Natural Language Processing (NLP) going mainstream
summary
Claims that NLP is crossing into everyday use with no-code/low-code tools that let nonprogrammers build models, concrete deployments in social listening and chatbots that cut call center effort, and an explicit push to fuse internal and external data for unified customer insight. Notes an S‑curve adoption pattern with many organizations still below the ~34% beyond-data‑science threshold. Lists concrete hurdles: systems integration, data linking, operational models, measurement frameworks, and human feedback loops required to make deployed models actually work.
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chapters (6)
- 0:00Start
- 2:11Please use the pollichat function to enter your response)
- 8:29Please use the poll function to enter your response)
- 10:32Business values associated with NLP are very focused around understanding customer needs and experiences
- 12:35Please use the chat function to enter your response)
- 14:38The bad, the ugly, and the good: Implications for the business, your immediate stakeholders, and your team(s) to truly embrace NLP as a new capability
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