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Augmenting KM with AI: 2024 trends, impacts, and solutions

There are a few key principles that shape the world of modern knowledge management. Among those tenets is rapid, democratized access to information—which, as evidenced by Unisphere Research’s 2024 Survey on Information Discovery, 92% of enterprise leaders agree that access to fast, accurate information from unstructured content is vital to their business.

At the forefront of revolutionizing this rapid access to information and knowledge-based work is AI, a formidable conduit of KM advancement that—with the necessary guardrails in place—can catapult organizations' knowledge strategies into a modernity defined by efficiency and ease.

Joe McKendrick, author and analyst for Unisphere Research, and Jason Zhou, VP of solutions, services, and customer success at Pryon, joined KMWorld’s webinar, Knowledge AI in 2024: Trends, Impacts & Predictions, to examine how AI can be effectively used to extract knowledge from enterprise content, acknowledging both its barriers and crucial strategies.

McKendrick pointed to the fact that “knowledge friction,” or the significant distance between what individuals need to know within their organization and their ability to access that information, is the main theme for many enterprises’ states of KM.

According to Unisphere Research’s survey, 70% of respondents reported spending an hour or more looking for a single piece of information, whereas nearly a quarter (23%) reported spending more than five hours per information piece. As Zhou explained, this knowledge friction is because content authors and content consumers are fundamentally disconnected. Content is scattered across silos and formats, forcing workers to waste significant amounts of time looking for the answers to their queries.

What is the true cost of knowledge friction? Fairly extensive, according to the speakers, where the impacts of stalled information access include the following:

  • Higher operations costs
  • Lost revenue
  • Customer churn
  • Conflicting/duplicative/incorrect information
  • Poor decision making
  • Siloed information
  • Data gaps and safety risks
  • Security and privacy issues
  • Supply chain disruption
  • Poor client outcomes
  • Unempowered/ill-equipped employees
  • Loss of customer trust

To tackle this detrimental reality of KM, many enterprises are looking to AI for help. As the survey found, 84% of respondents reported that they anticipate that AI will boost productivity within the next year, with half expecting this boost to be significant, exceeding a 20% increase in productivity.

However, despite AI’s promise, it comes with concerns. Seventy percent of organizations reported that data privacy and security challenges are the main obstacles to AI adoption, with close to two-thirds reporting that they worry about the accuracy of the results produced by AI.

McKendrick and Zhou offered an insightful conclusion: AI, paired with a well-governed and well-executed discovery strategy, will bring greater insights to decision makers at all levels. To achieve this, enterprises need a system that will:

  • Ingest multi-modal content from wherever it resides
  • Extract useful knowledge from the content
  • Provide accurate, user-based answers
  • Implement access controls while maintaining security and confidentiality

Pryon’s platform delivers these capabilities to empower organizations to smooth knowledge friction, eliminating the distance between critical information and those who need it most. Split into two key components—an ingestion and exchange engine—Pryon accesses, reads, and understands in-scope content like a human would while simultaneously understanding users’ queries and rapidly searches through ingested content to surface an answer.

Pryon delivers the following capabilities:

  • Ingestion and content capture engine that accesses customer’s content sources with read-only access
  • Optical Character Recognition (OCR) engine that reads each document to identify and label key components
  • Content normalization and interpretation that prioritizes more meaningful text and converts that text into machine-interpretable vectors that reflect content meaning
  • Query processing engine that translates user queries into machine-interpretable vectors that reflects the meaning behind the query
  • Content matching engine that rapidly matches the user query to all ingested content, returning highly accurate responses
  • Display engine that returns the response text, contextual text, and metadata so that it’s easily interpreted by the user

To view the full webinar discussion about knowledge management and AI, you can view an archived version here.

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