Hurdles to Overcome in AI
Last year, I stumbled upon two successful business models—Chorus and Gong— which provide suggestions to salespeople while they are having conversations. These products listen into conversations a salesperson has with a potential customer. They identify pivotal moments in conversations and allow salespeople to easily follow up with customers and close more deals.
Within 10 minutes of first finding these products, it was obvious to me that is how people will interact one day—that is, we'll use conversational intelligence (an industry within artificial intelligence) to better connect with one another.
More recently, I spent 25 minutes brainstorming with my friend and AR expert Tony Morales. Below is the results of our short brainstorm.
[:00-:05] Top problems facing conversational intelligence today
| Challenges |
|---|
| Lexical categorization |
| Speech to text not 100% |
| Speaker recognition in early phases |
| Microphones in loud environments |
| Relatively quiet environment is required |
| People don't want to be recorded all the time |
| Augmented reality is not mainstream |
| Most AR devices do not have microphones |
| Trouble recognizing niche keywords, terms, abbreviations |
| Concern about privacy, data, storage, and how it will be used |
| Schools/healthcare systems might not adapt if accuracy is low |
[:05-:10] Prioritize the largest problems & turn into an opportunity
Trouble recognizing niche keywords, terms, abbreviations → How Might We build a conversational intelligence system in a way that's valuable while not being 100% correct and not having 100% coverage?
[:10-:15] Identify solutions to the challenge/opportunity
| Solutions |
|---|
| Pick an niche industry with lower barriers to entry |
| Pick an industry that CIQ is a "nice to have" |
| Help people better their relationships with others by providing generic suggestions |
| Use existing lexical analysis tools to find one that's great |
| Take an existing AI tool and use it for a different industry |
| Save audio for later analysis and backfilling |
| Target fun, frivilous, or ancillary use cases |
| Prompt users at the start and end of a conversation |
| Focus on transcribing smaller sections but at higher accuracy |
| Build a professional development tool that addresses people's learning skills |
| Listen into Teams/Zoom calls and provide feedback based on call |
| Create a single script from multiple devices |
[:15-:20] Prioritize the best solutions
[:20-:25] Make an action plan
Lastly, we designed a quick 2 week experiment for the low-effort, high-impact solutions. Feel free to reach out if you're interested in our findings!
- Review Zoom/Teams API for transcription strategy
- Build 2-3 sustainable business models for Zoom/Teams listen-in product
- Test with 5-7 potential customers and log feedback