Introduction
The collection, categorization, and dissemination of knowledge within an organization should be one of the key pillars of its strategy. After all, this knowledge – a combination of experience, proprietary information, and huge amounts of data – represents the underlying value of a business.
Yet all too often, especially in the cases of industrial and manufacturing companies, this process is reduced to an afterthought; something haphazard left to individuals to navigate down the line.
A knowledge management system (KMS) or internal knowledge base, becomes a repository for massive amounts of data that’s just “thrown in.” The retrieval of relevant and accurate information becomes time-consuming, if not impossible.
Research from Deloitte confirms this: knowledge management is perceived as one of the top three most critical issues influencing company success, yet only nine percent of organizations feel it is being effectively addressed.
In this article, we’ll explore the gaps that exist within internal knowledge bases, the incredible opportunity that an efficient internal knowledge base provides, and how to safely and seamlessly move your organization from a position of under-optimization in this space to one of empowerment and value for all stakeholders.
Internal Knowledge Bases: What’s Missing
To implement a world-class internal knowledge base – which should be the goal of any organization, no matter its size or stage of technological advancement – it’s important to identify the current failings of such systems.
This in turn will surface the methods available to address these weaknesses, resulting in a robust, safe, tech-forward, and value-adding knowledge management system.
The following example highlights the outcomes of an inefficient internal knowledge base.
Consider the case of Alex, a field engineer working for a large manufacturing company.
Alex’s role involves troubleshooting and maintaining complex machinery across various company and customer sites. The company uses an internal knowledge base to document procedures, troubleshooting guides, and maintenance schedules. However, this knowledge base has significant inefficiencies, such as outdated information, duplications, poor search functionality, a lack of any AI capabilities, and a mix of resource types such as text documents, PDFs, and videos.
This affects Alex’s work and has a significant impact on the company itself.
Impact on individual’s work:
- Time wasted searching for information: Due to poor search functionality, Alex often spends hours searching for the right information, which delays troubleshooting and repair processes. This inefficiency reduces the time Alex can spend on actual maintenance work.
- Reliance on external help: When Alex cannot find necessary information in the internal knowledge base, he resorts to contacting external vendors or support teams for help, or even informal WhatsApp chats. This not only increases downtime and incurs additional costs for the company, but also results in critical information being lost.
- Inconsistent maintenance practices: The lack of detailed and updated documentation leads to inconsistent maintenance practices among field engineers. This inconsistency can lead to improper handling or maintenance of machinery, potentially resulting in more significant issues later on.
- Decreased employee satisfaction: The frustration of dealing with an inefficient knowledge base affects Alex’s job satisfaction. Constantly facing barriers to accessing information can be demoralizing and may lead to higher turnover rates among skilled field engineers.
- Lack of context: Often the internal knowledge base will point Alex to a long article or manual that requires him to dig through large amounts of material before he finds his answer. At other times, an answer is given with no context in terms of the information source.
- Limited formats: In many cases, Alex and engineers like him will want to watch a video of how to perform a specific task. Unfortunately, many videos aren’t classified correctly and are not searchable, and even when they are, he has to spend valuable time trying to find the right place in the video with the relevant information.
Impact on the Company:
- Increased downtime: The delays in maintenance and troubleshooting due to knowledge base inefficiencies directly impact production schedules. Increased downtime means lower productivity which is reflected in the company’s bottom line.
- Higher operational costs: Relying on external support and the potential for machinery to be incorrectly maintained or handled leads to higher operational costs. These costs come from increased service fees, repair costs, and potential penalties for delayed production.
- Loss of competitive edge: Inefficient knowledge management can slow down the adoption of new technologies and best practices. This sluggishness can put the company at a competitive disadvantage, as it’s unable to adapt to market changes or innovations quickly.
- Risk to safety and compliance: Inconsistent maintenance practices could also pose significant safety risks and issues with regulatory compliance, especially in industries where precision and adherence to safety standards are critical.
How AI Can Solve Internal Knowledge Base Challenges
Artificial Intelligence is revolutionizing the utilization of internal knowledge bases, making them more robust and intuitive than ever before. As organizations, especially in the industrial and manufacturing sectors, look to adopt these AI-powered systems, there are critical considerations to ensure that the technology aligns with operational needs and enhances overall efficiency. Below are six crucial points industrial and manufacturing organizations should consider when adopting AI-powered knowledge management systems. These principles will guide the successful integration of AI into their knowledge bases, ensuring they are not only advanced but also accurate, secure, and user-friendly.
- Content Ingestion: The quality of an AI-powered knowledge base’s output hinges on the data it receives. Prioritizing accurate data input is crucial, as it dictates the system’s effectiveness. A robust AI knowledge base should automate knowledge parsing, represent content hierarchies accurately, and exclude extraneous information. For instance, user manuals are traditionally integrated manually, a method that’s not only slow but susceptible to errors. AI, on the other hand, can streamline this by analyzing documents, standardizing formats, and utilizing NLP to auto-generate tags and workflows, ensuring the ingestion process is both efficient and precise.
- Search: Accuracy is pivotal for industrial and manufacturing sectors, where even minor discrepancies can result in significant issues. It’s essential that the search function accurately identifies critical technical terms and industry-specific jargon. Implementing restrictive domain searches is vital to ensure the retrieval of information solely from vetted internal sources. AI-enhanced knowledge bases significantly outperform traditional knowledge bases in terms of accuracy, efficiency, and value, directly addressing users’ needs by filtering out irrelevant data.
- Holistic Experience: In industrial and manufacturing settings, responses may need to integrate multiple media forms, including text, diagrams, videos, and audio. Advanced AI-powered knowledge bases excel by drawing from an array of content types during data ingestion and presenting users with comprehensive, context-rich answers, transcending purely text-based responses.
- Safety: As with search, safety is paramount, especially when incorrect information can have serious implications. Utilizing AI within internal knowledge bases necessitates a closed-domain, accuracy-focused approach, sourcing from approved internal content to avoid the risks associated with broad internet-based solutions.
- Privacy: Maintaining privacy within AI-driven internal knowledge bases is complex in industrial or manufacturing contexts. Access to sensitive information must be restricted based on roles and clearance levels, and protection against external threats must be ensured to comply with privacy laws and regulations.
- Seamless Integration: AI internal knowledge bases should integrate effortlessly with existing technological infrastructure, eliminating the need for extensive integration projects and enabling organizations to adopt AI swiftly and securely, with minimal risk.
Implementing AI Solutions Responsibly
Implementing a generic AI solution for enhancing your knowledge base efficiency can lead to suboptimal outcomes. Such approaches may result in inaccurate answers, inconsistency with your branding, and could fail to process a significant portion of your documentation corpus effectively.
This is where Korra stands out as the preferred choice for companies beginning their AI journey. Tailored to your specific data, it adapts to your users’ interactions, and by contextualizing information, it ensures consistent, pertinent, and precise responses.
Furthermore, the transition to Korra is risk-free. There’s no need to alter your existing documentation or internal systems. Simply connect your storage system, and with two straightforward steps, you can activate a fully optimized AI-driven internal knowledge management system.
Conclusion: The Safest and Most Effective Way To Leverage Your Internal Knowledge Base
In this case, we looked at the importance of an optimized knowledge base for teams in the field, but this is equally applicable to employee onboarding, content management, resale, product management, and engineering, not to mention customer-facing contact center solutions.
We explored the current weaknesses of internal knowledge bases and their implications and looked at ways that these can be overcome.
Specifically, we investigated how AI can be used to extract maximum value from your internal knowledge base, and the safest way to get started.
To start your journey of responsible AI implementation for your internal knowledge base, book a call with the Korra team today.