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Are you behind when it comes to generative AI?

VentureBeat/Ideogram
VentureBeat/Ideogram

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The AI landscape is evolving at breakneck speed, and it’s making it really hard for CIOs, data an AI executives to prioritize what to pay attention to.

In this week’s CarCast, tech entrepreneur Bruno Aziza reviews the latest insights from Scale AI’s AI Readiness Report and highlight the key questions every executive should know to ask about gen AI.

Highlights from the AI Readiness Report:

  • Adoption: Last year, 19% of companies had no plans for gen AI. This year, that’s dropped to just 4%.
  • Production: Last year, 21% were in production. This year, 38% are in production.
  • Challenges: The No. 1 barrier people run into when deploying gen AI? Security and governance.

Questions you should know to ask about gen AI:

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  • How should you identify the right gen AI use cases to pursue?
  • How should you budget for gen AI?
  • When should you NOT use gen AI?

What do people use generative AI for?:

There are 3 ways to think about gen AI use cases: Internal customers, external customer and embedding in existing applications. 

  • Internal customers. This is a fairly low risk and high reward equation. This could be about making your data better or supercharging the performance of your people across all disciplines: creation of content for marketing/sales, code for developers or summarization for finance, administration and customer support reps. A good example is how Twilio uses gen AI to help reps find answers faster or summarize calls after the fact.
  • External customers. Think chatbots for customer support or “gen AI in context,” such as with Wayfair Decorify, where you can upload a photo of your living room and the application can provide relevant and available Wayfair inventory to ‘dress your living room.’ 
  • Embedding of gen AI capabilities into existing applications. This is particularly powerful when you have very targeted use-cases that rely on applications that you’ve used or built over time.  ERP, HCM or CRM applications are prime examples. Here’s what’s interesting: With these applications, data might not be particularly wide or large, but its value and sensitivity is extremely high, so remember that as the selection criteria for these use cases might be different.

Bruno Aziza is a technology entrepreneur.