A Different Take on Data Privacy, AI, and AI management

CDW Canada Tech Talks

• Up until recently access to AI was really restricted to fewer people. The need for specialized tools, knowledge and technology created hurdles that many organizations were not able to clear. How have you seen access to AI and the perceived value of AI change across different consumers?
• One of the ways we have been segmenting generative AI with customers has been drawing a distinction between the targeted consumer of the AI tools and the risk profile of the data that data consumer will be accessing. How has the need for data governance become a barrier for broader AI use cases?
• How has the hype around generative AI changed the types of traditional AI conversations you are having?
• In addition to better establishing more mature data governance practices what are organizations doing to reduce the risk of over sharing data?
• Do organizations view digital sovereignty for AI services differently that other types of more traditional data and application services?
• What does the current distribution of AI workloads on premises, at the edge and in the public cloud look like?
• How sensitive is the data being used for the initial phase of generative AI services we are seeing organizations build today?
• Are some verticals more prepared to take advantage of their data with AI?
• How are organizations deciding between operating on premises vs in the public cloud for AI services?
• Are we able to put the factors that influence the decision-making process in order of importance?
• How do the traditional methods of managing data and technology limit our ability to integrate AI into our organizations quickly and effectively?
• What are some of the ways we can change to adapt to the new demands of AI?
• How can AI development platforms accelerate an organization’s AI journey?
• How does managing AI assets differ from traditional application deployment management?
• How will organizations decide to be a consumer of AI vs a creator of their own AI assets?
• What is the importance of measuring the efficacy of your AI model? How do you see AI assets being measured for efficacy and compared between iterations?
• As we look to the future what are some of the trends that we predict in this space?
• What excites you about the future of AI and the tools that you will get into the hands of organizations and users?
• How can our listeners become advocates for AI in their organizations and where can they go to learn more to stay on top of all things Ai related?

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