Generative Artificial Intelligence (Gen AI) brings forth transformative possibilities across various industries, each responding to this technological wave with unique challenges and opportunities. To understand this better, Everest Group, supported by WNS, surveyed Customer Experience Management (CXM), digital transformation and innovation leaders from 200 global companies (primarily) in the telecom and media, Banking, Financial Services and Insurance (BFSI), healthcare, retail, and technology and hi-tech industries.

This blog presents vital insights from the research report, delving into the nuanced landscape of enterprise awareness, perceived potential and preparedness for Gen AI adoption across different sectors.

Enterprise Awareness and Perceived Potential

The study reveals a spectrum of awareness across industries. While technology and hi-tech, along with telecom and media, lead the charge with a staggering 90 percent awareness, other sectors such as BFSI, healthcare and retail display varying degrees of understanding.

Telecom and media, technology and hi-tech, and retail sectors shine in recognizing the transformative potential of Gen AI’s text generation capabilities for CXM operations. BFSI places its bet on code generation capabilities, while healthcare and technology and hi-tech lead in foreseeing the impact of image and video generation. However, the retail sector is more cautious, with less than 60 percent expressing high expectations for certain Gen AI applications.

Enterprise Preparedness to Adopt Gen AI

The readiness landscape varies significantly among industries, encompassing technology, data, people, process, change management and previous experience with transformative technologies.

  • BFSI: BFSI stands out as a sector well-prepared across technology, people, process and change management. Despite talent readiness challenges, particularly in AI / Machine Learning (ML) engineering and software development, robust cloud infrastructure and experience with transformative technologies position BFSI for Gen AI adoption.
  • Healthcare: Healthcare mirrors BFSI in overall readiness but faces challenges such as insufficient computing power, high-quality training data scarcity and talent shortages. Talent readiness, specifically in AI / ML engineering and data science, remains a notable concern.
  • Retail: The retail sector lags in technology, people, process and change management readiness for Gen AI. Challenges include computing power limitations, talent shortages and obstacles in redundancy and failover measures. However, strong data storage and extraction capabilities and past transformative technology experience provide a foundation for growth.
  • Technology & Hi-Tech: Highly prepared for Gen AI adoption, the technology and hi-tech sector excels across key parameters, including technology, data and process readiness. Skilled talent availability sets this sector apart, although experience in implementing digital CXM solutions needs to catch up to other industries.
  • Telecom & Media: Telecom and media emerge as the leader in Gen AI adoption preparedness. With robust data capabilities, change management processes and prior digital CXM experience, this sector is well-positioned for organizational transitions. Talent scarcity and infrastructure-related improvements represent areas for growth.


The Gen AI landscape is dynamic, with each industry navigating its unique challenges and capitalizing on distinct opportunities. As enterprises embark on this transformative journey, understanding industry-specific nuances is crucial. This insightful report by Everest Group, supported by WNS, sheds light on the diverse paths industries tread in their pursuit of Gen AI adoption, creating a roadmap for strategic decisions and fostering collaboration to shape the future of AI.

Generative AI in CXM: Assessing Enterprise Readiness for this Disruptive Transformation

Dive into this comprehensive report for the full spectrum of insights and perspectives on the state of Gen AI adoption across industries and enterprises.

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