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Artificial Intelligence for CX Applications

Case Studies, Market Drivers, Market Barriers, and Best Practices for the Adoption of AI Within CX Platforms and Applications

Artificial intelligence (AI) has become nearly ubiquitous across a wide range of industries and use cases, and within the CX discipline, AI functionality is no different; AI is increasingly being integrated or incorporated into CX platforms and applications. AI functionality is being integrated or incorporated into CX platforms and applications, with low- or no-code interfaces that allow CX, marketing, and sales professionals with little data science or computer coding experience to manipulate data and tune algorithms to serve several different functions. Many organizations have already seen the benefit of deploying AI across customer-facing functions and in back-office systems to support applications including the generation of intelligent insights, predictions, customer preferences, next-best-action recommendations, and the support of higher levels of automation.

AI heavily relies on the capture, organization, and activation of customer data, processing the data and capturing various aspects of interactions with customers. As more data is captured and processed, more complex algorithms or combinations of algorithms can be deployed, resulting in greater value and a greater return on investment (ROI).

This Dash Research report focuses on the market drivers and barriers surrounding the adoption and use of AI in CX platforms, applications, and programs, the general use case categories for AI, and several representative case studies detailing the use of AI to improve CX. The report also details current AI regulations, which generally focus on the proper collection and use of personal information.

Key Questions Addressed:

  • How are companies using AI to support their CX initiatives?
  • What are the key drivers of AI adoption for CX applications and platforms?
  • What are the key functions within CX that AI can support or enable?
  • What barriers exist that may hinder the adoption of AI within CX platforms or applications?
  • What are the key underlying technologies used in AI?
  • What are the relevant regulatory issues of which CX professionals using AI should be aware?
  • What are some examples of AI being utilized in the real world?

Who Needs This Report?

  • CX practitioners
  • Marketing/sales managers
  • C-suite and strategy directors
  • IT integration specialists
  • Logistics specialists
  • Contact center managers
  • Investor community

Table of Contents

  1. Executive Summary
    1. Introduction
    2. Market drivers
    3. Market barriers
    4. Dash Research insights
  2. Market Overview
    1. Introduction
      1. Use case categories
        1. Intelligent insights
        2. Predictions
        3. Preferences
        4. Recommendations
        5. Automation
    2. Commercial integration of AI
    3. Market drivers
      1. Increasing demand for customer-facing automation and assistants
      2. Higher demand for backend automation and intelligent analysis
      3. Growing appetite for data-led insights and customer journeys
      4. More value seen with deeper customer engagement
    4. Market barriers
      1. Limited scope or quality of data
      2. Lack of alignment between CX challenges and AI solutions
      3. Limited data governance policies and privacy concerns
      4. Algorithm management issues
    5. Regulatory issues
      1. AI algorithm regulation
      2. State data privacy laws
      3. State data security laws
      4. State breach notification laws
  3. Case Studies
    1. Customer-facing case studies
      1. Netflix Recommendation Engine
      2. N26: Using AI to power a virtual assistant
      3. Kiwi.com: AI assistants for travel services
    2. Back-office case studies
      1. UPS: Using AI to improve logistics
      2. Cresta: AI-driven coaching
  4. Best Practices
    1. Develop a data-centric culture
    2. Eliminate data silos
    3. Use AI to support and augment human efforts
  5. Acronym and Abbreviation List
  6. Table of Contents
  7. Table of Figures
  8. Appendix
    1. Scope of study
    2. Sources and methodology
    3. Copyright notice

List of Figures

  • AI Maturity and Data Integration Depth
  • Predictive Modeling Using Machine Learning
  • A Typical Online/Offline Customer Journey Map
  • Data Observability
  • Netflix Recommendation Engine
  • N26 AI Assistant
  • UPS ORION System

Author Information

Keith has over 25 years of experience in research, marketing, and consulting-based fields.

He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.

In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.

He is a member of the Association of Independent Information Professionals (AIIP).

Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.

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