Not surprisingly, at Mayfield Fund we are seeing a big wave of Gen AI applications; below are 5 use case themes emerging: 1. Content Generation: LLMs producing custom content for marketing, sales, and customer success, and also create multimedia for television, movies, games, and more. 2. Knowledge CoPilots: Offering on-demand expertise for better decision-making, LLMs act as the frontline for customer questions, aiding in knowledge navigation and synthesizing vast information swiftly. 3. Coding CoPilots: More than just interpretation, LLMs generate, refactor, and translate code. This optimizes tasks such as mainframe migration and comprehensive documentation drafting. 4. Coaching CoPilots: Real-time coaching ensuring decision accuracy, post-activity feedback from past interactions, and continuous actionable insights during tasks. 5. RPA Autopilots: LLM-driven robotic process automation that can automate entire job roles. What else are we missing?
Love this, but all of those themes sound like fast adoptions of GPT APIs which means they're all prompt, no innovate. I'm wondering what they bring that differentiates them from ChatGPT and the eventual "easy buttons" that will pop up all over the web.
For #2 Knowledge CoPilots there are a ton of B2B and B2C Customer Service sub-use cases, depending on the business vertical: 1. Conversational Virtual Assistants 2. Speech Analytics of Sentiments and Topics 3. Human in the loop Intent Training 4. Virtual Agent Workforce Scheduling 5. Predicting Customer Lifetime Value 6. Customer Emotion Detection 7. Customer Segmentation 8. Customer Journey Analytics for Next Best Action Mappings 9. Pricing and Offer Personalization 10. Natural Language Support Document Search And the list goes on and on...... Adding in sequential "actions" such as AgentGPT (breaks down tasks into smaller sub-tasks and utilizes a wide range of online resources and tools to complete them autonomously) and the list gets even longer.
#5 is a kind of big generalization. What are some examples of that?
Love #1, the real challenge with personalization has been the extra requirement on design variants. Hopefully this type of Gen AI will help deliver new levels of relevance.
Patrick I mostly agree. Two points: for #2 I would focus on Knowledge management. The main thing we here is "where can I find XXX doc?" as a question. There's another one I would add and that's Customer Service/Support. Many brands working that angle.
Growth CoPilot
Adding to the list! Testing CoPilot by Roost.ai: test cases are auto-generated using LLMs. This eradicates the often undesirable effort developers must invest in writing them and also ensures 100% of test scenarios are covered. Rishi Yadav https://roost.ai
One additional category is Content Optimization (similar to content generation). Copilots helping to enhance and optimize the content by leveraging LLMs with Generative AI, which is a big use case in our world of contract markup.
Patrick Salyer Product Management Copilot It may look like it falls under #2 Knowledge Copilot but I would call out new category business intelligence copilot which can also comprise of data insights, business intelligence, product management - where multiple sources of customer data insights are analysed to provide insights
Co-Founder & CEO at Stealth
2yThe rise of LLMs for production data processing. Today we use LLMs to process millions of records, whereas just a year ago we would have needed a team of data scientists to create much less accurate NLP models. In my opinion, the variety of insights at scale is the real revolution. However, monitoring, production-grade models, and scaling remain unsolved issues at least for now.