In today’s competitive business environment, the significance of high-quality data in Knowledge Management (KM) cannot be overstated. Traditionally, KM processes have emphasized the necessity of robust data quality, a need that has only intensified with the emergence of advanced technologies like AI and Machine Learning. When implemented with the right guardrails, AI-powered tools such as chatbots and AI agents have the potential to transform how users access and leverage organizational knowledge. These tools enable teams to efficiently sift through vast organizational knowledge repositories, identify key insights, and make informed decisions - whether it involves closing a current deal, developing a differentiated proposal, or presenting tailored solutions to clients.
The effectiveness of these AI tools is reliant on the quality of the underlying data. Without a strong commitment to maintaining high data standards, organizations face significant risks, including inaccuracies, incomplete information, or contextually irrelevant insights. As organizations increasingly embrace AI for KM, ensuring data quality becomes a strategic necessity to mitigate these risks and unlock AI’s true potential.
In this blog, we offer insights and best practices from successful companies that have effectively balanced AI with the rigor for ensuring high-quality data in their KM initiatives.
What’s New in “Management” of Knowledge.
A common question we receive from clients is whether the new wave of AI tools, such as Chatbots and Conversational Copilots, represents the future of KM. This trend has certainly garnered attention amidst the excitement surrounding AI and AI Agents. While AI is a powerful enabler for KM, the effectiveness of a chatbot’s output is entirely dependent on the quality of the underlying data. Without high-quality data, Generative AI tools have been known to produce factually incorrect information, often without any indication of error, thus posing significant business risks.
As per Microsoft, nearly 2 billion documents are uploaded on SharePoint every day. That is a staggering stat to start with, but it is not new since SharePoint has been a leading content service platform for many years. Companies with successful KM initiatives know that they need to identify high-value documents from the large content base on SharePoint and manage their lifecycle. This is generally a much smaller set of documents that needs to feed into organizational knowledge and that’s what we call “knowledge assets”.
Creating such a centralized repository of knowledge assets requires identifying the final versions, having consistent processes across the company and regular audits to ensure processes are followed. This is an area where the KM specialists continue to play a critical role in ensuring high-quality data. Even for this “backend” process, Generative AI is a great enabler as it is already being used for annotation, summarization, and even comparison of document versions.
Risks of not focusing on Data Quality in Knowledge Management
AI Chatbots and Copilots have moved the needle in terms of ease, with which users can tap into organizational knowledge. Since these tools are much easier to use and the AI outputs generally “read” well, the prominent risk is that users take something away that is factually incorrect, incomplete, or out of context. Ensuring that AI tools operate on high-quality data is an essential and critical practice adopted by successful companies. The underlying data may include internal documents, external intelligence, policies, CRM data, customer interactions, and more. These are invaluable sources of organizational knowledge but can also lead to out-of-context or inaccurate responses if data quality is not properly managed. For instance, you would not want users referencing outdated proposal drafts or unsuccessful proposals, as this could result in misinformed decisions or inefficiencies.
Poor data quality can cause inaccuracies, outdated information, duplication, and compliance risks, adversely affecting KM initiatives. This leads to inefficiencies, errors, and poor decision-making. Bad data can also impede AI strategies by providing incorrect or incomplete information, impacting the performance and reliability of AI models.
Conclusion
Generative AI simplifies the search and discovery of information. However, the reliance on high-quality knowledge assets is essential. Without the right approach, it can lead to significant business risks. Our KM solutions ensure users get the latest AI tools for information search, while our integrated KM services focus on data quality.
At Evalueserve, we have seen tremendous success in enabling clients to capture, retain, and manage organizational knowledge. We work closely with large enterprises to capture both explicit and tacit knowledge. Our solutions ensure users have easy access to high-value knowledge assets, so they don’t need to reinvent the wheel. With a deep understanding of client processes and workflows, we also capture and manage tacit knowledge from consultants, pre-sales engineers, and more.
