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, Product Management, Ai & Machine Learning
JPMorgan Chase & Co.

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Anthony Puleo

Associate Director, Data Science
AstraZeneca

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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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From intelligent search and content generation to workflow automation and departmental process orchestration, this session will explore how AI can streamline daily tasks, enhance collaboration, and unlock new levels of productivity.

• Leveraging AI for intelligent search and knowledge discovery across documents and teams.
• Using Generative AI to summarize reports, meetings, and discussions for faster decision-making.
• Automating content creation, updates, and deliverables with AI-powered tools.
• Enhancing efficiency through AI-driven workflow automation and process orchestration.
• Improving communication with AI-generated responses and real-time insights.

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In this session, Anthony Puleo, who leads the strategic roadmap and AI integration within AstraZeneca’s Global Supply Chain & Strategy organization, will share how the company is building toward a self-healing, AI-native operating model. The talk will highlight AstraZeneca’s practical approach to enabling AI at scale—through enabling foundational infrastructure and targeted use cases delivering measurable value. The importance of digital twin technology, which is already saving over 2,000 hours of manual effort annually, and the impact to decision-making will be a focal point. Attendees will gain insights into:

• The essential enablers for successful generative AI adoption in supply chains
• How to prioritize high-impact AI use cases that drive business outcomes
• The evolving role of supply chain professionals in an AI-augmented future
• The orchestration power of large language models (LLMs) across software ecosystems
• Connecting strategic goals to real-time decisions via digital twins and prescriptive analytics

Anthony Puleo, Director Data Science & AI, Astrazeneca

Anthony Puleo

Associate Director, Data Science
AstraZeneca

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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.
• Optimizing data storage, retrieval, and processing to enhance AI model efficiency.

Abhijit Rai

Senior Director, Software Product Management & Strategy
New York Life Insurance

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Bhaskar Dakamarri

Vice President Data Science
JPMorgan Chase & Co

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This session explores how enterprise data leaders are tackling quality issues at the source, building robust validation pipelines, introducing smarter labelling and cleansing practices to ensure data is trustworthy, context-rich, and usable.

• Identifying hidden data quality risks in GenAI training and prompt engineering
• Designing quality pipelines to support structured, semi-structured, and unstructured data
• Collaborating across teams to embed quality controls into GenAI workflows
• Avoiding hallucinations and bias with better upstream data handling

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As organizations race to adopt generative AI, identifying the right use cases and scaling them effectively is critical to driving real business value. This session will explore how to generate, refine, and scale AI solutions that address both internal efficiencies and customer-facing innovation -

while ensuring responsible deployment.

• Methods for identifying and prioritizing generative AI use cases – spotlighting applications in sustainability reporting and code generation.
• Strategies to scale AI solutions, from proof of concept to enterprise-wide implementation.
• Techniques for combining diverse data sources with conversational AI to drive more powerful outcomes.
• How to use targeted questioning to optimize AI models and outputs in real time.
• Establishing guardrails and guidelines to empower engineers while maintaining compliance

Sreedhar Sistu

Vice President, AI Offers
Schneider Electric

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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.

Puneet Matai

Data & AI Governance Lead
Rio Tinto

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Keith Stouder

Vice President of Data Privacy and Artificial Intelligence Governance
ACT

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Olga Kozak-Anlar

Compliance AI Lead
Robinhood

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AI trust hinges on well-defined governance frameworks that enforce ethical, secure, and transparent AI usage. Beyond compliance, organizations must implement robust guardrails to mitigate risks while enabling AI-driven innovation for long-term AI resilience.

• Defining AI governance guardrails: Risk mitigation vs. innovation enablement.
• Implementing automated monitoring and enforcement of AI policies.
• Managing AI model transparency, explainability, and accountability.
• Addressing security, bias, and ethical considerations in AI deployment.
• Creating enterprise-wide trust through clear AI usage guidelines.

Session Available For Sponsorship

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

Senior Vice President of Product Management, Data, Platforms and AI
U.S. Bank

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Empowering employees with self-service analytics is key to driving data-driven decision-making across the enterprise. This session explores how Generative AI and automation can simplify data access, reduce bottlenecks, and enable users at all skill levels to extract insights without reliance on technical teams.

• Implementing AI-driven natural language query tools to make data analytics accessible to non-technical users.
• Automating data preparation and visualization to reduce complexity and speed up insight generation.
• Embedding AI copilots into enterprise tools to provide contextual recommendations and real-time analytics.
• Establishing governance frameworks that balance data accessibility with security, compliance, and quality control.
• Driving cultural adoption by upskilling teams and fostering a data-first mindset across the organization.

Enterprises must move beyond ad-hoc governance approaches and establish centralized, scalable frameworks to ensure long-term compliance, fairness, and accountability. This panel will discuss how you can embed responsible AI principles into your infrastructure, processes, and strategy.

• Establishing centralized AI governance models that scale across the enterprise.
• Automating policy enforcement, bias detection, and compliance monitoring.
• Aligning governance frameworks with evolving regulations and industry standards.
• Ensuring transparency, explainability, and accountability in AI decision-making.

Marzieh Abolhelm

VP Model Risk Manager & AI Governance
Mufg

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Kerry Barker

AI Governance Lead
Google

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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

VP/Director Analytics & BI Products Strategy, Bionics
State Street

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