When OpenAI released ChatGPT last year, interest and demand for generative artificial intelligence spiralled. Businesses quickly recognized the efficiency gains that could be made. By employing large language models (LLMs) to handle queries, generative AI can dramatically reduce the time people spend on manual tasks like searching for and compiling information. Businesses that previously dabbled in AI are now rushing to adopt and deploy the latest applications.
ChatGPT has democratized AI beyond the realm of data scientists, and there seems to be a wave of new offerings every month. A generative AI experience is designed to enable intelligent automation and accelerate productivity by simplifying repetitive tasks, increasing agility, and transforming the user experience. It is particularly relevant to CX. Generative AI tools enable companies to digest vast amounts of data and distill it into smart summaries, saving time and providing agents with greater insights.
But how do you harness AI for business advantage in the world of CX? One clear benefit I want to explore is generative AI and summarization…
With the amount of information available to us, it's becoming increasingly difficult to find relevant data. Generative AI summarization is a powerful tool that can help quickly and easily create summaries of any text.
Summarization uses generative AI to read and condense service data, this could include major incident details, product feature request from customer tickets, previous touchpoints, actions taken by agents, and resolutions, to create case summary notes in seconds. Simplifying what was traditionally a manual process enables quicker collaboration between internal teams, increases productivity, and creates more streamlined resolutions for customers and employees. By simplifying an arduous, manual process, employees can now focus on more complex assignments and projects.
This growing demand for generative AI and summarization is backed by a recent study by Valoir that found that AI can reduce the amount of time it takes employees to do their work by up to 40 percent, citing that summarization helps organizations realize near-immediate productivity gains. Its findings pointed out that summarization removed repetitive work, eliminated time spent searching, and significantly improved productivity.
"Generative AI's impact on productivity could add trillions of dollars in value to the global economy…Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed. This would increase the impact of all artificial intelligence by 15 percent to 40 percent. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases," McKinsey wrote in a recent report.
Generative AI, Summarizations, and CX
For many of the most straightforward customer service engagements, generative AI chatbots and virtual assistants have been handling customer queries for years. But there is huge potential for generative AI and summarization to improve the agent skillset experience. Many predict it will revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills.
"Research found that at one company with 5,000 customer service agents, the application of generative AI increased issue resolution by 14 percent an hour and reduced the time spent handling an issue by 9 percent. It also reduced agent attrition and requests to speak to a manager by 25 percent," McKinsey also noted.
Generative AI helps CX professionals search and summarize their existing customer feedback more effectively. Agents need to understand customers' problems and frustrations, and to do so they must review past interactions, which can take considerable time. To help agents more quickly pinpoint problems and understand perspectives, they can use generative AI to summarize previous interactions.
In addition, support managers constantly need to provide feedback to product teams on trends leading to feature requests, engineering teams with static backlogs and success teams with predictions of escalations. Generative AI can make all of this easy, while increasing efficiency.
So there is the agent component on summarization, but there is also the manager component. Through intelligent collaboration with success and product teams, managers can also drive efficiencies. The agent piece is coming along well, but the manager component is frequently missing.
Support managers need to be able to summarize product feedback or feature requests from support data, identify which features and bugs are causing blocks, pinpoint knowledge gaps in documentation, and be aware of trending incidents so they can share this with product and success teams in real time. Previously this could take months to pull together. Now, success teams can get summarized sentiment from every support ticket or conversation, no longer needing to log into a ticket, pull data, and manually tabulate it. Engineering fully understands the impact of backlogs. Summarizing major incidents is a way of alleviating the problem.
Let's says you're an agent trying to resolve a new problem. Logically you'd want to check whether this particular issue has occurred before and has been resolved. Historically, using similarity detection, the agent would be presented with a set of cases or tickets. To make sense of these, she would need to go through entire conversations. Time intensive. With summarization capabilities you can quickly extract details on how the problem was solved, in as little as five simple steps.
If I want to investigate why my customer satisfaction went down in a week, I can drill down and identify whether there is a particular customer segment that has been detrimentally affected. It might be a particular region and you identify 100 conversations on an issue. But how can you help agents not to have to go through each conversation to understand what went wrong?
This is where summarization becomes important. What the agent is really looking for is bad sentiment, so when you summarize you want to look for bad sentiment from the signals you've extracted. It might be performance issues, unusable product, frustration, or disappointment. Summarization enables you to identify specific metrics, such as 30 percent service-level agreement failure in terms of first response or 20 percent escalation rates, both of which are contextual.
The era of generative AI is just beginning. Excitement over this technology is palpable, and early pilots are compelling. But a full realization of the technology's benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining which new skills and capabilities the workforce will need, and rethinking core business processes, such as retraining and developing new skills.
Indeed, generative AI is a step change in the evolution of AI. At a time when many CX teams are being asked to do more with much less, AI can boost productivity, increase efficiency, and be deployed to ensure customers keep coming back. News skill sets are required to understand how to leverage AI effectively on a daily basis, which creates new opportunities.
Remember everybody was fearful of the transition to the cloud. The growth and real-world deployment of generative AI raises similar concerns. In a nutshell, generative AI and summarization enables humans to make faster and more reliable decisions, which is why its impact on the CX industry will be widely felt and welcomed.
Somya Kapoor is CEO and co-founder of TheLoops.