Tap Into Your Company’s Most Valuable Asset

James Lin
May 26, 2022

“If only HP knew what HP knows, we would be three times more productive,” Lew Platt, the former CEO of Hewlett-Packard (HP), famously stated. His observation couldn’t have been more prescient.

As the fusion of human abilities and digital tools like automation, AI, augmented reality and cloud-based computing reshape our world, knowledge will only become more valuable.

What your employees know – and how to turn it into organizational knowledge – could be the difference between a thriving business and a failed venture in the next age of innovation. According to the Journal of Management Review, 99 per cent of the work people do is based on knowledge. And although much of this work goes unseen (approximately 90 per cent is stuck inside colleagues’ heads) organizational knowledge has been proven to drive productivity, revenues, and agility.

So how does a business identify its collective knowledge in the first place? And once recognized, how can we capture, datarize, manage, and transform that knowledge into an asset?

First, let’s differentiate between the two fundamental types of knowledge: explicit and tacit.

Often logical, factual, and procedural in nature, explicit knowledge is relatively easy to communicate verbally or in writing. It is also easy to codify and store in easy-to-share formats, such as manuals, procedures, rules, databases, and spreadsheets. For instance, data entry instructions, bookkeeping rules, assembly line operating procedures, courier service training manuals, and manufacturing equipment maintenance guides are all forms of explicit knowledge that’s been captured, codified, and shared.

Then we have tacit knowledge. Coined by Michael Polanyi in 1958, pointed out that we know more than we can tell, the word “tacit” means “understood or implied without being stated”. True to its name, tacit knowledge is much harder to capture and transfer.  

Why is it so challenging to share? Tacit knowledge refers to know-how, know-why,  and expertise people possess from learning on the job for many years; it’s often difficult to put these experiential and contextual insights into words or write them down, making it difficult for businesses to transfer it from one person to another. Some examples of tacit knowledge are driving a car, riding a bike, handling customer complaints, closing sales deals, developing software applications, and designing application architecture. When a designer knows just the right shade to paint a room, a chef can simply taste a recipe and know what’s missing, or a doctor runs a test on a hunch – that’s tacit knowledge in action.

Comparison of Explicit and Tacit Knowledge

Explicit Knowledge
Tacit Knowledge

The type of knowledge that has been or can be articulated, documented, and passed on using a formal or systematic language (e.g., words, pictures, symbols, and concepts). 

Personal knowledge is gained through experience and embedded in an individual's actions, practices, and values. It is often difficult to articulate and cannot be expressed in a formal or systematic language. 


Codified knowledge. Objective, logical, and informational. 

Uncodified knowledge. Subjective, cognitive, and experiential.


Captured as data/content objects and stored in easy-to-access digital/physical formats (e.g., manuals, books, case studies, documents, spreadsheets, databases, files).

In the brain, mind, and embedded in habit and behaviour (e.g., know-how, know-why, heuristics, perspectives, rule of thumb, mental models).


Context-specific, associated with a particular situation, time, and place.

Context-free, generally transferable to other situations, times, and places.


High accessibility and transferability. Can be disseminated in digital formats using various information technologies. 

Difficult to communicate and share. Transfer mainly through direct interactions and joint activities (e.g., conversations, coaching, storytelling, mentoring, and apprenticeship). 

While tacit knowledge can be hard to capture, it’s far from impossible thanks to a model of knowledge dimensions called SECI (Socialization, Externalization, Combination, Internalization). Created by Ikujiro Nonaka, a Japanese organizational theorist, in 1991, and later refined by Harvard professor of management practice Hirotaka Takeuchi,  the SECI model is the best known conceptual framework for understanding how tacit and explicit knowledge is converted, combined, and transformed into organizational knowledge. It postulates that organizational knowledge creation is a continuous, iterative process of tacit and explicit knowledge conversions. Successive cycles of the process form a spiral, with each loop growing and amplifying the knowledge.  

When combined with AI and other emerging technologies, SECI creates a streamlined cycle of sharing and exchange – a cycle that will only create more organizational knowledge, growth, and innovation in the future. Here’s how it works:

SECI Model of Tacit Knowledge Gathering & Sharing Socialization: How to Generate Tacit Knowledge

Start by identifying individuals with tacit knowledge across the company – who knows what? How do they know it? Do you have an engineer in your New York office who could help fix a problem in Hong Kong? Or a CEO who has a knack for discovering new talent? Build a profile for each individual, so other employees can quickly ascertain their areas of expertise, and knowledge sharing can begin.

Companies can enable socialization by building their own knowledge networks. They can build a database containing every employee’s profile that includes information on his/her academic, skills, and past experiences, and with powerful tagging and categorization technology. As soon as each individual is accessible and searchable, employees can easily find the right people, start conversations, and pose questions via chat message, phone, email, or in person.

Externalization: Transform Knowledge into Searchable Data

Now that a company has created an easily searchable pool of individuals with different types of expertise within the organization, the knowledge starts to flow, especially if their answers to questions are logged and tagged, externalization can take place by turning work documents or conversations into easy-to-search, share, and consume formats that employees can harness across the workspaces.

AI-powered transcription and speech recognition technology can be used to capture and convert Q&As, phone conversations, presentations, and mentorship consultations into data.

For example, let’s say an employee has an insightful call with your firm’s lead salesperson about successful strategies for closing deals. The AI-driven technology can transcribe the call’s audio, then tag, classify and organize the information within the company’s knowledge database. In this way, the individual’s insight is being transformed into searchable data for employees to find and learn from.

Combination: Organize Data into a Collective Wisdom

Combination is the phase following the externalization phase whereby pieces of knowledge are connected into an organizational expert system – whether it’s a document, transcript, or Q&A – that is tagged, linked, and made easily accessible. This way, you would have built an AI-driven searchable bank of insights, linked to experts who have provided these insights over time and augmented by further interactions from others across the organization, resulting in building this knowledge bank proprietary to your organization, thus expanding your organization’s collective wisdom.

Connecting the knowledge data, and further linking it to people allows the continuous cumulation of knowledge data if there are more questions about the topic, the people with the expertise can come back to give more information and further augment this knowledge graph. The organization now has a knowledge ecosystem that continues to grow and is self-sustaining.

Internalization: Transforming the Collective Wisdom into Individual Wisdom

Internalization of Tacit Knowledge

In the internalization phase, employees will absorb the knowledge, apply it with their own understanding into their workflow, and then share it with others. Each individual can create their own workspace, save relevant questions and answers and discover new experts to consult with. They can then apply this newfound insight to their own tasks, making the insight their own.

For example, if a computer programmer in South Korea needs help with a challenging software problem, she could search the company’s knowledge database for her problem, find a Q&A on a similar issue, and follow the instructions. Or perhaps she reaches out to the colleague who shared that knowledge, and they talk it through on the phone. Next time, the technician in South Korea knows what to do and can help others, too. By applying the knowledge, she made it her own, and further augmented it with her own learnings.

Coming to Full Circle

Once employees have internalized the information and applied it to their own work, they can then share it with others. This effectively closes the loop and brings us back to the beginning, where we identify and categorize employees’ growing knowledge. As your company’s collective wisdom grows, you can continue sharing, datarizing, absorbing, and sharing again – an efficient cycle that continuously builds organizational knowledge.

Just as former HP head, Lewis Platt, realized many years ago, being able to easily tap into organizational knowledge has many benefits. It increases productivity, retains knowledge, reduces frustration, empowers employees, and improves scalability.  In short, there’s no better recipe for long-term success and innovation than a well-organized, searchable AI-driven knowledge management platform that grows with your company.

Curious about Lynk’s extensive, organized and secure knowledge base? Get in touch today.  


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