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    Recorded - available as on demand webcast

    Knowledge management (KM) and artificial intelligence (AI) have both gone through booms and busts—periods of hype followed by a sobering dose of reality.  After an “AI winter,” AI is currently enjoying an “AI spring,” because of a range of new applications driven by availability of training data, progress in algorithm performance, computing power and new funding.  There is also a growing understanding that cognitive applications of AI are trained in much the same way as humans.  The two work hand in hand.  Therefore, the same resources can be applied to preparing that information for AI that will solve problems even if AI is not your primary objective.  AI training content for cognitive systems such as knowledge retrieval bots, semantic search, intelligent virtual assistants, etc., should be designed to be reusable across multiple systems and platforms. 

    A well-integrated knowledge engineering approach solves immediate knowledge access by humans, while laying the foundation for an AI-powered future. Organizations will compete on their knowledge about customers, products, solutions, and technologies embodied in AI tools and systems. Making the investment in the foundation for knowledge management will pay off in the short term, as well as prepare your organization for the future.     

    Join us to explore these topics and more  

    • The role of KM in AI
    • Understanding Knowledge Engineering (KE) and how it is different from Knowledge Management (KM)
    • Ways to design training data and content for both humans and AI
    • Approaches for targeting processes that will provide the clearest ROI

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    Seth Earley
    Seth Earley
    Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.

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    Improving product data quality will inevitably increase your sales. However, there are other benefits (beyond improved revenue) from investing in product data to sustain your margins while lowering costs. One poorly understood benefit of having complete, accurate, consistent product data is the reduction in costs of product returns. Managing logistics and resources needed to process returns, as well as the reduction in margins based on the costs of re-packaging or disposing of returned products, are getting more attention and analysis than in previous years. This is a B2C and a B2B issue, and keeping more of your already-sold product in your customer’s hands will lower costs and increase margins at a fraction of the cost of building new market share. This webinar will discuss how EIS can assist in all aspects of product data including increasing revenue and reducing the costs of returns. We will discuss how to frame the data problems and solutions tied to product returns, and ways to implement scalable and durable changes to improve margins and increase revenue.