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.

Holly Walker

Director, Strategy Advancement
Humana

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Datra Oliver

Senior Director of Capabilities
The Coca-Cola Company

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Stephen Henn

MD, AI Technology Services
DLA Piper

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Abby Knowles

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

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Generative AI is reshaping ways of working across Pepsico, but realizing its full potential requires a strategic approach to identifying and scaling the right use cases. This session explores how Generative AI can transform workflows and experiences across the enterprise, 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

Jyotisana Dhawan

Head of Technology
Pepsico

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• Practical steps to evolve AI initiatives from pilot to production, avoiding common pitfalls & improving the GTM velocity with AI influenced insights
• 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

Distinguished Engineer
Capital One

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Adam Jones

Senior Director, Software Engineering - Cloud & IoT
GE Appliances

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Brad Kostreva

Assistant Director - Cloud Foundations and Platforms AI Engineering
Northwestern Mutual

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Dave Robertson

Director Enterprise Architecture - Software Engineering | Applications
Exeter Finance

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AI teams spend months building prototypes that never make it to production. The problems start early. Data is unstructured and inconsistent. Outputs can’t be validated. There’s no shared way to measure quality or control behavior at runtime. By the time a demo works, scaling it safely is nearly impossible. This session breaks down the patterns behind those failures and shows how to fix them. We’ll walk through a live build using Meibel to show how structured ingest, performance scoring, and runtime control turn brittle prototypes into dependable systems. Attendees will leave with a clear framework for identifying what’s breaking in their own stacks and what matters most when scaling AI across their organization.

Kevin McGrath

Co-Founder & CEO
Meibel

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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 of 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.

Samantha McConnell

Director of AI + Digital Strategy
Cox Communications

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Vishnu Chelle

Product Director, AI/ML Platform, AI Agents
GSK Plc

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Wendy Zhang

Head of Data, Analytics & AI
Hayward Holdings, Inc

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Andreas Welsch

Founder and Chief AI Strategist
Intelligence Briefing

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One of the biggest barriers enterprises face in scaling generative and agentic AI is ensuring accuracy, reliability, and trustworthiness at every stage of the lifecycle. At the root of this is often a lack of understanding and oversight of the data used.

This session explores how human-in-the-loop evaluation bridges the gap between a proof of concept and a production-grade AI system. We’ll explore how leading companies design evaluation programs that combine a deep understanding of data, human expertise, and automation to improve performance and maintain trust.

You’ll learn:

• Why most evaluations fail and where the biggest opportunities for improvement lie.
• How human-in-the-loop evaluation drives measurable gains in accuracy and reliability.
• How to optimize your LLM judge to work reliably and at scale.
• How to structure a data-driven evaluation program that scales with your AI initiatives.
• How leading companies bring AI from experimentation to production with continuous evaluation.

Jason Liang

Co-Founder & SVP of Business Development
Superannotate

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Leo Linden

Product Marketing Lead
Superannotate

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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 goal.

• 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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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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Attendees will see how a lightweight, chat-driven interface can guide staff through loan processing while automatically validating and organizing data from scanned documents. This practical, agentic approach shows that real AI transformation is possible even with limited resources.

• Laying the Foundation: Identifying impactful, low-cost AI opportunities to automate internal workflows.
• Using OCR and LLMs to digitize and extract critical loan data from unstructured PDFs.
• Agent-Driven Workflows: Enabling staff to manage and validate loan data through an intuitive chat-based interface.
• Building on a Budget: Leveraging off-the-shelf models and no-code/low-code orchestration to stay cost effective.
• • End-to-end showcase of a conversational system that handles document ingestion and variable extraction in real time.
• • Maintaining data privacy, compliance, and trust without a heavyweight AI risk team.

CK Bhatia

Chief Data Officer, SVP, Enterprise Data Strategy & Governance
Tdecu

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Maisum Hashim

Head of AI & Automation
TDECU

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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 & IoT
GE Appliances

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In this session, Prathap will guide you through the next‑gen of automation frameworks, showing how test and quality engineering teams can tap into Generative AI. He’ll cover:

• Automation Framework Evolution- Leveraging GitHub Copilot, Playwright, and LLMs
• Plugin-Based Architectures -Integrating GenAI services seamlessly
• Intelligent CI/CD by Embedding GenAI into continuous testing and deployment pipelines
• Scaling Test Intelligence using vector stores and retrieval augmented generation (RAG)
• Strategic Metrics: Evaluating ROI for AI and automation initiatives

Prathap Raghavan

Director of Product Management
Santander Bank

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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

Distinguished Engineer
Capital One

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Yogesh Kale

Sr Engineering Manager - AI Powered Experiences
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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