EACH CHANNEL POSES UNIQUE REQUIREMENTS FOR CONTENT PRESENTATION , CALLING FOR A CUSTOMIZED APPROACH ...
• Is written and formatted in a way that is useful ( e . g . not walls of text )
• Uses correct terminology
Despite the inherent importance of these endeavors , the traditional reliance on conventional document-based systems has gradually evolved into a significant impediment to the evolution of KM . These legacy systems often struggle to adapt to the dynamic demands of the digital age , impeding progress in knowledge sharing , access , and delivery .
EACH CHANNEL POSES UNIQUE REQUIREMENTS FOR CONTENT PRESENTATION , CALLING FOR A CUSTOMIZED APPROACH ...
Delivering Knowledge
The process of delivering knowledge is a multifaceted endeavor , necessitating adaptability across a diverse array of communication channels , whether it ' s within agent workspaces , on various social media platforms , or through voicebased interfaces .
Each channel poses unique requirements for content presentation , calling for a customized approach to effectively convey information to users . However , the inherent challenge lies in establishing seamless synchronization between the back-end KM processes and the distinct delivery prerequisites of each channel .
Traditional KM systems , which predominantly rely on a document-centric model , often prove inadequate in meeting these demands . They struggle to dynamically cater to the evolving needs of modern communication channels , which ultimately hamper the efficiency and efficacy of knowledge dissemination . This misalignment underscores the imperative for a more versatile and adaptable approach to knowledge delivery in today ' s rapidly evolving digital landscape .
Customer Service Experience
Traditional KM faces challenges in delivering excellent customer service experiences because it often relies on static documents and structured databases . These struggle to adapt to rapidly changing information and customer needs .
Moreover , traditional approaches may lack the ability to integrate diverse sources of knowledge seamlessly , leading to inconsistencies across customer touchpoints .
In the realm of customer service and customer experience ( CX ), challenges persist in :
• Maintaining up-to-date information .
• Integrating diverse sources of information .
• Ensuring consistency across all touchpoints .
• Easy access , retrieval and usage for agents and customers .
• Delivering content suited to the medium .
Traditional KM typically offer limited personalization , making it challenging to provide context-aware and highly relevant support information to customers . These factors ultimately hinder the delivery of exceptional customer service .
AI advancements offer promising solutions for all the above challenges , aiming for a single trusted source or consolidating multiple sources for seamless delivery .
TRANSFORMING KM
Traditional keyword-based search methods are proving insufficient because they primarily rely on specific words or phrases . They lack the ability to understand the context , intent , and semantic relationships within user queries .
KNOWLEDGE MANAGEMENT
This limitation leads to potentially irrelevant results , difficulties in adapting to variations in language , and the inability to handle ambiguous terms or synonyms effectively .
Modern search technologies , on the other hand , focus on natural language understanding ( NLU ) and semantic search . This allows for more precise , context-aware , and user-centric information retrieval , surpassing the constraints of traditional keyword-based approaches .
Semantic search , powered by NLU and vectorization , marks a significant leap forward . Vectorization , in the context of semantic search , refers to the process of converting text data into numerical vectors while preserving semantic meaning .
This involves representing words , phrases , or documents as high-dimensional vectors in a continuous vector space , where semantically similar items are positioned closer to each other . In the context of NLU , vectorization enables algorithms to analyze and understand text by capturing its underlying semantic structure , allowing for more accurate and efficient semantic search capabilities .
Through technologies like RAG ( retrieval-augmented generation ), AI can provide contextual , personalized responses in real-time , revolutionizing customer interactions .
A simple example would be calling an airline at the last minute to book a flight due to a funeral . While a standard process and answer would then simply ask for your dates , or your departure and arrival airports – completely ignoring the context – a RAG-based answer as described above could say :
“ Hi Alan , I ’ m so sorry to hear about the funeral and extend my condolences . I ’ d be happy to help during this challenging time . Please tell me where you ’ d like to leave from and your destination airport .”
From a customer service perspective , the value is clear . You achieve contextual , personalized , and relevant results immediately , show sympathy , and get the job done : all while containing this inquiry .
Moreover , instead of returning a copy / pasted FAQ , scripted answer , or a long wall of text , the customer receives that contextual , personalized response in a medium-appropriate format .
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