Enterprise knowledge management (KM) is a whole-company priority that makes business knowledge central to your entire organization. With a focus on strategic planning, knowledge sharing, avoidance of knowledge loss, and data management, the process requires the right tools and technology to help spearhead knowledge organization and sharing.
Increasingly, AI-powered solutions are shifting how companies iterate and implement their knowledge management processes. For example, you could augment your knowledge management solution with an AI search platform like Coveo, which uses machine learning to refine search results based on user data. Ultimately, this helps employees find relevant information quickly.
AI-powered search solutions (especially ones that are KCS verified or aligned) can also help unify and integrate access to multiple knowledge databases, creating a single search result hub. This facilitates knowledge transfer by making it easier to search for and retrieve relevant knowledge.
There are impactful ways to integrate AI into your knowledge management strategy at every stage of project and service management that we’ll illustrate in this post. But first, a brief definition of the knowledge management process and its key roles and responsibilities.
What Is The Knowledge Management Process?
A knowledge management process involves capturing, distributing, and using three types of company knowledge which include:
- Explicit knowledge: This is a tangible knowledge asset that’s been captured in some way — a blog post, a document, a spreadsheet, etc.
- Implicit knowledge: Knowledge that puts explicit information into practice is implicit knowledge (e.g., you read a blog post and can answer the customer’s questions based on the knowledge you’ve just gained).
- Tacit knowledge: The valuable knowledge that’s locked inside your employees’ brains is tacit, or institutional, knowledge. This knowledge is gained from experience and typically passed down verbally via hands-on training and communication.
Gartner’s Solution Path for Knowledge Management features a comprehensive knowledge management process diagram that illustrates five phases of establishing a mature and sustainable KM program.
Steps start with defining the vision and strategy up front and wrap up with automation and AI to assist with knowledge capture, content enrichment, and generative AI to power intelligent assistants.
Keep this framework in mind as a guide for developing your own KM process:
Key Roles and Responsibilities in a Knowledge Management Process
A company with a strong knowledge sharing culture takes ownership of business processes connected to knowledge sharing across the entire organization. Key KM roles and responsibilities include:
- An executive sponsor that provides strategic vision and support around the knowledge management cycle.
- A knowledge manager who oversees KM processes, change management, and implementation.
- IT Specialists that guide technical infrastructure and integration of knowledge management tools.
- Content authors and editors across various teams that create and maintain knowledge content.
- Change agents who drive adoption and engagement within the organization.
- Data analysts to evaluate KM effectiveness and provide insights that support knowledge discovery and sharing.
- Knowledge champions across various departments who promote knowledge management best practices to their teams.
These roles are collectively what make a good knowledge management process work. They infuse a knowledge management culture that puts a daily focus on the effective capture, distribution, and implementation of knowledge. They keep knowledge management practice top of mind too, continuously refining and improving the KM process so that it meets the changing needs of employees and customers.
Relevant Reading: 3 Simple Questions to Grow a Knowledge Sharing Culture
How the KM Process Influences Knowledge Sharing
The KM process establishes a foundation for gathering, distributing, and utilizing information. This structured approach supports and rewards employee collaboration — and employee participation is where knowledge sharing begins and ends.
When employees feel confident in accessing and contributing knowledge, it breaks down silos and promotes continuous learning and innovation. Here’s an overview of how KM and AI search can work together to influence knowledge sharing across an organization.
Improve Customer Self-Service
A comprehensive knowledge management process contributes to more effective self-service capabilities for customers and employees by connecting users to helpful information independently.
AI supports this in several ways, most of which we covered already — AI search makes results more relevant, and AI models like smart snippets or generative answering pull content from various assets and display it at the top of the search results pages. Creating content to fill knowledge gaps means that information is available when users search for it. These functions work together to enable more successful self-service experiences.
Refines Information Access
AI search underpinned by a unified index supports the KM process by improving access to relevant — and current — information, and it can do this for multiple audiences. Recency of information is particularly important in KM, since knowledge has a shelf life.
By prioritizing the most recent content, AI keeps information fresh. As users (such as employees) naturally gravitate towards more current, useful information. AI search can help phase out outdated content.
Removes Silos
A unified AI search platform removes barriers to information by stitching information sources and data silos together — while respecting security and permissions. Once information from sources like your website, CRM platform, Slack, email archives, and cloud-based drives like Dropbox is all in one place, the content therein becomes searchable.
This reduces the amount of “context switching” — moving back and forth through different applications when searching for information. In our 2023 Workplace Relevance Report, 44% of respondents said that siloed data slows them down or prevents them from finding the information they need.
Analytics and Recommendations
With robust search analytics, you can surface insights that can inform your entire knowledge management process, ensuring you keep knowledge at the center of everything you do.
AI-powered recommendations also use data to personalize knowledge delivery, customizing elements like search results, content, and products to each individual user.
Machine learning algorithms then use behavior and user data to improve search accuracy over time.
Identify Knowledge Gaps
Search analytics can help determine what employees are searching for and the knowledge that they turn to the most. This is an important way that AI helps organizations identify a knowledge gap or gaps — areas where they might need to fortify and build a more comprehensive internal knowledge base.
If employees are frequently coming up empty when searching for information, it’s likely you need to build additional knowledge assets to support areas like onboarding, training, customer service, and product development. (Or re-assess which content sources are in your unified index!)
Risks of Not Having a Knowledge Management Process
Good knowledge management process requires a bit of an organizational pivot. It moves from an “individual expertise approach” to a “collective knowledge” approach. While it may seem daunting, the risks of not having a solid KM process are high.
Without a structured approach to managing organizational knowledge, companies face some steep challenges that include:
- Loss of institutional knowledge: When employees leave the company, valuable tacit knowledge leaves with them.
- Uninformed decision-making: Employees need access to relevant up-to-date information so they can make informed decisions.
- Reduced productivity: According to our 2023 Workplace Relevance survey, employees spend an average of 3 hours a day searching for information across up to six different sources. They also waste time recreating existing knowledge. A strong approach to KM reduces the effort it takes to find information using tools like AI-powered search.
- Inconsistent experiences: Lack of a centralized index creates inconsistencies across various touchpoints and channels. This impacts every user who interacts with your business including employees and customers.
- Missed opportunities: Poor access to information inhibits collaboration and idea-sharing and makes the kind of data analysis done across a unified knowledge base nearly impossible. That means you can’t react to new opportunities and trends quickly – and this can stifle innovation and creativity.
- Increased training costs: Without a structured way to pass on knowledge, onboarding and training new employees becomes more time-consuming and expensive. This is compounded as remote and hybrid work becomes the norm.
- Compliance risks: Failing to maintain and share up-to-date knowledge can create compliance issues, particularly in highly regulated industries like healthcare and finance.
- Increased employee frustration: When employees can’t find necessary information quickly, they get frustrated. This creates lower job satisfaction and potentially higher turnover.
These risks make it clear that a robust knowledge management process supported by AI-powered tools is foundational when it comes to enabling good user experiences. When knowledge is adequately captured, shared, and managed across an organization, employees are more successful in doing their jobs and customers are more satisfied.
Common Knowledge Management Process Challenges
Outdated Technology
One of the main KM process challenges companies typically face is outdated technology that fails to meet modern knowledge sharing needs. Legacy systems may not integrate well, creating information silos that are basically roadblocks which prevent effective knowledge distribution.
AI-powered unified search platforms like Coveo address this since one of Coveo’s key capabilities is connecting knowledge across different sources. Coveo also standardizes data, putting it in a common format that can be ingested into the AI system. A recent whitepaper by Gartner notes that AI is useful in content enrichment and knowledge capture, automating many routine tasks and freeing up employees to focus on more complex knowledge work.
Lack of Employee Buy-In
Another significant challenge is the lack of employee buy-in. Staff may resist adopting new knowledge sharing practices because of concerns like the time involved in learning a new system or approach, fear of change, and worries about job security. Per the Gartner report, “KM moves the enterprise from individual expertise to collective knowledge. This is an extremely difficult challenge, especially in geographically distributed organizations with a remote or hybrid workforce.”
This is why having C-level support of the KM process is so important. An executive sponsor and KM manager can set the stage for a strong knowledge-sharing culture — one that’s supported by leadership and incentivized through recognition and rewards for employees who participate in knowledge sharing.
Undefined Goals
Another obstacle to KM success is the need for appropriate knowledge resources for content creation and maintenance. This must be addressed when creating a KM process strategy and structure, as the Gartner whitepaper notes, “Start by articulating a well-defined vision of what KM should accomplish for the enterprise. Identify the business drivers motivating the undertaking and establish the KM principles that will guide the program.”
KM is, first and foremost, driven by people and the tacit knowledge they contain. Technology supports and enhances KM processes, but successful knowledge management relies on an underlying culture of knowledge sharing and creation. Each employee plays a role in perpetuating and enabling knowledge sharing, which is why all employees should be encouraged to contribute to the knowledge base as part of their regular workflow.
Measures of KM Success
You’ll need to assess the impact of your knowledge management initiatives so you can refine and optimize your approach. Gartner recommends tracking the following metrics:
- Number of active community members
- Number of questions answered
- Template usage
- Frequency of edits and updates
- Call time reduction
- Number of self-serve articles
- Knowledge article reuse rate
For customer support teams, a decrease in average call duration and an uptick in the use of self-service articles point to a well-functioning KM system. Several factors contribute to the successful implementation of a knowledge management process, but none are more important than a well-defined vision and strategy that aligns with your company’s objectives.
And, as we already noted, solid support from leadership and a dedicated team overseeing KM initiatives keeps the knowledge management process humming along. Intuitive technology that meshes smoothly with existing workflows supports the entire process, along with an organizational culture that promotes and recognizes employee participation in knowledge sharing.
By concentrating on these elements and regularly evaluating performance, companies can build a strong, effective knowledge management process — one that supports innovation, aids in making informed decisions, and boosts overall productivity.
Dig Deeper
Learn the truth about three common knowledge management myths and the solutions that can help you plan the right path to leveraging enterprise knowledge and grow your business.