Generative AI Week 2025 Day One Conference: Tuesday, November 11 2025

Effective change management is critical to ensuring adoption, alignment, and long-term success. This panel will discuss strategies for navigating organizational change, fostering an AI-ready culture, and overcoming resistance to transformation.

• Managing workforce impact: Upskilling, reskilling, and redefining roles in an AI-driven environment.
• Overcoming resistance: Strategies for engaging stakeholders and addressing concerns.
• Establishing governance and accountability for responsible AI integration.
• Measuring success: Tracking adoption, productivity gains, and business impact.

Nisheeth Chaudhary

Vice President Emerging Technologies and AI Governance
State Street

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Holly Walker

Director, Strategy Advancement
Humana

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Stephen Henn

MD, AI Technology Services
DLA Piper

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Mariano Gutierrez

VP Marketing Transformation & Chief of Staff
MetLife

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Generative AI is reshaping ways of working across enterprise, but realizing its full potential requires a strategic approach to integration and scaling. This session explores how Generative AI can transform products & platforms, to drive immediate efficiencies, bridge system gaps, and evolve ways of working to stay ahead in a rapidly shifting landscape.

• Identifying high impact use cases for Generative AI that drive immediate operational efficiencies.
• Strategies for scaling AI from pilot projects to enterprise-wide adoption.
• Overcoming integration challenges to connect siloed systems and maximize AI’s value.
• Adapting to the fast-evolving AI ecosystem while ensuring governance and control.
• Building an AI-ready culture that fosters innovation and continuous

Session Available For Sponsorship

As Director of AI & Digital Strategy at Cox Communications, Samantha Mcconnell has been instrumental in translating the promise of Generative AI into real business impact. In this session, Samantha shares how Cox is bridging the gap from proof of concept to full-scale production while navigating the human, structural, and governance dimensions of AI. Samantha will unpack the structural, cultural, and operational levers that are enabling Cox to evolve from pilot projects to enterprise-wide GenAI adoption.

• Practical steps to evolve AI initiatives from pilot to production, avoiding common pitfalls.
• Exploring Cox’s frameworks for identifying, tracking, and communicating the measurable impact of Generative AI solutions.
• Integrating AI in ways that enhance, not replace, human capabilities - addressing adoption, trust, and collaboration.
• Building enterprise-wide structures that support AI innovation, including data, talent, and cross-functional alignment. 

Samantha McConnell

Director of AI + Digital Strategy
Cox Communications

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Gautam Krishna

Director, AI Products
Cox Communications

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Generative AI is rapidly shifting from experimental to essential in modern software development. This panel will explore how organizations can strategically embed generative AI into the software engineering lifecycle — from design and coding to testing and maintenance — to drive productivity, code quality, and innovation. Experts will discuss practical steps, governance, and overcoming integration barriers.

• Identifying high-value entry points for generative AI within existing SDLC stages, including coding, documentation, and QA.
• Balancing AI-generated code with human oversight to ensure security, compliance, and maintainability.
• Establishing governance frameworks for responsible AI use across engineering teams.
• Managing cultural and workflow shifts: equipping developers to collaborate effectively with AI tools.
• Measuring ROI and success metrics when integrating generative AI into engineering processes.

Kumar Anand

Director of Software Engineering
Capital One

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Adam Jones

Senior Director, Software Engineering - Cloud & IT
GE Appliances

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Tomer Lancewicki

Director AI & Machine Learning
Walmart Global Tech

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Brad Kostreva

Assistant Director - Cloud Foundations and Platforms AI Engineering
Northwestern Mutual

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Deploying AI at scale requires a robust engineering approach that synchronizes infrastructure, data pipelines, code, and models—while ensuring reliability, performance, and maintainability. This session explores technical strategies to streamline AI deployment, minimize bottlenecks, and optimize system efficiency.

·        Designing AI infrastructure that supports scalable compute, efficient storage, and seamless model deployment.

• Building automated data pipelines that ensure real-time availability, quality, and compliance across AI workflows.
• Implementing MLOps and CI/CD for AI models to enable continuous training, validation, and deployment.
• Synchronizing data versioning, model retraining, and deployment cycles to prevent drift and maintain performance.
• Monitoring AI systems in production with observability frameworks for real-time performance tracking and issue resolution.

Session Available For Sponsorship

Gregory Johnson, VP Product Management for AI & ML is responsible for accelerating cost-effective service delivery for JPMorgan Chase’s vast network of contact centres, receiving over 400 million calls annually. In this session he joins us to share how they are building production-ready systems that are consistent, compliant, and reliable, to deliver tangible business value within highly regulated sectors at scale. Key takeaways include:

• Unpacking the risks of output variability in high-stakes, regulated environments.
• Designing and implementing production-grade guardrails to enforce response consistency.
• Leveraging extensive A/B testing to validate accuracy and stability before rollout.
• Aligning generative AI outputs with enterprise governance and regulatory expectations.

Gregory Johnson

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

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As enterprises push beyond static AI models, agentic AI is emerging as a key driver of autonomous decision-making and adaptive workflows. These AI systems go beyond execution—they perceive, plan, and act dynamically within complex business environments. This panel will explore how enterprises can architect, deploy, and scale agentic AI to unlock new efficiencies and innovation.

• Architecting agentic AI: Frameworks, orchestration strategies, and multi-agent collaboration.
• Integrating LLM-powered agents with enterprise data systems and APIs.
• Designing guardrails for reliability, interpretability, and compliance.
• Optimizing feedback loops: Reinforcement learning, retrieval-augmented generation, and self-improvement mechanisms.
• Scaling agentic AI across the enterprise: Infrastructure, compute considerations, and cost-efficiency.

Anuj Bindal

GM and Director, Visual Shopping
Amazon

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Samantha McConnell

Director of AI + Digital Strategy
Cox Communications

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Naresh Dulam

VP, Software Engineering
JP Morgan Chase & Co.

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Scaling AI agents across an enterprise requires more than just technical deployment—it demands seamless integration, robust governance, and continuous optimization. From aligning AI agents with existing workflows to managing compute costs and security, enterprises must navigate key challenges to achieve scalable automation and intelligence.

•        Establishing a scalable AI agent architecture: Infrastructure, orchestration, and interoperability.

• Integrating AI agents into enterprise ecosystems, including CRM, ERP, and cloud platforms.
• Managing adoption challenges: Organizational readiness, user trust, and change management.
• Optimizing AI agent performance through monitoring, feedback loops, and iterative improvements.

Session Available For Sponsorship

As the promise of agentic AI grows, media enterprises like Paramount are testing its boundaries - from media workflows to streamlining end-user operations. In this session, Brijesh Mandan, Senior Director of End User Technology, shares how Paramount is moving beyond experimentation, he’ll explore the technical, cultural, and governance challenges of scaling agentic AI in a complex, high-stakes enterprise environment - and what it takes to overcome the roadblocks to bring agentic to scale.

•        How Paramount is expanding agent capabilities from narrow tasks to orchestrated workflows across content and IT systems

•        Maintaining transparency and trust in AI-driven decisions within creative and operational domains

•        Orchestrating multi-agent collaboration through mature architectures and governance.

•        Ensuring agents have secure access to high-quality data and enterprise tooling to maximize effectiveness

Brijesh Mandan

Senior Director - End User Technology
Paramount

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This session explores how DLA Piper have built an AI development shop for the law firm, helping teams to pioneer AI within legal and, and unlock new growth opportunities.

• Identify areas where generative AI can eliminate waste in time and resources across workflows.
• Exploring AI as a potential revenue driver – Looking at PiperAI, an internal tool turned external solution
• Apply generative AI for smarter forecasting, pricing optimization, and enhanced revenue capture.
• Align AI implementation with financial planning to ensure long-term, scalable gains.
• Shift from cost-cutting to growth acceleration by leveraging AI to double revenue potential.

Stephen Henn

MD, AI Technology Services
DLA Piper

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As Prudential’s first Director of Generative AI, Ashley Cheretes has pioneered the strategy & implementation of GenAI tooling to redefine marketing strategies. This session dives into how Prudential is using generative AI to transform marketing operations at scale – employing their first digital co-workers to collaborate with creatives on drafting tailored content for millions of customers and advisers using real-time data, while ensuring every output aligns with compliance, brand voice, and strategic goals.

• Combining AI's agentic real potential with robust human oversight and clear outcome alignment.
• Scaling personalized marketing content without losing control of quality or compliance.
• Understand what a successful human-in-the-loop workflow looks like in a heavily regulated environment.
• Connecting tech to clear business outcomes – How do you ensure success?
• Changing creatives mindsets around AI – From sceptic to ambassador

Ashley Cheretes

Director Generative AI
Prudential Financial

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As Generative AI unlocks new possibilities across landscapes, GE Appliances is taking a strategic approach to where it builds vs where it buys. In this session, Adam Jones, Senior Director of Software Engineering – Cloud & IT shares how GE Appliances is developing custom integrations and user experiences in-house. This session explores how the team balances speed, ownership, and long-term value across its GenAI portfolio.

•        How GE Appliances evaluates which GenAI solutions to build internally vs. buying off-the-shelf

•        Techniques for identifying AI opportunities that create real ROI—not just hype—across the organization.

•        Frameworks for identifying high-ROI AI use cases that solve real business challenges

•        GE Appliances framework for evaluating current AI use cases and technology stacks against measurable business outcomes.

•        Practical guidance on balancing speed, cost, and long-term AI capability development in an enterprise context

Adam Jones

Senior Director, Software Engineering - Cloud & IT
GE Appliances

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Knowledge distillation offers a powerful technique to transfer knowledge from large models to smaller, more efficient ones - reducing computational demands while maintaining strong performance. Anuj Bindal presents how Amazon is leveraging knowledge distillation to optimize AI inference, enabling cost-effective, high-speed AI applications for customers at scale.

• Understanding how knowledge distillation reduces model size while preserving accuracy.
• Distilling large language and vision models without performance loss.
• Balancing inference efficiency with output quality in Generative AI applications.
• Implementing distillation in enterprise AI workflows for real-time and batch processing.

Anuj Bindal

GM and Director, Visual Shopping
Amazon

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Generative AI is evolving from passive insight generation to active process transformation. This session examines how organisations can harness support assistants, and developer tools to reimagine workflows—while managing risk, compliance, and without impeding on innovation.

• Deploy AI-powered assistants that go beyond knowledge retrieval to actively support decision-making and task execution
• Integrate agentic workflows into core business processes to enable autonomous, multi-step task automation
• Accelerate the software development lifecycle with Gen-AI tools for code generation, testing, and documentation
• Establish a culture of safe, continuous AI experimentation in regulated environments through sandboxing, auditability, and compliance alignment
• Evaluate open source and proprietary AI solutions to strike the right balance between customisation, speed, and control

Kumar Anand

Director of Software Engineering
Capital One

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Scaling AI agents across an enterprise like JPMorgan requires more than just technical deployment - it demands seamless integration, robust governance, and continuous optimization. From aligning AI agents with existing workflows to managing compute costs and security, enterprises must navigate key challenges to achieve scalable automation and intelligence.

• Establishing a scalable AI agent architecture: Infrastructure, orchestration, and interoperability.
• Integrating AI agents into enterprise ecosystems, including CRM, ERP, and cloud platforms.
• Managing adoption challenges: Organizational readiness, user trust, and change management.
• Optimizing AI agent performance through monitoring, feedback loops, and iterative improvements.

Naresh Dulam

VP, Software Engineering
JP Morgan Chase & Co.

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