Explore the critical intersection of authenticity, trust, and transparency, crucial for success for your employees & customers with a focus on tangible solutions for delivering safe and reliable AI solutions. Uncovering solutions to fix AI’s biggest ethical and trust concerns

• Establishing clear communication channels to convey the ethical use and limitations of generative AI systems.

• Implementing transparent AI governance frameworks to build trust and ensure responsible and ethical AI practices.

• Fostering collaboration and dialogue between businesses, AI developers, and the public to address concerns collaboratively.

Amie White

Senior Director of Risk, Ethics & Policy
Hinge

See Bio

There is often resistance from many angles in adopting AI, in this talk Kaustubh will exploring the challenges and strategies of marketing GenAI-enabled technology in regulated sectors.

• Partnering with regulatory bodies: Collaborating to navigate compliance requirements effectively.

• Educating stakeholders: Building trust through evidence-based demonstrations and educational initiatives.

• Fostering an innovation ecosystem: Engaging with thought leaders and collaborating for accelerated market adoption.

Kaustubh Bhatnagar

Global Marketing Director - Artificial Intelligence
Medtronic

See Bio

Human-in-the loop and traditional AI standards can only take us so far. In order to ensure large-scale, sustainable production and implementation of Generative AI, we need to be able to rely on our models

to ensure company data is safe & hallucinations are completely avoided. Join this session to understand how business leaders can explore:

• Implementing policies and guardrails for safe scalability from a human-in-the-loop perspective.

• Exploring augmented AI solutions to enhance data protection and ethical decision-making.

• Fostering & preparing for sustained, safe, large scale generative AI through responsible AI practices.

John Rares Almasan

Senior Managing Director | Head of ClienTech and AI
TIAA

See Bio

Explore the transformative potential of leveraging open source LLMs on on-premises infrastructure for enhanced AI capabilities. Unlock insights into inference frameworks, serving architectures, and best

practices for developing, evaluating, and monitoring GenAI applications.

• Embracing open source LLMs to harness the collective intelligence and innovation of the AI community.

• Optimizing on-premises infrastructure to ensure scalability, efficiency, and security for GenAI deployments.

• Implementing robust evaluation and monitoring protocols to drive continuous improvement and innovation in AI applications.

Optimizing compute in generative AI is paramount. As businesses increasingly rely on AI technologies for innovation and growth, optimizing compute resources becomes crucial not only for financial

sustainability but also for environmental responsibility.

• Distributing computing efforts strategically to maximize resource utilization and minimize wastage.

• Investing in Spotify’s Hendrix ML Platform, which streamlines AI training and serving processes for models with over 70 billion parameters.

• Collaborating with cloud service providers to access specialized hardware for efficient model training.

Avin Regmi

Engineering Manager, ML
Spotify

See Bio

Selecting between on-premises and cloud infrastructure is pivotal for successful Generative AI operationalization. Business leaders must weigh factors such as data security, scalability, and cost-effectiveness.

• Assessing organizational needs and stakeholder concerns to determine the optimal infrastructure.

• Establishing clear communication channels between technical and business teams to align priorities.

• Developing a roadmap for implementation, iteratively evaluating progress and adjusting strategies accordingly.

Satya Choudary

VP, Data & Analytics - Global Head of Cloud Platform, Data Engineering & Mgmt, Data Science & ML/AI
Credit Suisse

See Bio

Rama Ryali

Head, Data Platform, Compliance & Operations
McKesson

See Bio

Join us as we explore actionable strategies for optimizing value and return on investment from Generative AI. In this talk, Arjun will delve into how businesses seeking to leverage generative AI on a budget can

effectively drive innovation and achieve tangible results.

• Developing a business-focused technology strategy and roadmap aligned with organizational objectives.

• Democratizing AI by fostering collaboration and knowledge-sharing within the community.

• Use case prioritization - Prioritizing use cases based on effort, impact, accuracy, cost effectiveness, scalability and risk profile

Arjun Srinivasan

Head of Data Science (AI/ML/GenAI)
Wesco

See Bio

Integrating generative AI within business operations is pivotal for efficiency. This session explores practical steps for business leaders to deploy intelligent agents effectively, Tom will share his insights leading Astellas’ Generative AI efforts to a reality.

• Developing a compelling vision for intelligent agents in the corporate environment.

• Establishing technological frameworks and structures, prioritizing user experience (UX) for effectiveness.

• Addressing use cases, HR policies, and procurement considerations for seamless AI deployment.

Tom Bauer

Head of Digital Innovation
Astellas Pharmaceuticals

See Bio

Successfully transitioning from pilot studies to large-scale implementation is a pivotal challenge in realizing the potential of Generative AI.

• Gaining insights into the critical considerations and strategies for scaling Generative AI solutions across organizations.

• Learning how to address infrastructure, data, and compliance challenges when moving from pilot to production.

• Discovering best practices for talent acquisition, training, and collaboration to ensure a smooth transition to large-scale implementation.

James Lin

Head of AI/ML Innovation
Experian

See Bio

Kaushal Lahankar

Director - Machine Learning
S&P Global

See Bio

Generative AI is revolutionizing media creation and production processes. In this session, Heidi will explore the transformative potential of AI tools in enhancing media creativity and driving results within BestBuy,

through:

• Redefining the creative process: from conceptualisation, ideation, production & beyond.

• Establishing brand guidelines for ethical and authentic AI across the entire enterprise.

• Initiating various pilot projects to explore the massive potential of generative AI

Heidi Tuneberg

Senior Director, Production & Business Affairs
Best Buy

See Bio

One of the primary ways customers shop online, visual shopping refers to any shopping based on visual attributes. Everything from clothes to home décor, visuals and aesthetics are increasingly important to consumers. Join this session to understand how Generative AI can enable meaningful visual shopping experiences, transforming the ways customers find products and make decisions.

• Exploration and visualization of new products helping customers personalize options and dream up their desired products.

• Enabling greater search options by allowing customers to increasingly use visual descriptions to find products.

• Evaluating products to accurately understand how a product will look in their space before purchase to reduce return costs.

• Increasing conversion rates by offering customers more opportunities to shop.

Anuj Bindal

GM and Director, Visual Shopping
Amazon

See Bio

Discover strategies to achieve authentic personalization at scale within the creative industries.

• Transitioning from cohort-based to true personalization for enhanced customer experiences.

• Overcoming legal, compliance, and tech limitations to implement personalized concierge services.

• Addressing data privacy concerns and infrastructure limitations while ensuring effective personalization strategies.

Sky Kelley

Global Co-GM, Digital Product Innovation
Nike

See Bio

This talk will explore the seamless transition from traditional data and model governance to the nuanced realm of AI governance. We’ll delve into how leveraging existing best practices can be the cornerstone for

developing robust AI governance frameworks, emphasizing an adaptive approach over a direct “lift and shift” method.

Key Highlights:

• The evolution from data and model governance to AI governance.

• Adapting existing governance frameworks to address AI’s unique challenges.

• Practical steps for integrating AI governance into organizational practices.

• The importance of an evolutionary approach in ensuring ethical, transparent, and accountable AI systems.

• Join us to uncover how to pivot your organization’s governance strategies to embrace AI effectively, ensuring responsible use and management of AI technologies.

The operationalization and governance of generative AI demand a meticulous approach. In this session, Alayna will explore crucial considerations and offer practical insights for responsible implementation.

• Integrating AI governance recommendations into operational practices for ethical implementation.
• Establishing robust oversight mechanisms to monitor generative AI applications and ensure compliance.
• Prioritizing practical approaches for effective risk management in AI operationalization.

Alayna Kennedy

AI Governance
Mastercard

See Bio

Fernanda Del Castillo

Director, AI Governance
Mastercard

See Bio

In the era of Generative AI, establishing a robust enablement strategy in is paramount. As AI becomes increasingly integrated into healthcare workflows,ensuring ethical, regulatory-compliant, and secure practices is essential for patient safety and trust. Attend to explore how leading enterprises can navigate responsible AI practices in this time of rapid innovation:

• Prioritizing stakeholder engagement to gather diverse perspectives and ensure alignment.

• Establishing clear policies and procedures to guide ethical and regulatory-compliant AI use.

• Investing in AI ethics training to empower healthcare professionals with responsible AI practices.

• Fostering a culture of innovation and calculating GenAI business value and cost drivers.

Joe Siryani

Head of Data Science & AI
Wellstar Health

See Bio

Embark on a visionary journey exploring the “Text-to-Spaceship” concept, revolutionizing AI integration across NASA. Discover how AI innovations can transform text-based data into tangible advancements

in spacecraft design, through:

• Fostering interdisciplinary collaboration to integrate AI across all facets of aerospace operations.
• Investing in advanced AI technologies to unlock new possibilities for spacecraft design and development.
• Embracing the “Text-to-Spaceship” vision to propel the aerospace industry into a new era of innovation.

Ryan Mcclelland

Research Engineer
NASA

See Bio

Integrating Generative AI into business processes is crucial for driving innovation and efficiency. Business leaders must focus on strategy, risk management, scalable design and infrastructure to successfully implement Generative AI platforms. Understanding agentic reasoning design patterns and its applicability to enterprise generative AI use cases via agent workflows.

• Developing comprehensive strategies and ROI metrics for generative AI initiatives.

• Streamlining risk approval processes to accelerate deployment of AI applications.

• Investing in scalable hosting infrastructure to support the demands of generative AI.

Rashmi Shetty

Senior Director - Enterprise Generative AI Platform
Capital One

See Bio

In this talk, Sander will discuss the current state of Prompt Engineering drawing from his recent survey paper, The Prompt Report, done in collaboration with OpenAI and Microsoft. The 76 page paper is the largest ever study of prompting, and has gone viral on social media, obtaining millions of views. He will discuss the prompt classification system he developed for prompting techniques, including Zero-Shot, Few-Shot, Decomposition, Thought Generation, Ensembling, and Self-Criticism. Attend this talk to delve into the technical and business implications of prompting and how to get the best results for your teams.

Sander Schulhoff

CEO
Learn Prompting

See Bio

Generative AI presents unparalleled opportunities to transform healthcare delivery and outcomes. From personalized treatment plans to drug discovery, the potential impact is immense. Join this panel discussion to

delve into how pioneers in Generative AI for healthcare are approaching, predicting & overcoming challenges in this space.

• Prioritizing data privacy and security measures for patient information.

• Fostering collaboration between healthcare providers and AI experts for innovation.

• Investing in training and recruiting AI talent to drive generative AI initiatives.

Kausar Riaz Ahmed

Director, Generative AI Medical Engagement Lead
Pfizer

See Bio

Nadia Wood

Senior Machine Learning Engineer
Mayo Clinic

See Bio

Petrina Kamya

VP, and Global Head of AI Platforms and President (Canada)
Insilico Medicine

See Bio

Explore the imperative of driving innovation in Generative AI with a multi-modal platform approach. This talk will delve into use cases & strategies for building and leveraging next-generation platforms tailored to industry-specific needs.

• Collaborating with domain experts to develop models tailored to industry requirements.

• Prioritizing applications built atop the platform to address specific industry challenges and opportunities.

• Addressing infrastructure challenges to optimize cost and scalability for multimodal language models.

Abhinav Kohar

SLB
Head of AI Platform (Generative AI)

See Bio

AI agents are a fast-developing domain that leverages Large Language Models (LLMs) to autonomously perform and refine tasks, mirroring human-like efficiency and adaptability. Within the AI agent domain, multi-agent collaboration has been promising domain in recent research to solve complex tasks through multiple AI agents collaboration or debating. The talk will cover most recent research conducted in fields like finance, software and medicine and understand how multi-agent frameworks can help complete complex tasks, 

Tina Zhao

Head of Applied AI Modeling
BlackRock

See Bio

This session explores the critical importance of model selection and provides actionable steps for businesses to enhance their Generative AI efforts:

• Evaluating the specific requirements and constraints of each application to determine the most suitable model.

• Considering factors such as model complexity, data availability, and computational resources when making choices.

• Continuously monitoring model performance and adapting model selection strategies as AI technologies evolve.

Chirag Patel

Global Head of Data & Analytics - E2E Business Partner
CSL Behring

See Bio

Tina Zhao

Head of Applied AI Modeling
BlackRock

See Bio