Use Cases: Key Considerations [03:35]
Generative AI is probabilistic, offering varied outputs for the same inputs. While this can be challenging and disorienting, there are a number of use-cases where this can be an advantage.
Use Cases: External and Internal Examples [5:57]
Internal use cases for AI often focus on productivity and creativity, including by automating manual tasks, speeding up content creation, and helping employees with tasks outside their immediate expertise.
External use cases for AI are more varied and dependent on your business; consider enhancements or extensions of your current products and services that have the potential to build long-term value and differentiated experiences for customers.
Using Large Language Models: Buy vs. Build Strategies [11:08]
Many companies will likely use generative AI via third party software. For those looking to build custom experiences, it’s important to understand the spectrum of build vs. buy options to decide on the approach that aligns with your business needs.
Getting Started: Balancing Top-Down and Bottom-Up Approaches [14:05]
Incorporating generative AI requires a balance between top-down guidance from leadership and bottoms-up experimentation from teams. Experimentation and proof of concepts are crucial in the early stages to determine valuable use cases.
Cost Considerations [16:43]
The costs associated with integrating generative AI technologies into your business operations vary significantly depending on your use cases and the models or applications you employ, or resources (including talent) your organization requires.