Nearly all companies — no matter the size or industry — are in some state of digital transformation. At the heart of those efforts is data. Without clean, clear, actionable data, digital transformation is nearly impossible. Join Brian Koehler, who leads WWT's data streaming practice, as he dives deep into the vital role data plays in digital transformation, how organizations should structure data governance to make the most of those transformations, and key technologies that are tying it all together.

C-level leaders are looking to artificial intelligence (AI) to accelerate automation within their organizations. But in the rush to adopt AI, many forget that the success of both AI and automation hinges on an organization's data maturity. Join Anshul Chaturvedi, a principal consultant with WWT focused on data, analytics and AI, as he talks about how organizations can mature their data capabilities to maximize their AI and automation investments. Anshul breaks down the interplay between automation and AI, pragmatic ways to start incorporating AI into an automation journey and provides tips on communicating progress to executive and line of business stakeholders.

The incredible popularity of generative AI and ChatGPT is forcing technology leaders to take notice and understand its role in enterprise IT. What is it? How is it made? What are its use cases? And what are its limitations?   Join WWT data scientists Ankur Gupta, Achal Sharma and Aditya Prabhakaron as they go under the hood of ChatGPT and talk about the technology behind the scenes of Chat GPT and how large language models are poised to make an impact on how we do business.

Today's artificial intelligence models are revolutionary — reshaping industries by automating complex business decisions previously left for experts. Success with AI, as many now know, is predicated on data. So, what happens when the data we feed our AI models is inherently biased? Join WWT Data Scientist Charlene Ulrich and Big Data Consultant Daniel Cholakov as they talk about the growing need for leaders to scrutinize AI and natural language processing (NLP) models for bias. Charlene and Daniel use a recent WWT Research paper on mitigating bias in AI using debias-GAN as a jumping off point for a conversation that includes why de-biasing AI is important, where and how bias can take place and how you can use our findings to to improve your AI strategies.