• In a world where many believe access to more and more data will lead to ever better decision-making, we’ll look at what AI really is - Identifying the current and future challenges and opportunities for emerging technologies

• New framework for thinking about AI, and discussion on how organisations can practically adopt these technologies and avoid being seduced by the hype

• Whilst these technologies are incredible at creating growth and streamlining operations, for companies to stay innovative they need to also use AI to unlock the creative capacity of their workforce.

• Macro impact these technologies may have on business and humanity over the coming decades

Daniel Hulme

Chief AI Officer
WPP

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This panel discussion dives into the latest industry and technology trends in Generative AI and provides insights into the outlook of this rapidly evolving field. Experts will discuss advancements, challenges, and potential applications of Generative AI, offering valuable perspectives on its impact across various sectors and the exciting possibilities that lie ahead.

• Examining the current state of the Generative AI industry, including recent technological advancements, breakthroughs, and emerging trends

• How to navigate all tools and offerings? How to categorize different tools?

• How can we keep pace with new platforms and technologies?

• Conscious AI depends on computing capacity – What is next needed from technology view - Quantum Computing

Sonika Kapil

AI & App Chief Architect
Microsoft

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Donatien Chedom Fotso

AI & ML Team Lead
Deutsche Bank

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• Understand the role of low-code/no-code tools in lowering barriers to AI adoption

• Analyze the implications for industries traditionally slow to adopt AI

• Review case studies showcasing innovative applications of these tools

• Explore the challenges and opportunities in the democratization of AI

Cristina Duta

Director of Intelligent Automation
AECOM

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• Key steps and best practices for integrating GenAI into organizational workflows.

• Enhancing human potential: the positive impact of GenAI on teams and professionals.

• But also discuss the potential pitfalls and strategies to mitigate risks associated with

 GenAI deployment.

• Case Studies: GenAI in HR Onboarding for better access to information ; and GenAI to

 enrich Customer Service data for transferring loan claims

Sébastien Robert

Chief Data & AI Officer
Floa

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• Developments in GenAI

• Trends in healthcare and potential of GenAI-powered innovation

• Example GenAI applications

• Challenges and way forward

Milan Petkovic

Head of AI & Data Science, Connected Care
Philips

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This presentation will provide insights into navigating the dynamic start-up landscape, focusing on the latest trends, innovations, key learnings, prospects, and outlook for aspiring entrepreneurs. Gain valuable knowledge and practical advice to thrive in the start-up ecosystem.

• Trends, innovations, key learnings

• Prospects and challenges for start-ups

• Adaptability, agility, continuous learning

• Building partnerships for innovation and growth

Kazik Surala

Head of Data Governance & Analytics- Poland
Phillip Morris International

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Pooja Bhatia

Data & AI Cloud Solution Architect
Microsoft

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The future demands of Gen AI, focusing on sustainable and scalable growth, revolve

around several key areas. Organizations will need to address these demands to maximize

benefits while minimizing risks:

• Enhanced Computational Efficiency: Future demands will include developing more energy-efficient AI models to reduce the environmental impact of training and running these systems. This involves innovations in hardware (like specialized AI processors) and

 software (like algorithms that require less computational power).

• Scalable Infrastructure: As AI applications grow in complexity, scalable infrastructure that can support the expansion of AI systems without excessive costs will be crucial. This includes cloud services, data storage solutions, and network capabilities that can dynamically adjust to the needs of AI systems.

• Ethical AI Development: There is a growing demand for AI systems that are not only effective but also ethically designed. This includes transparency, fairness, and accountability in AI operations, ensuring that AI systems do not perpetuate biases or lead to undesirable societal impacts.

• Data Privacy and Security: With Gen AI heavily reliant on data, future demands will increasingly focus on securing and managing data privacy. This involves developing robust cybersecurity measures and data governance frameworks that protect sensitive information while allowing AI systems to learn and adapt.

• Regulatory and Compliance Frameworks: As AI technology impacts more aspects of life, appropriate regulatory frameworks will need to be developed and refined. These frameworks will ensure that AI technologies are used safely and in ways that contribute

 positively to society.

• Cross-Domain AI Applications: Future demands will involve extending AI applications across various domains, requiring multi-disciplinary knowledge and hybrid AI systems that can operate in diverse environments, from healthcare to transportation and

 beyond.

• AI Literacy and Workforce Development: There will be an increasing need for AI literacy among the general population and specialized AI training within the workforce. This is critical for enabling more people to interact with AI systems effectively and ethically.

• Sustainable AI Models: Sustainable growth in AI will also depend on developing models that can operate over long periods without needing constant retraining or consuming vast amounts of resources. This includes the ability to update models efficiently and manage the lifecycle of AI systems.

• Collaborative AI: Future Gen AI systems will likely be more collaborative, both in terms of how they interact with other AI systems and how they work with humans. Developing cooperative behaviors and interfaces that enhance human-AI interaction will be crucial.

• Global Standards for AI: As AI technologies become ubiquitous, there will be a need for global standards and benchmarks for AI performance, ethics, and interoperability. This will facilitate international cooperation and ensure a level playing field in AI

 advancements

Jonathan Crowther

Head, Predictive Analysis
Pfizer

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