Ryan Welsh, Founder and CEO of Kyndi, recently compiled a list
of trends that he believes will help customer service professionals practically deploy GenAI and make it effective for users.
Almost every organization is evaluating generative AI (GenAI) solutions and trying to figure out how to leverage the technology to drive business value today, rather than waiting for the future. The integration of GenAI solutions has become a strategic imperative for organizations seeking to stay competitive and deliver exceptional customer experiences. GenAI, powered by sophisticated natural language processing (NLP) and machine learning algorithms, holds the potential to automate tasks, provide insights, and enhance decision-making processes for customer service professionals and beyond.
In 2023, organizations learned that GenAI can help them achieve a breakthrough search experience to existing and prospective customers by delivering the right answers to their product and service-related questions, via a search or Chatbot interface for both customers and support agents. It also is a critical enabler to helping improve customer satisfaction and retention, reduce case escalation, and ensure a positive customer experience.
As a result, organizations are under pressure from customers, executives, and boards to “do something,” but what? With so many possibilities, all of which sound enticing, where do they start? Where shall they fall along the scale between jumping in with both feet to adopting the more conservative “wait and see” approach? With this in mind, expect to see the following trends in 2024:
Businesses will learn that adding GenAI to existing tools will not address foundational weaknesses in delivering relevant content.
While GenAI can provide valuable assistance, it cannot miraculously solve foundational issues related to searching through volumes of content and/or determining the relevance of results. If an existing tool was unable to reliably surface relevant information immediately ten months ago, bolting GenAI onto any of these offerings will fail to make them work better. Similarly, if a solution did not effectively answer questions previously, the mere addition of GenAI would not change its performance. Put simply, when it comes to GenAI and one’s ability to answer questions effectively, garbage in produces garbage out.
In 2024, implementations of Retrieval Augmented Generation (RAG) will emerge as the only possible way to successfully eliminate hallucinations which is when a generative AI model generates inaccurate information as if it were correct. AI hallucinations are often caused by biases in training data and algorithms, and can result in delivering inaccurate (and potentially harmful) results. As an AI framework, RAG attempts to provide a narrow and relevant set of inputs to GenAI to yield an accurate and reliable summary. However, the successful execution of this framework is no easy task and consequently not all instances of RAG are created equal. For instance, if RAG yields pages of results that may or may not be accurate and defers the task of deciphering the correct answer to GenAI, the outcome will once again be subpar and unsuitable for business use.
GenAI faces the same challenge as a human would in trying to summarize ten pages of relevant and irrelevant data. In contrast, both GenAI and humans do a much better job synthesizing ten relevant sentences. Furthermore, RAG alone can still fail to surface highly accurate answers when it comes to answering questions containing domain-specific context. Boosting the result’s relevance requires last-mile fine-tuning of the LLM. The combined RAG + fine-tuning approach will help achieve production-level performance of the GenAI solution for companies next year.
Input from human experts will make or break GenAI success.
Without a doubt, GenAI is a major leap forward; however, many people have wildly overestimated what is actually possible. Although generated text, images, and voices can seem incredibly authentic and appear as if they were created with all the thoughtfulness and the same desire for accuracy as a human, they are really just statistically relevant collections of words or images that fit together well (but in reality, may be completely inaccurate). The good news is the actual outputs of AI can be incredibly useful if all of their benefits and limitations are fully considered by the end user.
As a result, 2024 will be the year organizations insert human subject matter experts to overcome the limitations that GenAI and large language models can bring to their business. Most organizations have a deep bench of individuals who have amassed an immense amount of valuable knowledge over the years. These individuals play a key role in providing other employees and customers with quick and accurate answers to their complex questions. However, with the increasing amount of information and knowledge available today, harnessing this wealth of knowledge effectively is a significant challenge. Many AI approaches suggest using automation to replace human-curated knowledge, arguing that it saves time and money while accomplishing the same goal. However, in the coming year, content and knowledge experts will play a critical role in ensuring targeted and relevant content is available to customer service professionals and answers are distributed to audiences based on their specific question.
GenAI will streamline new employee onboarding for customer service and knowledge management teams.
Organizations continually cope with employee turnover and retirements in a labor-constrained environment. As a result, they are constantly working to hire and onboard new employees and knowledge management teams must deal with this influx. The problem is that these new hires often struggle to navigate complicated and confusing company processes, policies, and specific language used throughout the organization. Existing learning systems frequently fail to surface the right information to answer new hires’ questions. Simultaneously, new employees and customer service professionals will likely not know the right way to phrase a question to obtain the information they need when they search for answers hidden in the training materials.
GenAI, coupled with answer engines, are emerging as a solution to accelerate this process significantly. In 2024, organizations will increasingly leverage GenAI and answer engines to dramatically improve this process. Using these technologies, employees can ask questions in their own words, eliminating the need to master keywords and domain-specific terminology upfront. Incorporating answer engines into the onboarding process ensures that individuals become productive contributors to the organization at a much faster pace.
About the Author
Ryan Welsh is Founder and CEO of Kyndi, a global provider of the Kyndi Generative AI Answer Engine.