AI For Enterprise

At Philips, we believe the value of AI is only as strong as the human experience it supports. That’s why we combine the power of AI with deep clinical knowledge, gained through decades of experience in healthcare. We envision AI that is fully integrated into the workflows of healthcare professionals and people’s daily health routines – supporting them at every stage of the health continuum. With AI, we can help people take better care of their health and well-being, and enable healthcare professionals to do what they do best: prevent, diagnose, treat, and monitor. In this presentation we will highlight few important Ai principles that governs our AI Vision:

• Well-being – We design our solutions to benefit the health and well-being of individuals and to contribute to the sustainable development of society.
• Fairness – We develop and validate solutions using data that is representative of the target group for the intended use, and we aim to avoid bias or discrimination.
• Oversight- We design AI-enabled solutions to augment and empower people, with appropriate human supervision.
• Transparency- We disclose functions and features of our offerings that are AI-enabled, the validation process, and the responsibility for ultimate decision-taking.
• Robustness – We develop AI-enabled solutions that are intended to do no harm, with appropriate protection against deliberate or inadvertent misuse.

• What to look for in an AI solution in 2021 
• How processing at the edge can create more efficient decision making 

• How your organisation can accelerate digital transformation through end-to-end automation 
• Layering tools to enable operational efficiency 
• Opening doors to innovation 

• What is AI bias, and is it fair? 
• Discussing liability and security 
• Who benefits from AI, and could it result in a decrease in human-human interaction and employment issues? 

• Details to follow…

• What are the ethical dilemmas with the development and deployment of AI?
• To what extent do frameworks, regulations and tools provide meaningful guidance? 
• How can or should organizations deal with these dilemma’s/The organizational dimension of responsible data science  
• Understanding the role that AI technologies play in Covid-19 crisis response  
• Examining the impact of collaboration on a global scale  
• Accelerating capabilities through detection, prevention, response & recovery 

• As your organisation grows, how can you avoid congestion, outages and cost spikes?  
• Matching your data architecture to your workloads – creating a new storage category  
• Delivering an enterprise-ready data platform that can keep up   

• Best practices to ensure digital competitiveness 
• Increasing operational workflow through data management 
• Optimizing systems for maximised performance 

• How to move your project from “experimentation” to “live”  
• R&D budget signoff – understanding why you need AI 
• Turning AI into ROI – what does value mean to your business? 
• Top tips for moving your pilot to the next stage 

• Details to follow…

• Details to follow…

• How can organisations turn to data and AI to meet digital transformation demands?  
• Protecting against fraud  
• Making faster and smarter decisions to drive growth and reduce risk   

• Understanding the limits of automation, and the balance when implementing AI   
• How do the best organisations approach risk management and governance in their AI projects?