Explore proactive approaches to embedding risk management into AI development, crucial for building trust and ensuring safety. This talk provides practical insights on integrating responsible AI principles from the outset, safeguarding brand reputation and user trust.

• Incorporating risk management into the foundational stages of AI projects to prevent issues.
• Communicating responsible AI concepts effectively to engineering and product teams using relatable language.
• Prioritizing user-centered design and sustainability to mitigate risks and foster long-term trust and brand reputation.

Cecilia Nunn

Responsible AI Lead
Digital Catapult

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Jenifer Cheung

Technology Ethicist
Digital Catapult

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Delve into the paramount role of robust risk management and cybersecurity in securing generative AI ecosystems, exploring:

-       Fortifying data protection protocols, ensuring end-to-end encryption and secure handling of generative AI outputs.

-       Conducting regular penetration testing to identify vulnerabilities and proactively address potential cyber threats.

-       Establishing comprehensive risk assessment frameworks, integrating AI-specific considerations for a resilient generative AI environment.

Valentin Khatchatourian

Director
UBS

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Jules Ferdinand Pagna Disso

Head of Cyber Risk Intelligence, Insider Technology Risk and Digital Asset Risk
BNP Paribas

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Gaurav Dhama

Direct of Product Development - AI
Mastercard

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The imperative to harmonize the understanding of data engineering fundamentals between technical experts and executives is paramount. This session delves into the criticality of

educating both cohorts for seamless collaboration and informed decision-making.

• Implementing Technical Leadership Training: Introducing executive level training programs focusing on advanced data engineering principles and AI technologies.

• Fostering Cross-disciplinary Workshops: Organizing workshops that facilitate in-depth discussions between technical and executive teams, promoting mutual comprehension & improving outcome.

• Establishing Technical Liaison Roles: Appointing technical liaisons or data ambassadors within executive teams to enhance communication channels.

Joe Reis

Best Selling Co-author of Fundamentals of Data Engineering
O'Reilly

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Computing large foundation models is pivotal for generative AI, but it presents unique challenges. This session explores the importance of efficiently handling these models and offers actionable strategies for businesses:

-       Learn best practices to optimize computing infrastructure for large foundation models.

-       Explore cost-effective solutions for scaling AI computing while maintaining performance.

-       Gain insights into cutting-edge technologies and tools to manage, deploy, and fine-tune large AI models.

Tom Mason

Chief Technology Officer
Stability AI

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Robert recently joined Newcross to help the business maintain its competitive advantage in the age of AI. In this session Robert will cover:

-       Developing a ‘NewcrossGPT’ to enable staff to leverage Generative AI capabilities with internal data and IP

-       Creating unique contexts for each team, such as sales, marketing and engineering, to enhance the effectiveness

-       The method for deciding what data to train the model on and the governance structures around data access

-       Ensuring that data used in the system is both secure and not ending up in OpenAI’s data set

-       Taking an agile approach to technology implementation

Robert Chilvers

Chief Data & AI Officer
Newcross Healthcare

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Using the analogy of a modern commercial aircraft that it is effectively a flying Data Lake, David, an expert in the practical application of AI, will talk about how Generative AI can be used to tame both the Still Waters and the Stormy Squalls in these Data Lakes, potentially in real time. Such an aircraft is a great proxy for the complex data infrastructure of a large business, where data changes very rapidly. 

A veteran of AI, David will cover a multitude of topics relevant to many businesses, not just aerospace, and explain where and how Generative AI can be used to best effect, including:

• Taming the mounds of documentation in many businesses using AI Natural Language Processing
• Using Computer Vision for personnel security or quality control in manufacturing
• Detecting cyber security threats using AI anomaly detection
• Predicting equipment failures before they happen to increase availability and up-time

In conclusion, David's expertise in practical AI applications offers valuable insights into leveraging Generative AI to navigate the complexities of modern data infrastructure, covering diverse topics from taming documentation with NLP to enhancing security and predicting equipment failures, demonstrating its potential to optimize operations across various industries beyond aerospace. Attend session to learn more.

David Stevenson

Chief Data Scientist
Leonardo

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It's crucial to understand how to transition from AI hype to real business value:

-       Defining clear AI objectives aligned with business goals to ensure meaningful value creation.

-       Cultivating cross-functional AI teams that work collaboratively to drive tangible outcomes.

-       Continuously evaluating AI investments, pivoting strategies as needed to maximize generative AI's impact. 

Hugo Hamad

Leader AI Factory
Decathlon

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Antonio López González

Chief Data Officer (Spain)
Decathlon

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Join to explore how to pioneer human-centric approaches to generative AI, test outcomes with real users, and adapt AI models based on human feedback.

-       Building interdisciplinary teams for comprehensive model evaluation, involving UX/UI engineers, data scientists, and designers.

-       Establishing robust workflows for planning, commentary summarization, and experimentation to enhance generative AI outcomes.

-       Adopting user-centric methodologies, test AI outcomes with real users, and iteratively adapt models based on feedback.

Igino Fucci

Head of Experience Design for Artificial Intelligence (Investments)
AIG

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In today's AI-driven world, crafting seamless user experiences is paramount. Join this session to explore:

-       Problem solving: Understanding the problems that users are keen to address

-       Co-designing AI Products: navigating the collaborative process between users and AI engineers

-       The Human-AI Interface: understanding the pivotal role of UX in AI integration

-       User-Centric AI: discovering strategies for enhancing user satisfaction in AI-powered systems

Geraldine Low

Lead Product Designer, Artificial Intelligence and Machine Learning
GSK

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Adeo have several use cases in production, including improving product descriptions on their websites and a search engine for internal documents to improve collaboration. In this session Djamel will share their process for moving from ideation through to production:

-       Aligning Ideation with Business needs: Learn strategies for aligning AI ideation with organizational objectives

-       Proof of Concept to MVP: Explore techniques to develop successful proof of concepts and transition them into MVPs

-       Measuring ROI: Gain insights into measuring the impact and ROI of generative AI projects

Djamel Mostefa

Head of Data and Artificial Intelligence
Adeo

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Explore how to take a strategic approach to navigating the lifecycle of generative AI, highlighting key insights and best practices. Understanding this journey is crucial for businesses aiming to harness the full potential of generative AI technologies.

• Investing in robust data infrastructure to support generative AI training and deployment.
• Fostering interdisciplinary collaboration to ensure diverse perspectives and expertise in AI projects.
• Implementing continuous monitoring and iteration processes to adapt generative AI models to evolving needs

Jair Ribeiro

Analytics and Insights Leader
Volvo Group

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Learn practical strategies to navigate the hype and effectively scale generative AI within your business:

-       Understanding the real-world applications of generative AI for process optimization and efficiency.

-       Discovering actionable tips for seamless integration of generative AI into existing workflows.

-       Gaining insights into potential challenges and how to overcome them, ensuring scalable success.

Chanuki Illushka

Former Head of Data Science
Zoopla

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David Vinagre Solans

Head of Data Science
Santander

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In today's fast-evolving technological landscape, businesses face the dual challenge of harnessing the power of AI innovation while ensuring public confidence and trust. Delve into this discussion, focusing on the complexities of fostering trust with clients and stakeholders alike.

-       Exploring the importance of customer perspective, public understanding & model explainability

-       Understanding methods of fostering comfortable user adoption and maintaining brand

-       Overcoming challenges in transparency & awareness for the public

Rebecca Swift

SVP Creative Content
Getty Images

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As generative AI becomes more pervasive, the need for conscious adoption is paramount. This session sheds light on the importance of careful deployent and offers practical steps for businesses in:

-       Embracing conscious design principles to mitigate unintended effects when implementing AI solutions

-       Understanding the liabilities associated with generative AI deployment and proactively address them

-       Navigating the complex landscape of regulations and risks in the EU while promoting responsible AI use

Jason Smith

Chief Digital Officer (CDO)
Publicis Groupe

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Explore the critical intersection of authenticity, trust, and transparency, crucial for success for your employees & customers.

-       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.

Andy Parsons

Senior Director of the Content Authenticity Initiative
Adobe

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Adriana Castro

VP of Data and Digital Strategy
The Adecco Group

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The session will provide valuable insights into utilizing data and technology to enhance operational efficiency, save costs, and improve customer experience, through:

-       Learning how the application of Generative AI is helping to prevent costly delays and solve complex rail problems to improve overall customer experience, operation, and reliability.

-       Understanding the role of Generative AI in maximizing the efficiency of asset-intensive industries, amid challenges such as urbanization and climate change.

David Moore

Director of Emerging Technologies
Balfour Beatty

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Given their position as an early adopter of chatbots, Vodafone have a significant amount of data from interactions with customers both pre and post Generative AI. This enables them to better understand where the key value lies. In this session Emre will share:

-       Strategies for enhancing the ease of use for customers

-       Transitioning more of their customer relationships and product sales through their app and chatbot

-       Balancing the value of particular use cases against the costs to identify the best applications for Generative AI

-       Steps to mitigate the risks of hallucinations

Emre Ergun

Group Digital Director
Vodafone

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Assessing and optimizing the return on investment (ROI) for Generative AI projects is paramount as businesses increasingly invest in this transformative technology.

-       Learning to measure the tangible and intangible ROI factors to justify and enhance generative AI projects.

-       Discovering strategies for aligning Generative AI goals with business objectives and market demands.

-       Gaining insights into continuous monitoring, model refinement, and adaptation to maximize Generative AI project ROI. 

Detlef Nauck

Head of AI & Data Science Research
BT

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Jim Edwards

Global Innovation Capabilities Leader
Kimberly-Clark

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In this session, Bilal will outline their approach for ensuring they have the right data to drive successful AI outcomes.

-       Leveraging Generative AI as one of the potential solutions rather than the solution to all problems.

-       Identifying the most important data to support organisational goals.

-       Developing a data governance and maintenance structure

-       Harmonizing data across the business.

Bilal Alani

Group Head IT & Data, R&D
Danone

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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.

Natalia Konstantinova

Architecture Lead in AI
bp

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David Foster

Founding Partner
Applied Data Science Partners (ADSP)

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Explore the central role of Swarovski's AI competence center in empowering the business to realise the full potential of generative AI.

-       Monitoring Trends: Staying ahead by tracking generative AI advancements and market shifts

-       Delivering Value: Translating generative AI capabilities into tangible business impact

-       Scorecards and KPIs: Developing metrics to measure generative AI success effectively

-       Collaboration with AI Champions: Engaging key stakeholders for holistic business integration

Alvise Montini

Head of AI Center of Excellence
SWAROVSKI

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