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The New Face of Knowledge Management

The New Face of Knowledge Management

The New Face of Knowledge Management

How AI, LLMs, and vectorization has changed KM.

Knowledge management (KM), as we’ve known it, is swiftly becoming outdated. While its name suggests a cohesive solution, it actually encompasses two distinct functions: managing knowledge and delivering it. However, the rise of artificial intelligence (AI), coupled with vectorization technology, threatens to permanently decouple these components.

In the realm of contact centers, KM stands as a vital support pillar, pivotal to problem resolution. Yet traditional approaches are akin to using a typewriter in an iPhone-dominated world. With advancements in AI and large language models (LLMs, a.k.a. Generative AI), we are facing a new era of KM solutions and approaches.

Let’s explore the current state of knowledge management: and the reasons behind a new era of KM solutions and what this means for contact centers.

Traditional KM Shortcomings

Managing Knowledge

Knowledge management encompasses a comprehensive set of activities facilitated by various software solutions. It incorporates a multitude of intricate back-end processes, including the creation, meticulous structuring, rigorous review, thorough approval, and continuous optimization of knowledge assets.

These processes are essential for guaranteeing the accuracy of knowledge resources, ensuring alignment with brand guidelines in terms of style and tone, and delivering content in a user-friendly and digestible format.

Management of information refers to the entire backend required to create, structure, review, approve, and optimize information over time. The focus on the management side is that the knowledge:

  • Is current
  • Is accurate
  • Follows brand guidelines for style, tone of voice
  • 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.

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 voice-based 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.

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.

Each channel poses unique requirements for content presentation, calling for a customized approach...

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.

Goodbye Documents, Hello Snippets, Chunks

The concept of the document has historically served as a fundamental building block in both the analog and digital realms, often taken for granted and unquestioned.

However, the transition from analog to digital merely transposed this concept into a new form without adequately considering the substantial disparities in capabilities, formats, and delivery mechanisms between the two worlds.

A prime example of this disconnect can be observed in Google’s evolution from presenting full documents or web pages in search results to providing succinct snippets that directly address users’ queries. It’s safe to say that few, if any, users actively desire lengthy walls of text in their search results, a sentiment shared by customers seeking swift and relevant answers.

Consequently, the future of KM demands a harmonization of the final delivery format with the structure and format initially used for content creation and storage.

While some KM software has already made strides in this direction, there remains a significant journey ahead. Presenting customers with mere links to webpages, copy-paste FAQ responses, or cumbersome blocks of text represents suboptimal CX.

Today’s users expect quick and precise solutions to their queries, having often conducted preliminary research. A response that merely redirects them elsewhere falls short of the definition of a solution.

Similarly, providing contact center agents with extensive textual resources can impede their ability to efficiently resolve customer issues during phone calls or chats. This emphasizes the need for a more user-centric approach to knowledge delivery.

A New Era of KM Solutions

The current landscape of KM software primarily revolves around solutions centered on editorial and management functionalities, often offering limited capabilities for content delivery.

Additionally, these solutions often rely on outdated machine learning and AI technologies, which are not their primary foci or areas of expertise.

Consequently, the impending evolution of KM software is poised to disentangle the realms of management and delivery, likely necessitating the adoption of entirely distinct solutions for each facet.

...contact centers are increasingly turning to Knowledge AI...

On the delivery side, there is a burgeoning trend referred to as “Knowledge AI,” wherein vendors leverage a combination of NLU, LLMs, and vectorization, as discussed earlier, to substantially outperform conventional methods.

But while these cutting-edge delivery technologies show promise, they currently lack the critical management capabilities crucial for ensuring the accuracy, quality, and usability of knowledge resources, as previously highlighted.

This growing disparity underscores the need for a reevaluation of KM strategies, as the technological advancements on the delivery side have surged far ahead of their managerial counterparts, warranting the development of new solutions (Knowledge AI) to bridge this gap.

The trajectory is clear, signaling an inevitable shift toward more agile and specialized KM approaches.

Best Practices for Contact Centers

In this ever-evolving landscape of customer service, contact centers are increasingly turning to Knowledge AI and cutting-edge knowledge management solutions to enhance their operations. Leveraging these advanced technologies opens up a new era of possibilities, where customer interactions are more streamlined, efficient, and personalized.

To maximize the benefits of Knowledge AI, contact centers must adopt several best practices that ensure optimal utilization and seamless integration of these innovative tools.

Holistic Integration

  • Comprehensive knowledge base. Develop and maintain a well-structured and up-to-date knowledge base that encompasses a wide range of topics and issues relevant to your products or services.
  • Seamless integration. Implement Knowledge AI solutions that seamlessly integrate with your existing contact center infrastructure, including CRM systems, chatbots, and agent interfaces.
  • Multichannel accessibility. Ensure that knowledge resources are accessible across all customer touchpoints, such as websites, mobile apps, social media, and live chat, to provide a consistent CX.

User-Centric Approach

  • Contextual understanding. Utilize NLU capabilities to comprehend user queries, considering context, intent, and sentiment, enabling the delivery of personalized responses.
  • Dynamic content delivery. Employ Knowledge AI to dynamically tailor responses based on user needs, preferences, and interaction history, offering the most relevant and concise information.
  • Self-service optimization. Encourage self-service by empowering customers with easy-to-navigate knowledge resources, reducing the need for agent assistance and enhancing customer satisfaction.

Continuous Improvement

  • Regular updates. Maintain a proactive approach to KM by continuously updating and refining your knowledge base to reflect evolving customer queries and industry trends.
  • Performance analytics. Leverage analytics and reporting tools to monitor the effectiveness of KM solutions, identifying areas for improvement and optimization.
  • Feedback loop. Establish a feedback mechanism to gather input from both customers and contact center agents, using their insights to enhance the quality and relevance of knowledge resources and AI-driven responses.

By adhering to these best practices, contact centers can harness the power of Knowledge AI and a new era of KM solutions to deliver efficient, personalized, and consistently excellent customer service experiences.

Conclusion

In summary, the landscape of traditional KM software solutions is rapidly evolving into obsolescence, primarily due to the remarkable advancements in AI, LLMs, and vectorization.

This powerful trio, encompassed in Knowledge AI, is revolutionizing the way contact centers approach KM, ushering in an era that departs from the conventional document-centric methods and embraces dynamic, context-sensitive, and user-centric approaches.

The future of KM resides in harnessing the potential of AI technologies to deliver precise, context-aware, and highly personalized knowledge to users in real-time. This transformative shift goes beyond mere management; it fundamentally alters the nature of how support information is both controlled and disseminated within contact centers.

By adhering to...best practices, contact centers can harness the power of Knowledge AI and a new era of KM solutions...

With the dawn of this new era in KM, it becomes increasingly imperative for contact centers and CX leaders to fully grasp, acknowledge, and proactively plan for these significant changes.

Doing so will enable organizations to seize the substantial benefits that these innovations offer, enhancing their customer service capabilities and overall operational efficiency. Embracing this evolution will undoubtedly shape the future of CX and service excellence.

Alan Ranger

Alan Ranger

With a career spanning 30 years, Alan Ranger has held a variety of sales, marketing, and leadership roles, both in start-up and large enterprise software companies. As Cognigy’s VP Marketing, Alan’s focus is on leading and inspiring his high-performance global team to deliver programs across branding, product marketing, demand generation, events, and social media.

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