Thanks for all the comments and resources. I’ve gone down quite the rabbit hole this morning digging through this. The way I’m thinking about my current goals in learning more about this:
- I strongly concur with the now well trod opinion that AI is unlikely to supplant most jobs, but rather will carve off pieces of jobs until the “uniquely human” components remain. For example in a customer service setting, AI will take on the role of transcriber, summarizer, fact recaller, etc.
- AI can recognize patterns and provide decision support. Continuing the customer service example, if a user calls in to diagnose a syncing problem with their file sharing system, AI can aggregate and present “cases” recorded in past support issues to prime the call center associate to recognize an issue that’s been solved before and offer avenues to attempt a resolution
- Over time, many of these inbound calls would become relatively rote and could eventually be fully automated; for the exceptions, that’s where CTA/NDM starts to come into play. In our call center example, deep troubleshooting becomes the core skill in play, and as discussed in this thread (https://forum.commoncog.com/t/troubleshooting-as-a-perennial-skill/2574) it’s where humans beat AI
- Our problem has now shifted from one of “capture all the information in a KMS” to “identify the uniquely human skills/scenarios in a job, and codify how to accelerate expertise in these skills”
I still feel like there’s something there with GenAI/LLMs in particular - could a human/AI pair to complete CTA at scale more effectively?