversight but limit flexibility for domain-specific innovation. Across industries, the story remains consistent: choosing the “right” intelligent automation tool isn’t just about technology - it’s about timing, feasibility, and the maturity of both people and process.

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

• Translating business requirements into technical evaluation criteria
• Assessing feasibility in terms of budget, manpower, integration complexity, and data readiness
• Balancing enterprise standardization with domain-specific flexibility
• Avoiding automation tool redundancy and ensuring interoperability across the automation stack
• Building governance models that enable sustainable value realization

Gladson Baby, Vice President & Director of AI Enablement, Intelligent Automation & System Integration, FIFTH THIRD BANK

Henry Lyles, Director, Global Technology & Intelligent Automation Solutions CoE, MCDONALD’S

Puneet Thakkar, Finance Process & Systems Transformation Lead, GOOGLE

Jimit Sukkawala, Vice President, Intelligent Automation & Business Solution Architect, LPL FINANCIAL

Moderator/Speaker: Curt Burghart, Vice President, People Delivery, UNUM

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

Sumit Abhichandani, Global Director of Quality Assurance, VISA

Moderator/Speaker: Gaurav Sharma, Associate Director, Quality Engineering, LTIMINDTREE

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

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

Suzanne LaLena, Senior Vice President, Automation Engineering, BNY 

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

Sindhu Vasudevan, Director of Software Engineering, TRUIST 

“Open Network” Event…

• This event is open to IA FORUM members’ NON-member colleagues, to be their event VIP Guests.
• It’s also open to other NON-members, that have either be invited to attend the event, and/or qualify to attend the event.
• Vendor can NOT attend this event.

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

Srinivas Akella, Head of Contact Center Technology & Director of Engineering & Gen AI & Agentic AI Enablement, U.S. BANK

Nitin Sharma, Senior Manager, Digital Product Solution Design, AMERICAN EXPRESS

Mostafa Rafaie, Director, Data Science - Aligned Assurance, MUTUAL OF OMAHA

Moderator/Speaker: Suzanne LaLena, Senior Vice President, Automation Engineering, BNY

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

“Open Network” Event…

• This event is open to IA FORUM members’ NON-member colleagues - to be their event VIP Guests.
• It’s also open to other NON-members, that have either be invited to attend the event, and/or qualify to attend the event.
• Vendor can NOT attend this event.

As artificial intelligence reshapes the digital enterprise, the foundation for trustworthy, scalable, and explainable AI begins not with models, but with data. Building Data as a Product (DaaP) in the age of AI represents a paradigm shift from treating data as an exhaust of systems to managing it as a discoverable, governed, and value-driven product. This talk explores how organizations can architect data platforms around product principles like ownership, usability, quality, and lifecycle management to accelerate AI adoption responsibly and efficiently.

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

• Evolution of Data and mental models and building the mental model for Data as Product
• Overview of six pillars of data as product
• Demo of Unity Catalog, vector search, fine grain access control and AI based SQL Functions

Junaith Haja, Senior Data Engineer, AMAZON

Graph databases are redefining how enterprises understand and manage relationships within their customer ecosystems. What was once siloed data across CRM, ERP, and IoT systems is now being connected into dynamic relationship graphs that reveal patterns, dependencies, and opportunities traditional databases can’t uncover.

As organizations strive to create a single source of truth for customer data, the challenge lies not just in integrating systems like Salesforce and Oracle - but in understanding how customers, locations, contracts, and transactions interconnect. The true differentiator lies in how enterprises architect, govern, and operationalize graph-based intelligence to improve data accuracy, decision-making, and customer experiences. Building an intelligent, connected customer master requires a balance of data engineering precision and business context awareness - designing models that can detect duplicates, correct address mismatches, and surface cross-sell opportunities while ensuring scalability, data quality, and trust.

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

• Designing graph-based data models that unify customer entities across systems
• Detecting duplicates, incomplete records, and mismatched addresses using graph analytics
• Integrating graph insights with AI, data quality, and CRM governance frameworks
• Measuring business impact through improved accuracy, customer satisfaction, and operational efficiency
• Scaling graph architectures for enterprise adoption, data trust, and continuous improvement

Phani Chilakapati, Global Data Architecture Leader, Digital Transformation, GenAI & Enterprise Analytics, INDUSTRIAL SCIENTIFIC

Join your peers for a virtual holiday networking and social mixer.

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

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 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

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

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

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

As enterprise intelligent automation strategies mature, the focus has shifted from isolated tools to connected ecosystems. Organizations are now seeking integrated platforms that unite RPA, artificial intelligence, machine learning, process mining, orchestration, low-code development, and data intelligence into a single operational framework capable of scaling across functions.

This evolution raises critical questions for intelligent automation leaders: How can we ensure our tools scale end-to-end? How do we align architecture with enterprise data strategy and minimize tool sprawl? And how do we select and integrate the technologies that best support long-term intelligent automation growth, governance, and ROI?

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

• Defining a target enterprise architecture that enables end-to-end automation
• Evaluating platform consolidation vs. function-specific solutions
• Ensuring governance, data integration, and scalability in a converged model
• Aligning tool selection with business value, ROI, and long-term sustainability

Puneet Thakkar, Finance Process & Systems Transformation Lead, GOOGLE

Sasan Sadr, Former Director, Enterprise Continuous Improvement, ULTA BEAUTY

Bhawini Navapura, Senior Vice President - DevOps Environment Service Management, CITI

Deji Adedayo, Intelligent Automation & Product Owner - North America, GORDON FOOD SERVICE

Moderator/Speaker: Doug Shannon, Global Intelligent Automation & GenAI Thought Leader & Global Intelligent Automation Manager, GLOBAL PHARMACEUTICAL COMPANY

Abstract soon to come!

Kunal Tanwar, Ph.D., Director of Artificial Intelligence, Business Intelligence & Data Management, GOOGLE

“Open Network” Event…

• This event is open to IA FORUM members’ NON-member colleagues, to be their event VIP Guests.
• It’s also open to other NON-members, that have either be invited to attend the event, and/or qualify to attend the event.
• Vendor can NOT attend this event.

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