Nearly 75% Enterprises Pivoted to Text-Based Generative AI to Improve Operational Efficiency
According to the recent “Generative AI in CXM Survey Report” by Everest Group and WNS, 75% of enterprises are elevating their business strategies by piloting, deploying, or scaling up text-based generative AI solutions, followed by 62% for code generation and 52% for image generation. The potential of this is widely recognised, with more than 90% of enterprises believing in the high potential of text generation, and around 70% for code and image generation. In terms of deployment, about 75% are piloting, deploying, or scaling up text generation solutions, and nearly 60% are doing the same for code generation. Growth Drivers The generative AI adoption in customer experience (CX) operations is driven by the need to enhance customer satisfaction and operational efficiency. Enterprises are increasingly integrating tech into CX management to personalise and customise customer interactions by understanding individual preferences and behaviors. This leads to more engaging and gratifying customer experiences. “When generative AI tailors interactions to individual preferences, it improves operational efficiency by automating tasks and intelligent tools like agent assist and language translation. This helps improve productivity, reduces response time, and lets agents focus on value-added tasks, ultimately resulting in more satisfying customer experiences,”  Sanjay Jain, chief business transformation officer, WNS, told AIM. Furthermore, it boosts efficiency within CX operations. It equips customer support agents with intelligent tools such as agent assist, next-best-action recommendations, language translation, cross-sell, up-sell, and accent neutralisation. Another driver is its ability to analyse vast amounts of customer data, enabling enterprises to extract meaningful insights for competitive advantage. This facilitates data-driven decision-making for strategy development, product improvements, and service enhancements. However, 45% of enterprises report a shortage of internal technical expertise in generative AI which hinders their ability to come up with new models. Talking about the AI talent gap, Jain said that investing in strategic talent acquisition and development is crucial for maximising the potential of AI in CXM. “Enterprises need to focus on recruiting AI and ML engineers, data scientists, and software developers, especially those with a mix of AI and software development skills to enhance teamwork and communication. Additionally, ongoing training programs are vital to ensure that current employees are upskilled,” said Jain. He also added that AI, despite fears of job displacement, acts as an enhancer rather than a substitute for human skills. “While AI excels in accuracy and speed for execution, human critical thinking is pivotal for oversight,” added Jain, highlighting that this symbiotic relationship promises long-term benefits. Key Findings of the Report The survey highlights the expanding role of tools like OpenAI’s ChatGPT, Google’s Bard Gemini, and Microsoft’s Copilot in business transformation. These advancements are driving a significant shift in CXM, with generative AI’s content generation, data analysis, and insight extraction capabilities leading the way. Key findings from the survey cover the awareness, perceived potential, and deployment areas for generative AI applications. It also covers the technology, process and enterprise readiness for foundational models. Everest Group’s research included over 200 companies from diverse sectors such as telecom, media, BFSI, healthcare, retail, and technology, including FGT/Hi-Tech industries. These companies, predominantly from regions including Asia Pacific, have annual revenues exceeding $500 million. Awareness and perceived potential of generative AI applications Text generation capabilities: Over 75% of enterprises have high awareness. Code generation capabilities: 62% awareness. Image generation capabilities: 52% awareness. Over 90% of enterprises believe in high potential for text generation. About 70% see high potential for code and image generation capabilities. Planned Deployment Areas Text generation capabilities: About 75% of enterprises actively piloting, deploying, or scaling up solutions. Code generation capabilities: Nearly 60% of enterprises have initiated pilot programs or implementation. Readiness for Generative AI Nearly 50% of enterprises are concerned about having sufficient computing power. Over 70% say they have adequate cloud capabilities. Approximately 40% express concerns about the availability of high-quality training data. Over 60% express significant concerns regarding data security. More than 45% of enterprises report a shortage of internal technical expertise. Over 70% of BFSI and healthcare sectors noted regulatory compliance issues. 40% identify cultural inertia as a major obstacle. Only two-thirds have adequate capability for redundancy and failover measures. Enterprise Readiness for generative AI by Industry BFSI: Approximately 60% prepared across technology, people, process, and change management. Healthcare: 60-70% readiness across technology, data, process, and change management. Retail: Least prepared, with key challenges in computing power, training data, and talent. Technology and FGT/Hi-Tech: Over 60% significantly ready across key parameters. Telecom and media: Approximately 65% highly ready across favorable parameters for generative AI implementation. Read more: Data Science Hiring Process at WNS
The WNS report states that over 45% of enterprises identify the lack of internal technical expertise as a major barrier to generative AI implementation.
The WNS report states that over 45% of enterprises identify the lack of internal technical expertise as a major barrier to generative AI implementation.
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Shritama (she/her) is a technology journalist at AIM who is passionate to explore generative AI with a special focus on big techs, database, healthcare, DE&amp;I, hiring in tech and more.