Data Science Leaders

The EU AI Act, is it the first step towards comprehensive AI regulation that will make us all safer, or is it the scariest thing in AI today, or both?

In this episode we speak with Adam Gale, Field CTO for AI and Regulatory Compliance at NetApp, to demystify the EU AI act and discuss future-proof strategies to ensure compliance.Join us as we discuss:

• The core requirements of the act and how they impact your use cases
• Gray areas to look out for in the act
• Compliance with existing regulations such as DORA and GDPR
• Core governance capabilities that enable compliance for the EU AI act and future regulation

AI governance is no longer a hypothetical consideration—it's critical not only to meet regulatory requirements but also to build trust, drive adoption, and deliver tangible impact with AI. In this episode, we bring together experts Jared Vaudrey and Dr. Dylan Bobby Storey, who have spent years developing AI solutions in heavily regulated industries. They share their hard-won insights on the realities of implementing AI governance across a wide range of organizations and use cases.

Join us as we discuss:

• The value of AI governance for driving adoption and innovation
• How governance principles apply to traditional ML and genAI
• The common challenges of governance 
• Real-world examples and best practices from advanced teams integrating AI governance
• How automation helps scale governance and provide the flexibility to support future regulation

AI governance is more important than ever, but confusion reigns about basic questions such as: what it is, why it is important and what we should do about it. As an AI, data science, or business leader, you need to dispel these governance misconceptions in order to manage AI risk and ensure the safety and reliability necessary to drive adoption and impact. The ability of your organization to drive impact with AI, and potentially your career, depends on it.

Join us as host Kjell Carlsson, gives an opinionated take on the basic questions of AI governance:

• Why you should care about AI governance (and why it goes beyond ethics and regulation)
• What AI governance is (and how it differs from ethical AI, responsible AI, and trustworthy AI)
• Why AI governance is difficult (and the gap between frameworks and practices)
• What AI governance looks like in practice (across the AI/ML lifecycle)
• What we need for AI governance (and the capabilities to do it at scale) 

  

Data privacy - why is it so important in the age of AI, why is it so difficult, and what should we be doing to improve it in our organizations?

In this episode we speak with Dr. Rebecca Balebako, privacy engineer and Chief Privacy Officer at Lotic.ai, about why AI makes privacy more important than ever, the common misconceptions around data protection, who should own privacy, and the benefits and limitations of privacy enhancing technologies (PETs).

Join us as we discuss:

• The value of privacy (~5% of annual profit)
• The many definitions of privacy.
• The top 3 misconceptions: no one cares, it can be fixed with a privacy policy, and privacy is a blocker.
• Who should own privacy in your organization.
• PETs vs. data hygiene

For more on Rebecca’s consulting on privacy strategy, coaching and how to build a privacy advocate team see https://www.privacyengineer.ch/ and for more on privacy methods check out the book “Practical Data Privacy” by Katharine Jarmul.

What capabilities do you need to take advantage of AI and what changes will you need to make to your IT architecture? Well, let’s look at the data.

In this episode, Shawn Rogers, CEO and Fellow at BARC US, shares the results of their survey on how enterprises are optimizing their architecture for AI innovation. Shawn unpacks data on everything from the biggest obstacles to delivering AI impact to how companies are sourcing their AI capabilities. 

Join us as we discuss:

• The AI talent gap and the strategies that firms are using to close it
• The myriad AI capabilities that high-readiness firms are implementing
• The need to manage AI costs upfront to ensure deployment
• The perpetual question of build vs. buy (or both!)
• The crucial need for AI governance to ensure approval and adoption

Download the full report here.

How do you drive digital strategy and transformation with AI? Do you need an AI strategy or a business strategy that intelligently leverages AI?

In this episode, we delve into the challenge of driving transformation with AI in insurance with Fu'ad Butt, VP Head of Digital Strategy and Automation at OneAmerica. Fu’ad shares his best practices for identifying and executing AI projects. These range from how to identify the most promising use cases (hint: focus on augmented intelligence, but tie it to business value) to executing them successfully (build a test and learn process, and use multifunctional pods).  

Join us as we discuss:

• The role of AI in digital strategy today.
• Overcoming the challenge of aligning AI to business value.
• Experimenting efficiently with digital twins and a contrarian in the loop.
• Breaking hierarchies with multifunctional pods for faster impact.

AI is disrupting marketing, but the biggest threat isn’t AI systems misbehaving, it is the unintended consequences of AI systems performing exactly what they were intended to do.

In this interview with Dr. Daniel Hulme, Chief AI Officer at WPP and CEO of Satalia, we discuss the ways that AI is transforming marketing – from accelerating content creation and maximizing activation to exploring the creative landscape and creating “brains” that ensure it is responsible and legal. Also, tune in for fascinating discussions of AI consciousness and what it means to be a Chief AI Officer.    

Join us as we discuss:

• The greatest GenAI opportunities in marketing and beyond
• How to maximize AI impact with decision optimization
• Responsible AI and the challenges of AI systems going very right
• The emerging field of AI consciousness
• The Chief AI Officer: why you need one and the prerequisites for success  

For more information about the new research organization focused on AI consciousness co-founded by Daniel Hulme see conscium.com and his interview on the London Futurists Podcast.

How do you deliver impact with AI and ML and cut development time by weeks and even months? By understanding your customer, building trust, and managing risk. 

Done well, effective and responsible AI practices can be the secret to faster implementation, adoption, and performance at lower cost and risk.

In this episode with Dr. Alex Manasson, Data Science Leader for the Americas at International Flavors and Fragrances (IFF), we uncover their best practices for managing risk and driving rapid AI development and adoption in the safety-focused world of manufacturing.. Dr. Manassof shares insights on balancing statistical process control with predictive modeling, the importance of adapting your data collection processes, and the pros and cons of digital twins. Discover practical tips and strategies for implementing AI and ML tools to boost efficiency and foster trust in high-stakes environments.

How do you deliver value with responsible AI, who is responsible for it, how do you put it into practice, and could we use AI to make our organizations more ethical?  

This episode comes to you from the RevX conference in London, where we asked these questions of Chris Wiggins, Chief Data Scientist at the New York Times. He is also Professor of Applied Mathematics at Columbia University and author of the books “How Data Happened: A History from the Age of Reason to the Age of Algorithms” and “Data Science in Context”.

Join us as we discuss:

• What we can learn from the history of research ethics and data legislation
• The need for clear principles and defined ownership to ensure ethical AI
• The translation of ethical principles into checklists, standards, and product decisions
• The importance of benchmarking AI against human performance and addressing how human biases in data lead to biased AI outcomes

To see all of the sessions at the RevX conferences go to domino.ai/revx.

AI is not all about the data, however, your ability to develop and deploy efficient data pipelines is absolutely critical for unlocking the power of AI at scale. But how do you manage modern data pipelines for AI and how do you deal with fragmented ecosystems and spiraling costs? 

In this episode, brought to you from the RevX Philadelphia conference,  Richard Swakla, AI/ML Specialist at NetApp, joins us to discuss the current trends and best practices in the life sciences around data and AI. 

Join us as we discuss:

• The role of AI in enhancing productivity in healthcare and the life sciences, particularly in drug discovery,  claims processing and fraud detection.
• The growing importance of hybrid cloud solutions to balance cost, efficiency, and infrastructure access.
• Challenges in transitioning AI projects from pilots to production due to high costs and rapidly evolving models.
  
  

To see all of the sessions at the RevX conferences go to domino.ai/revx.