ServiceNow is making significant strides in infusing AI throughout its company, with over 25 generative AI use cases already in production. The company has developed an AI roadmap for every department, integrating generative AI into various areas such as software engineering, HR, customer service, marketing, sales, and financing. As a result, an impressive 84% of ServiceNow's workforce uses generative AI on a daily basis. The company's investments in AI have already yielded positive outcomes, including the equivalent work of 50 full-time employees within four months and a 10% net reduction in work for the customer support call center. ServiceNow believes that generative AI will become an essential part of businesses in the future, and it has integrated generative AI assistants, ServiceNow Now Assist and Microsoft Copilot, to enhance employee productivity. Despite these advancements, ServiceNow maintains its previous sales outlook for 2026. Chief Customer Officer Chris Bedi emphasizes that generative AI is not intended to replace jobs but rather to assist employees in performing their tasks more effectively and reducing manual work.
In addition to infusing AI throughout the company, ServiceNow has launched an AI solution designed to revolutionize skills development, learning opportunities, and professional growth. The AI solution analyzes employee performance, skill sets, and career aspirations to provide tailored recommendations for training and advancement opportunities. It also helps organizations identify talent gaps, align training programs with strategic objectives, and make informed decisions about resource allocation. ServiceNow's AI solution offers personalized learning experiences, continuous feedback and assessment, and real-time insights into operational performance metrics. It can be easily integrated with existing HR systems to streamline processes and enhance efficiency. The future potential and expansion of AI solutions in platforms like ServiceNow are expected to drive efficiency, foster innovation, and create a more skilled and adaptable workforce.
Contact centers are also leveraging AI and generative AI to improve operational efficiency and customer service. The two main types of contact center AI are conversational AI and data analysis. Conversational AI uses large language model (LLM) algorithms to enable voice and text-based interactions with customers, while data analysis AI sifts through statistics and KPIs to make performance improvement suggestions. The five popular contact center AI features are interactive voice response (IVR) systems, self-service chatbots and virtual agents, real-time agent coaching and performance monitoring, automatic call insights with predictive analytics, and AI-generated transcription, call, and chat summaries. These features enhance customer engagement, reduce wait times, improve agent performance, provide context and recommendations, and analyze customer interactions for future sales opportunities.
In today's customer service landscape, businesses are using sentiment analysis to gauge customers' sentiment and improve the overall customer experience. AI and ML technologies have revolutionized sentiment analysis, allowing contact centers to analyze vast datasets from diverse communication channels in real-time. Sentiment analysis can be coupled with the reason for contact to gain more depth in insights. It helps identify upstream issues, improve performance monitoring and coaching, and address ethical considerations. Contact centers must embrace sentiment analysis as a strategic imperative for driving customer engagement, satisfaction, and loyalty. Advancements in AI and ML technologies will provide even more sophisticated tools for analyzing and interpreting customer sentiment in the future. [bd3b74a1]