AI & RoboticsNews

This week in data: How to create or destroy value with generative AI

When it comes generative AI, data really is your moat. This week, we cover the latest in gen AI research from the Boston Consulting Group (BCG) (you may have read VentureBeat’s Matt Marshall’s latest perspective on the findings). I also bring an expert guest to help use determine why chief data officers are set up to fail. Let’s dive right in.

The CarCast also includes extras such as: “The latest cybersecurity MAP,” “The future of generative AI in 15 charts” and insights from Netflix cofounder Marc Randolph on what defines a company.

Bruno Aziza is a technology entrepreneur and partner at CapitalG, Alphabet’s independent growth fund.

VentureBeat presents: AI Unleashed – An exclusive executive event for enterprise data leaders. Network and learn with industry peers. Learn More


When it comes generative AI, data really is your moat. This week, we cover the latest in gen AI research from the Boston Consulting Group (BCG) (you may have read VentureBeat’s Matt Marshall’s latest perspective on the findings). I also bring an expert guest to help use determine why chief data officers are set up to fail. Let’s dive right in.

  • Improving and destroying productivity with gen AI: In the BCG study, 90% of participants improved their performance when using gen AI for creative product innovation, and in fact converged on a level of performance that was 40% higher than that of those working on the same task without gen AI. However, when participants used the technology for business problem solving, they performed 23% worse than those doing the task without GPT-4. Even participants who were warned about the possibility of wrong answers from the tool did not challenge its output. Bottom Line: Gen AI is a powerful leveler of performance but people might mistrust the technology in areas where it can contribute massive value and, conversely, trust it too much in areas where it isn’t competent.
  • How to prioritize generative AI use cases: Drawing from examples of great organizations (Wendy’s, Mayo Family Foundation, Walmart, Wayfair, Bloomberg) and research from BCG, McKinsey and more, I unveil my “MT-CAC” acronym to select the right use-cases for enterprise gen AI applications. MT-CAC stands for Multi-Modal, Trusted, Current, Applied, Contextual. In this LinkedIn Live, we also discuss why data quality is in fact your moat and how genAI execution is stuff between FOMO and FOMU right now.
  • Are data leaders set up to fail? A meager 20.6% of executives reported that a data culture had been established within their companies, down from the 28.3% of companies that established a data culture in 2019. It doesn’t seem we’re making progress. What’s really happening? My special guest explains.

The CarCast also includes extras such as: “The latest cybersecurity MAP,” “The future of generative AI in 15 charts” and insights from Netflix cofounder Marc Randolph on what defines a company.

Bruno Aziza is a technology entrepreneur and partner at CapitalG, Alphabet’s independent growth fund.

DataDecisionMakers

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing an article of your own!

Read More From DataDecisionMakers


Author: Bruno Aziza
Source: Venturebeat
Reviewed By: Editorial Team

Related posts
AI & RoboticsNews

DeepSeek’s first reasoning model R1-Lite-Preview turns heads, beating OpenAI o1 performance

AI & RoboticsNews

Snowflake beats Databricks to integrating Claude 3.5 directly

AI & RoboticsNews

OpenScholar: The open-source A.I. that’s outperforming GPT-4o in scientific research

DefenseNews

US Army fires Precision Strike Missile in salvo shot for first time

Sign up for our Newsletter and
stay informed!