By 2026, deploying Natural Language Processing (NLP) in businesses will be significantly faster and more cost-effective, shifting from custom model training to API calls with prompt engineering. This evolution enables previously unviable NLP use cases to become profitable, with contract review, support ticket triage, and sales call summarization showing the highest return on investment. Retrieval-Augmented Generation (RAG) remains crucial despite larger context windows, primarily due to cost and accuracy considerations for retrieving specific information. AI
IMPACT Accelerates enterprise adoption of NLP by reducing deployment time and cost, making advanced AI capabilities more accessible.
RANK_REASON Article discusses future trends and use cases for NLP in business, rather than a specific release or event.
- Bert
- Equal Employment Opportunity Commission
- EU AI Act
- natural language processing
- retrieval-augmented generation
- T5 Text To Text Transfer Transformer
- Whisper API
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