How will GenAI advancements disrupt traditional DocAI or Intelligent document processing landscape? Are there any emerging trends? Which use cases might be impacted?
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GenAI is definitely creating strides in document processing. In my org, we are working on Incidents discovery based on OpenSearch and LLM. It's very promising and augment Care applications.
It charging big times. We are working on VLM based transformers to completely transform the document data extraction process. We see big improvements over traditional methods and able to customize the same for our business needs
Generative AI per se really got about as far as it could. Breakthrough in document generation will come from the evolution of comsposite agentic AI (lately often referred to as "agentive AI"). With the help of complex tool calls and context augmentation driven from knowledge graphs, quality of boilerplates and their relevance will be boosted significantly.
Further improvement will come with proliferation of domain-specific models (DSLMs), which will become more and more quantized and useful. That would improve quality of the generated content pre-population, likely to the point of making it superior to even a high quality human origin content.
Context and relevance - these will be the drivers of doc gen evolution, overall.
Let me cite former HPO CEO Lew Platt who should have stated the following sentence: “If HP knew what HP knows, we would be three times as profitable”.
I see a lot of potential in resurrecting developed but currently (on file-share servers) buried domain knowledge that's lost as the corresponding employees retired or have left the company for other reasons.