Knowledge Management
May 12, 2025

7 Types of Knowledge: Definitions & Use Cases [2025 Guide]

Contributors
Dhanashree B
Product Marketing Manager
Updated on
May 12, 2025

Knowledge is the foundation of human progress. From ancient philosophy to modern scientific advancements, understanding how we acquire, categorize, and apply knowledge has been at the center of intellectual inquiry. As we navigate an increasingly complex world shaped by technology, globalization, and artificial intelligence, the ways in which we define and distinguish between different types of knowledge are more relevant than ever.

In this comprehensive blog, we take a deep dive into the types of knowledge that have shaped human understanding, their relevance in today’s society, and how emerging technologies are transforming our relationship with knowledge itself. We approach this topic from an interdisciplinary lens, drawing from philosophy, education, cognitive science, and artificial intelligence to offer a holistic exploration of what knowledge is, how it functions, and why it matters.

7 Types of Knowledge in 2024

  1. Explicit knowledge
  2. Implicit knowledge
  3. Tacit knowledge
  4. Declarative knowledge
  5. Procedural knowledge
  6. A priori knowledge
  7. A posteriori knowledge

Understanding the Concept of Knowledge

Defining knowledge is difficult, but knowledge can be seen as an awareness of facts, ideas, or situations. It includes familiarity with different subjects, people, and experiences. 

A research paper defines knowledge as “Knowledge  is  a   familiarity,  awareness,  or  understanding  of  someone  or  something,  such   as  facts, information,  descriptions, or  skills,  which  is   acquired  through  experience  or education  by  perceiving, discovering, or learning.” 

Wikipedia defines knowledge as Knowledge is an awareness of facts, a familiarity with individuals and situations, or a practical skill.

Before diving into the different types of knowledge, it’s essential to understand what knowledge itself means. Emerging technologies are transforming our relationship with knowledge. Philosophically, knowledge has often been defined as "justified true belief." This classic definition implies that for someone to know something, the belief must be true and there must be justification for it. While this definition has evolved and been debated extensively, it forms a foundational framework for differentiating between types of knowledge.

Knowledge is not monolithic. It exists in various forms, each with its characteristics, modes of acquisition, and application. In 2025, this diversity is even more pronounced due to the proliferation of digital platforms, access to big data, and the evolving role of AI in knowledge management.

On a side note: Did you know data is different from information which is in turn different from knowledge and wisdom? 

In a research paper, Ramona Nelson mentioned how data, information, knowledge and wisdom differ. Data is nothing but raw facts, numbers or facts which is further used to draw insights from. When the raw facts are processed together to produce meaning or insight - it is called information. Now, when we piece together multiple information pieces - we get knowledge. Think of it as having all chapters of a textbook - where every chapter is information and every chapter’s facts are data. The true benefit of doing this exercise is when multiple knowledge pieces are used together to apply the knowledge to human problems - example using FAQs to solve customer queries or more. 

Our main motto of accumulating every knowledge source is to ultimately take action - which is wisdom. 

How does data convert to knowledge?

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Data vs Information vs Knowledge vs Wisdom

Let’s clear up a common confusion:

Layer Meaning Example
Data Raw facts or numbers “45, 76, 89”
Information Processed data with context “Average customer age is 45”
Knowledge Organized information used for action “Target millennials for new plan”
Wisdom Applying knowledge to make smart decisions “Launch digital-first campaigns”

What are the 7 types of knowledge?

Now that we know what knowledge is, let’s explore the 7 types of knowledge that you need to be aware about:

We’ll discuss all of them in detail below with a definition, explanation and examples for each one of them. 

The 7 types of knowledge you must know in 2024 

1. Explicit Knowledge

Source 

Explicit knowledge is the most tangible form of knowledge. It is codified, documented, and easily transferable. Examples include manuals, textbooks, procedures, and databases. In business environments, explicit knowledge is captured in standard operating procedures (SOPs), training materials, and policy documents.

With the growth of knowledge management systems and enterprise AI tools in 2025, explicit knowledge is now more searchable and accessible than ever before. Organizations are investing in advanced tagging, metadata frameworks, and natural language search to ensure this type of knowledge is efficiently leveraged.

Examples of Explicit Knowledge

Here are some examples of explicit knowledge: 

Instruction Manuals: A user manual for a smartphone that details its features, operation, and troubleshooting steps serves as an example of explicit knowledge. The manual codifies the device's functionalities in a clear, accessible manner, allowing users to understand and utilize the product effectively.

Scientific Papers: Research articles published in academic journals represent explicit knowledge. These papers detail the methodology, findings, and conclusions of scientific investigations, offering a structured and documented account of research endeavors. They facilitate the dissemination of knowledge within the scientific community and beyond, enabling further research and application.

Technical Applications:

In 2025, explicit knowledge is leveraged via ontologies and taxonomies embedded into enterprise knowledge management systems. AI models are trained on structured datasets extracted from databases, APIs, and content repositories. For instance, large language models (LLMs) use embedding-based retrieval over explicit knowledge sources to answer queries in enterprise virtual assistants.

For companies, explicit knowledge can be FAQs, one sheets, pitch decks, knowledge base, privacy policy, etc. Here is an example of documented explicit knowledge used by companies. 

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2. Tacit Knowledge

Tacit knowledge is personal, experience-based knowledge that is difficult to articulate or formalize. It includes intuitions, insights, and know-how. Unlike explicit knowledge, tacit knowledge is acquired through practice and often shared through social interaction, mentorship, or collaboration.

For example, a master carpenter might "just know" how to choose the right wood or angle a cut perfectly. This know-how cannot be fully captured in writing. In 2025, capturing and sharing tacit knowledge has become a strategic priority for organizations, especially as experienced employees retire or transition.

Source

Examples of Tacit Knowledge

Artistic Skill: An artist's ability to create art is an example of tacit knowledge. This includes understanding how different colors interact, the impact of light and shadow, and the expression of emotion through brushstrokes—all of which are learned through practice and experience, not easily articulated or taught.

Leadership Abilities: Effective leadership often relies on tacit knowledge. A seasoned leader's ability to motivate a team, navigate complex organizational politics, and make strategic decisions is rooted in experiences and insights that are difficult to codify but are critical for success.

Technical Applications:

In 2025, capturing tacit knowledge involves multimodal sensing (e.g., eye-tracking, biometrics) and behavioral logging. AI tools analyze repeated behavioral patterns to infer tacit rules. Knowledge elicitation via techniques like cognitive task analysis (CTA) and structured interviews is also used to convert some tacit knowledge into semi-explicit forms.

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3. Procedural Knowledge

Procedural knowledge refers to knowing how to perform certain tasks or procedures. It is closely related to tacit knowledge but can be partially codified. Examples include knowing how to ride a bicycle, cook a recipe, or operate a machine.

In education and corporate training, procedural knowledge is often taught through simulations, demonstrations, and hands-on practice. Virtual Reality (VR) and Augmented Reality (AR) tools are increasingly used to teach procedural knowledge in immersive environments in 2025.

Here is an idea of what procedural knowledge tackles: 

Image source

Examples of Procedural Knowledge

Riding a Bicycle: The knowledge of how to balance, pedal, and steer a bicycle is a form of procedural knowledge. These skills are acquired through practice and become ingrained in the rider's actions, allowing them to ride a bike without consciously thinking about the steps involved.

Programming: Writing computer code involves procedural knowledge. A programmer knows the syntax and structure required to create functional software, but this knowledge is applied through the process of coding, debugging, and testing, which are learned through experience.

Technical Applications:

Procedural knowledge is embedded in expert systems, robotics, and training simulators. In 2025, digital twins and AR/VR interfaces are used to simulate procedural knowledge environments. Reinforcement learning models are a computational analogue—they learn procedural knowledge by optimizing policy functions through repeated task execution in simulated or real environments.

4. Declarative Knowledge

Declarative knowledge is factual knowledge. It consists of information that can be stated or declared, such as historical dates, mathematical formulas, and factual data. This type of knowledge is essential in academic education and forms the backbone of many standardized tests.

With AI-powered learning platforms, declarative knowledge is now customized to individual learning styles. Adaptive learning algorithms in 2025 can dynamically adjust difficulty levels, recommend revision schedules, and identify gaps in a learner’s declarative knowledge.

Technical Applications:

Declarative knowledge is the foundation of semantic knowledge graphs, ontologies, and AI search engines. In education technology, adaptive learning platforms leverage student profiles to dynamically sequence declarative content based on knowledge mastery curves and spaced repetition models.

5. Conceptual Knowledge

Conceptual knowledge involves understanding the relationships between concepts. It goes beyond facts and focuses on understanding frameworks, theories, and structures. For instance, understanding the concept of gravity involves recognizing how it relates to mass, force, and acceleration.

This type of knowledge is crucial for innovation and problem-solving. In research and development, conceptual knowledge enables scientists and engineers to connect seemingly unrelated ideas and create novel solutions. AI tools like concept mapping software and semantic search are accelerating the development of conceptual knowledge in 2025.

Technical Applications:

Conceptual knowledge is increasingly modeled through vector semantics and graph-based reasoning in 2025. Concept mapping algorithms visualize nodes (concepts) and edges (relationships). In generative AI, prompt engineering often requires a deep understanding of conceptual structures to ensure effective output generation across contexts.

6. Contextual Knowledge

Contextual knowledge refers to understanding the context in which information is used. It includes cultural, historical, and situational awareness. For example, knowing how to behave in a foreign country requires contextual knowledge of local customs, language, and etiquette.

In 2025, contextual knowledge is vital in global business operations, diplomacy, and cross-cultural collaboration. AI-powered translation tools, cultural intelligence apps, and scenario-based training modules are helping individuals and organizations build contextual knowledge.

Technical Applications:

AI agents in 2025 integrate contextual knowledge via situational embeddings, location-aware modeling, and cultural ontologies. Contextual bandits and meta-learning algorithms personalize outputs based on dynamic user and environmental data. This knowledge is critical for LLMs deployed in multilingual, multicultural, and multijurisdictional settings.

7. Experiential Knowledge

Experiential knowledge is gained through personal experience. It encompasses emotions, memories, and firsthand encounters. This type of knowledge is often visceral and deeply ingrained.

Healthcare, counseling, and customer service are domains where experiential knowledge plays a critical role. In 2025, experience-based learning is supported by reflective journals, peer sharing platforms, and AI-driven empathy training to enhance experiential understanding.

Technical Applications:

In 2025, experiential knowledge is captured through immersive technologies (e.g., wearables, emotion-sensing AI, and video diaries). Generative models analyze multimodal experiential data (text + emotion + behavior) to build empathetic systems, such as therapeutic bots or human-centric design assistants.

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Wrapping up types of knowledge 

Understanding different types of knowledge is the first step of creating better knowledge management systems in personal as well as professional lives. Every type of knowledge plays an important role in improving knowledge sharing across teams. 

If you’re looking to improve your knowledge management processes, implement Gen AI for enterprise search or simply use Gen AI to improve employee productivity - take a look at Alltius. Alltius’ Gen AI platform converse company’s organizational knowledge into an easy to search database - so your employees can find information when they want at the click of a button. 

Alltius works on PDFs, audio, video, email, conversations and more and also integrates with your current tech stack. You can start using our enterprise search platform in as little as 15 minutes! 

So why wait? Start your 14 day premium trial period and enhance your knowledge management processes in an instant. 

How Artificial Intelligence is Transforming Types of Knowledge

AI is not just a repository of data; it is becoming an active participant in the knowledge ecosystem. In 2025, AI systems are capable of generating, validating, and contextualizing various types of knowledge. From language models that help articulate tacit knowledge to machine learning algorithms that personalize declarative content, AI is reshaping how knowledge is created and consumed.

Knowledge graphs, natural language processing (NLP), and multimodal AI are driving this transformation. They enable machines to connect different types of knowledge, offering new insights and augmenting human understanding.

Knowledge in the Age of Collaboration

The shift toward collaborative knowledge is another hallmark of 2025. Knowledge is increasingly co-created through digital platforms, social networks, and decentralized systems. Wikipedia, open-source projects, and collaborative research platforms exemplify this trend.

This democratization of knowledge creation is challenging traditional hierarchies and empowering diverse voices. It also raises questions about credibility, bias, and information overload, underscoring the need for critical thinking and media literacy.

The Future of Knowledge: What Lies Ahead

As we look to the future, the types of knowledge we value and cultivate will shape our societies, economies, and technologies. Lifelong learning, interdisciplinary education, and ethical AI integration will be central to this evolution.

Organizations must foster a culture of knowledge sharing, invest in intelligent knowledge systems, and prioritize human-centered design. Individuals, too, must adapt by becoming more reflective, adaptable, and informed consumers and creators of knowledge.

How Alltius Can Help

Alltius revolutionizes insurance operations by using intelligent AI agents to streamline tasks that traditionally bog down teams. With the ability to process claims at speeds up to 70% faster, our solution automates document parsing, from FNOLs to medical records, ensuring faster and more accurate claims resolutions. In underwriting, Alltius extracts and evaluates risk from both standard and supplemental documents, allowing your team to focus on decision-making rather than paperwork.

Customer support becomes more efficient with AI copilots that handle complex queries, quickly pulling data from your core systems to provide accurate responses. This dramatically reduces response time, improving both customer satisfaction and operational efficiency. Our AI agents also assist in sales enablement by offering real-time prompts and personalized scripts tailored to each prospect, boosting conversion rates and streamlining sales workflows.

Why Alltius Is Different

  • No-code deployment that fits seamlessly into your current systems—no rip-and-replace required.
  • Fine-tuned for insurance with pre-trained models on forms, loss reports, policies, and more.
  • Scalable and secure to meet the demands of large carriers and fast-growing MGAs alike.
  • Enterprise-ready AI assistants that reduce repetitive work and elevate human decision-making—not replace it.

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Conclusion

In 2025, the landscape of knowledge is more complex and dynamic than ever. Understanding the different types of knowledge—explicit, tacit, procedural, declarative, conceptual, contextual, experiential, aesthetic, ethical, and strategic—is essential for navigating this landscape. With the rise of AI and digital transformation, our relationship with knowledge is evolving. But at its core, knowledge remains a deeply human pursuit—one that connects us, empowers us, and drives us forward.

Whether you're an educator, a leader, a technologist, or a lifelong learner, appreciating the diversity of knowledge types equips you to think more critically, act more wisely, and contribute more meaningfully in an ever-changing world.

Types of Knowledge FAQs

What are the main types of knowledge?

There are 7 types of knowledge that you need to be aware about:

  1. Explicit knowledge
  2. Implicit knowledge
  3. Tacit knowledge
  4. Procedural knowledge
  5. Declarative knowledge
  6. A Priori knowledge
  7. A Posteriori knowledge
What is a Knowledge Base?

In simple terms, a knowledge base is a centralized collection of information and resources that serves as a self-service tool for employees, customers, or both. It is designed to provide instant access to relevant information, enabling users to find answers to their questions or solve problems on their own.

What Is Knowledge Management?

Knowledge management is any process that involves efficient storing and retrieval of information in an organization. It involves planning, organization, operations and more, to ensure that any knowledge is available to any information seeker at a moment's notice.

What are the three majors of knowledge?

The 3 major types of knowledge are explicit knowledge, implicit knowledge and tacit knowledge.

What is an AI Knowledge Base?

An AI knowledge base is a centralized repository that stores and organizes vast amounts of information, data, and knowledge to support artificial intelligence (AI) systems, machine learning models, and other intelligent applications. It serves as a single source of truth, providing a unified view of an organization's knowledge, expertise, and experiences.

Knowledge FAQ Accordion

Knowledge FAQ

There are 7 types of knowledge that you need to be aware about:
- Explicit knowledge
- Implicit knowledge
- Tacit knowledge
- Procedural knowledge
- Declarative knowledge
- A Priori knowledge
- A Posteriori knowledge

In simple terms, a knowledge base is a centralized collection of information and resources that serves as a self-service tool for employees, customers, or both. It is designed to provide instant access to relevant information, enabling users to find answers to their questions or solve problems on their own.

Knowledge management is any process that involves efficient storing and retrieval of information in an organization. It involves planning, organization, operations and more, to ensure that any knowledge is available to any information seeker at a moment's notice.

The 3 major types of knowledge are explicit knowledge, implicit knowledge and tacit knowledge.

An AI knowledge base is a centralized repository that stores and organizes vast amounts of information, data, and knowledge to support artificial intelligence (AI) systems, machine learning models, and other intelligent applications. It serves as a single source of truth, providing a unified view of an organization's knowledge, expertise, and experiences.

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