Keynote: Revolutionizing KM with AI & Document Understanding #KMWorld


Speaker: Paul Nelson, Innovation Lead, Search & Content Analytics, Accenture

Session Description: Intelligent document understanding is drastically changing the search and knowledge management landscape thanks to AI technologies. As 80% of all enterprise data is unstructured, document understanding delivers tangible benefits across industries and business functions saving time, money and resources. Paul highlights how one of the world’s leading pharmaceutical companies leveraged this solution, along with innovative AI technologies and assets, to automate KM for the purpose of detecting sensitive IP within unstructured enterprise content. He shares how this solution is helping other clients, including Accenture, to improve compliance and risk management, increase operational efficiencies, and enhance business processes.

[These are my notes from the KMWorld Connect 2020 Conference. Since I’m publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]


  • He will focus on the three practical KM scenarios to make abstract AI real
  • Scenario #1: Customer Support — we started with individual customer support representatives who use their knowledge to respond to customer request, then we documented the usual queries and responses (FAQs), then we added some search for efficiency. But as the system scaled, we needed a document management system to organize all then content. Then, we superimposed AI to monitor data streams, summarize the patterns, and determine what topics should be addressed by technical writers and included in the document management system. But, with the advent of a new neural network (GPT-3), which can recognize new tokens, now we can replace a technical writer by a robot writer to create a fully automated customer support system.
  • Scenario #2: Research Reports — initially we relied on individual researchers to run tests and produce research reports. But sometimes, this resulted in duplication. So we interposed a gatekeeper who received research requests, checked to see if the work has already been done, and then either distributed the earlier report or commissioned a new report. Now, a robot can replace the gatekeeper. It can understand the incoming request, search the database, understand the content of the research reports, and send the appropriate responsive report — even if it contains words that are different from those in the initial request.
  • Scenario #3: Proposal Writing — how do you respond to RFPs or RFIs? No one person knows enough to respond to the entire proposal request by themselves. Currently, various experts complete the section of the proposal related to their expertise. To increase efficiency, we have tried to create templates and snippets that can be used as necessary. Now, the proposal writing is happening in collaborative sites. And key sections are tagged with relevant data. Going further, you can slice and dice the sections into Semantic pieces that can be sent into a semantic search of a neural network to find relevant information that can be reused automatically. Further, the search can identify cross-correlations, which will find the pieces that usually go together for a specific kind of proposal. After the proposal has been assembled and sent to the potential customer, the system can use cross-correlation between the proposals and the CRM system to determine which proposals were successful and which were not.
  • Closing Remarks
    • AI is more than just classification and entity extraction
      • It can find new topics
      • It can connect content by meaning — Semantic Search
      • It can evaluate content quality
      • It can summarize and rewrite
    • Successful projects will improve existing knowledge flow
      • [This is similar to the old KM adage: pave the cow paths]
      • Don’t fight the existing process. Instead, work within it.
    • Successful projects will include targeted AI
      • Focus on solving the problems you can solve today via AI
      • Gather the data you need to solve the problems of the future
    • You still need a great search engine
      • The results of AI feed and enhance the search engine
      • Search engines are the only way to scale