Generative AI Week 2025 Day Two Conference: Wednesday, November 12 2025

Generative AI holds immense potential to revolutionise sectors like banking, healthcare, and government—but innovation in these fields must operate within tight regulatory and compliance boundaries. This panel brings together leaders from regulated industries to explore how they are unlocking AI-driven transformation without compromising on ethics, safety, or governance.

• Balancing agility and control: Driving Gen-AI experimentation while maintaining regulatory compliance
• Rethinking guardrails, tooling, and internal policies enable safe AI deployment at scale
• Identifying high-value, low-risk entry points for Gen-AI in compliance-heavy environments
• Making infrastructure and tooling decisions that meet both innovation and compliance needs

Gregory Johnson

Vice President of Product Management - AI & Machine Learning
JPMorgan Chase & Co.

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Pratik Gautam

Vice President and Lead AI/GenAI Product Manager
Citi

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Kausar Riaz Ahmed

Director, Generative AI Medical Engagement Lead
Pfizer

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Nisheeth Chaudhary

Vice President Emerging Technologies and AI Governance
State Street

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Abby Knowles

Former Vice President, Global Consumer Technology Planning, Engineering & Transf
Independent

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Generative AI is rapidly reshaping the global security landscape, carrying both extraordinary potential and significant risk. From geopolitical competition to information warfare, the technology’s impact on national security extends far beyond borders. As governments and enterprises accelerate adoption, the international community faces urgent questions: how do we build cooperative policies, prevent misuse, and ensure readiness for the crises that may emerge?

• Assessing the national security risks and opportunities Gen AI introduces.
• Frameworks for building international alignment and AI policy collaboration.
• The case for a global entity to oversee standards, safety, and accountability.
• Anticipating how Gen AI may reshape geopolitics, defense, and critical infrastructure.
• Proactive steps to prevent and mitigate AI-driven crises before they materialize.

Colonel Gabe Arrington

Chief, International Affairs
US Air Force

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Building AI-ready data products requires a well-architected approach to data collection, processing, and governance to ensure quality, accessibility, and scalability. This session will explore how enterprises can design and deploy data products that seamlessly support AI workloads, enabling reliable insights and automation at scale.

• Designing AI-ready data architectures for performance, flexibility, and governance.
• Implementing real-time and batch data pipelines to support diverse AI workloads.
• Leveraging data mesh and data fabric principles for scalable AI integration.
• Ensuring data quality, lineage, and compliance for trustworthy AI outputs.
• Assessing readiness for Data Products from semi-automated products to autonomous agents

Darshan Mahendrakar

Product Director - Enterprise Gen AI Platform
GSK

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Nasir Zaidi

Former Global Director, Data, Analytics & AI COE
LyondellBasell

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Jacqueline Karlin

Former Head of AI Product
PayPal

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Sundar Krishnan

Director of AI/ML
Optimum

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Generative AI promises to transform marketing, but moving from scattered pilots to a scalable, business-wide strategy is anything but straightforward. This session unpacks how TradeStation has approached Gen AI adoption holistically - balancing ambition with operational reality.

• Mapping high-impact use cases across performance marketing strategy.
• Navigating the build-vs-buy dilemma to harmonise AI tooling.
• Reconciling department-level priorities with company-wide objectives.
• Lessons learned from both successes and failures in embedding AI into daily workflows.

Tony Fazzini

VP, Performance Marketing & Strategy
TradeStation

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As AI adoption accelerates, enterprises must establish strong governance frameworks that enable innovation while mitigating risks and ensuring ethical responsibility. This panel will explore how organizations can foster cross-functional collaboration to create governance strategies that align with regulatory requirements, business objectives, and user expectations.

• Defining AI governance frameworks and policies for your enterprise
• Engaging cross-functional risk, compliance, and technical teams for effective oversight.
• Implementing AI risk management strategies without stifling innovation.

David Wendt

Innovation and Artificial intelligence Governance Manager
Independent

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Sam Swift

Senior Director of Product Marketing
ActiveFence

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Felix Abu

Director of Corporate & Advisory
Robinhood

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Aveen Sufi

Director Privacy Operations
SCAN Health

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As open source models become foundational to enterprise AI strategies, legal and compliance leaders are raising critical questions about accountability, usage rights, and downstream liabilities. Franklin Graves, Linkedin’s Senior Counsel for AI & Data Product, Platform & Partnerships, examines the evolving risk landscape of open source AI through a legal lens, outlining how everyone from strategic leadership to developers, deployers and engineering teams can stay ahead of legal and regulatory expectations while promoting responsible innovation.

• Identifying key legal exposures in open source AI adoption, including licensing conflicts, IP ambiguity, and model provenance.
• Building internal frameworks that enable legal, risk, and engineering teams to vet, monitor, and document AI component usage effectively.
• Translating emerging AI regulations into practical development workflows, ensuring traceability, transparency, and compliance-readiness.
• Clarifying terminology distinctions between #AI, #ML, and #GenAI to prevent misinterpretation in policy drafting and stakeholder communication.
• Promoting engineering accountability by embedding responsible AI principles into code reviews, model evaluations, and deployment processes

Franklin Graves

AI & Data - Legal Team
LinkedIn

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As telecom networks grow in complexity, the ability to harness real-time data and non-real-time data to help automate decision-making at scale is critical. Nokia is at the forefront of building agentic AI and Generative AI driven platforms, that transform how telcos manage vast network infrastructures. This session will explore Nokia’s approach to implementing and scaling generative AI, driving efficiency, customization, and compliance in a highly regulated industry.

• Developing agentic AI tools to streamline telecom network operations and scale enterprise AI use cases.
• Designing a robust AI platform that enables real-time data access, customization, and network optimization.
• Extracting business value from telecom data through effective governance, modelling, and security strategies.
• Navigating the regulatory landscape while ensuring responsible and compliant AI deployment.
• Leveraging open-source models to accelerate AI adoption and innovation across telecom networks.

Jitin Bhandari

Chief Technology Officer, Cloud and Network Services
Nokia

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Generative AI is transforming journalism by uncovering hidden patterns in vast datasets, automating content creation, and addressing challenges like misinformation. Beyond enhancing reporting, AI also presents new opportunities for media organizations to monetize data through AI-driven products and services.

• Identifying hidden patterns in rich datasets to enhance reporting accuracy.
• Automating content workflows while maintaining editorial integrity.
• Developing AI-driven data products and transitioning toward SaaS models.
• Addressing AI disillusionment and ensuring responsible AI adoption in journalism.
• Applying AI to key industry challenges – including misinformation, covert operations, and state-funded media.

Ramkumar Shanker

Director Data Science
Gannett

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This strategic session explores how AI is reshaping the future of product, data, and engineering leadership - and what it takes to lead in this new era. Drawing from the 9-Step Framework for AI Product Creation, this talk outlines a disciplined approach to building AI capabilities that scale, align with business outcomes, and deliver measurable value.

• How AI product creation is redefining roles across the enterprise
• The new metrics, mindsets, and models required for AI-era leadership
• Why organizations must shift from scattered experiments to structured execution - and how to do it
• Designed for executive and senior product audiences, this session offers a clear blueprint to lead AI transformation with intention, not - improvisation.

Shub Agarwal

SVP, AI, U.S. Bank, AI Professor, Bestselling Author
U.S. Bank

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In this session, Kartick Kalaimani will discuss how organizations seek to build the right foundations to enable long-term success with AI. Drawing on real-world experience from leading large-scale transformation efforts, Kartick will outline how to align business cases, streamline technology selection, and manage the shift toward self-serve innovation, offering attendees a practical framework for scaling AI initiatives without creating operational chaos.

• How to frame and prove the business value of investments, including data in the AI era
• Choosing the right AI tools to solve the real business problems
• Establishing strong data governance frameworks, ensuring responsible AI and automation adoption at scale
• Building and enabling a network of empowered users without losing control
• Managing cultural and operational change across business, finance, data, and tech teams

Kartick Kalaimani

Vice President
Dentsu

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As enterprises seek to scale Generative AI solutions, achieving true value demands a balance of robust governance, strategic investment, and clear ROI measurement. This session explores how to establish enterprise-wide AI assets, supported by a robust infrastructure with governance at the core. Elevating the business value of Generative AI while addressing the critical challenge of measuring efficiencies and mapping desired outcomes to infrastructure requirements.

• Building governance frameworks to support scalable enterprise AI assets.
• Mapping infrastructure investment against core metrics to demonstrate a clear ROI
• Practical methods to measure the value of AI-driven efficiencies in workflows.

For financial services leaders, 2024 was focused on building AI governance frameworks and establishing responsible AI principles. Now, in 2025, the challenge shifts to execution - moving from policies to real-world AI deployments that scale across the organization. This session will explore how State Street is bridging governance with implementation, ensuring AI use cases progress from pilots to production while aligning legal, technical, and business stakeholders.

• Translating AI governance frameworks into actionable deployment strategies.
• Scaling AI use cases from proof-of-concept to enterprise-wide impact.
• Aligning legal, technical, and business teams for seamless AI adoption.
• Understanding the evolving role of AI models in operational decision-making.
• Ensuring continuous governance as AI systems mature and scale

Nisheeth Chaudhary

Vice President Emerging Technologies and AI Governance
State Street

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