Recap: WLDA Data & AI Summit 2023

Last month, I had the incredible opportunity to attend the WLDA Data & AI Summit, an event that stood out for its unique blend of creativity and innovation. The summit featured five thought-provoking think-tank sessions, allowing attendees to delve into specialized topics of their choice. Among these, the session on Operationalizing AI—moderated by Ellen Nielsen (Chief Data Officer at Chevron) and Maria C Villar (Head of Enterprise Data Strategy and Transformation at SAP)—was particularly enlightening, offering deep insights into the practical aspects of AI implementation.

The keynote address by Jane Lauder (EVP of Enterprise Marketing and Chief Data Officer at The Estée Lauder Companies Inc.) was a highlight, setting the tone for a day of insightful discussions and groundbreaking ideas. Dara Meath, PMP (SVP, Chief Technology Officer at Build-A-Bear Workshop) skillfully led a recap of the think-tank sessions, distilling the essence of each discussion and presenting key takeaways that resonated with all attendees.

One of the most inspiring segments of the summit was the WLDA ventures presentations, where women entrepreneurs showcased their innovative ideas and ventures.

Networking at the event was nothing short of phenomenal. I had the honor of meeting Asha Saxena (Founder of WLDA) and connecting with a host of Data & AI thought leaders. It was also a pleasure to finally meet Peggy Tsai in person, a testament to the power of bringing virtual connections into the real world.

Here are some key takeaways from the summit that I believe are crucial for anyone in the field of Data & AI:

1. From Collecting to Connecting Data: The shift from merely collecting data to effectively connecting it, enabling deeper insights and more impactful decision-making.
2. Crowdsourcing AI Ideas: Encouraging the entire company to contribute ideas for using AI, fostering a culture of innovation and efficiency.
3. The Emergence of AIOps: Understanding the importance of AIOps, akin to DevOps and DataOps, in streamlining and optimizing AI applications.
4. Breaking AI Stereotypes: Addressing and overcoming the trust issues and skepticism often associated with AI, and building a foundation of reliable and ethical AI use.
5. AI and Business Synergy: Highlighting the importance of bringing business and AI together to successfully enable and implement AI strategies.
6. Metrics That Matter: Focusing on metrics that drive meaningful use and impact, measuring what truly matters.
7. Responsible AI: Approaching AI with responsibility, intention, and sustainability, considering the ethical implications and long-term impacts.
8. Balancing AI Applications: Understanding the balance between General AI and ‘vintage’ AI, and recognizing the appropriate applications for each.

The WLDA Data & AI Summit was more than just a conference; it was a confluence of ideas, innovations, and inspirations. I left the event with a renewed sense of purpose and a deeper understanding of the evolving landscape of Data & AI.