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CX and AI: From Early Steps to a Decade of Limitless Horizons

eglobalis

This analytical exploration guides executives through current AI advancements, realistic future scenarios, and outlines strategic actions essential to prepare their organizations for a highly automated, AI-driven CX landscape projected by 2035.

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Experience Under Fire: How Conflict Impacts CX, EX, Innovation, and the Future of Technology

eglobalis

Organizations also lean heavily on EX analytics to measure stress indicators—through pulse surveys, attrition risk models, and performance volatility. Governments and organizations turn to AI for threat detection , misinformation control, and public communication. The EX and CX of innovation are no longer optional—they are core.

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Analytics and Insights Teams Build the Foundation for Data-Driven Success

InMoment XI

Analytics teams bridge the critical gap between raw data and business impact, ensuring that companies’ data investments deliver tangible results. What Is an Analytics Team? Analytics teams have evolved significantly from their origins in business intelligence and reporting. What Does an Analytics Team Do?

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From Asking to Knowing: How AI Is Replacing B2B Customer Surveys—Not If, but When

eglobalis

The rise of advanced analytics and AI is presenting an alternative: rather than asking customers for feedback at every turn, leading firms are beginning to know what customers think by analysing behaviours, conversations, and data signals in real time.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. However, as data volumes and complexity continue to grow, effective data governance becomes a critical challenge.

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Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks.