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    Designing AI Programs: How and where to focus resources

    Recorded - available as on demand webcast

    AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers.  Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking.  This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to plague AI projects and reduce the likelihood of success.  The sessions will provide actionable steps using proven processes to improve AI program outcomes.

    In the final session the topic is "what's next?"

    Many organizations have begun their AI journey and need to bring capabilities to the next level. There may be disconnected approaches and decentralized decision making.  Lessons and successful approaches are not leveraged across siloes or repurposed and built upon.  In other cases, projects may have shown value in a pilot, but are being held back from full deployments by various constraints.   

    This final session in our four-part series provides several approaches for:

    • Determining priorities for your efforts
    • Installing metrics to monitor progress and impact
    • Structuring governance and decision making
    • Engaging in appropriate risk and change management

    Be sure to check out all sessions in this series:

    view webcast

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    [RECORDED] Product Data: Insights for Success - How AI is Automating Product Data Programs

    Artificial Intelligence is changing the way businesses interact with their customers. From hyper-personalized experiences to chatbots built on Large Language Models, AI is driving new investment in digital experiences. That same AI and LLM can also be used to automate your product data program. From data onboarding and validation to generating descriptions and validating images, AI can help generate content faster and at a higher quality level to improve product findability, search, and conversion rates. In our second webinar in the Product Data Mastery series, we’re speaking with Madhu Konety from IceCream Labs to show exactly how AI and product data can work together for your business.

    AI’s Value for Product Data Programs

    By Dan O'Connor, Director of Product Data, Earley Information Science

    The Critical Role of Content Architecture in Generative AI

    What is Generative AI? Generative AI has caught fire in the industry – almost every tech vendor has a ChatGPT-like offering (or claims to have one). They are claiming to use the same technology – a large language model (LLM) (actually there are many Large Language Models both open source and proprietary fine-tuned for various industries and purposes) to access and organize content knowledge of the enterprise. As with previous new technologies, LLMs are getting hyped. But what is generative AI?