What is Knowledge Management in the Technology Sector
Knowledge Management in Tech Sector Defined
Knowledge management (KM) in the technology sector refers to the systematic process of creating, capturing, organizing, and sharing trusted information and knowhow within tech organizations. It encompasses the tools, strategies, and cultural practices that help companies leverage knowledge to elevate experiences for stakeholders—customers, employees, suppliers, channel partners, and others—while improving operational performance.
In tech companies, knowledge management systems must address unique challenges like rapid innovation cycles, complex technical information, and highly specialized workforce expertise that can be difficult to capture and share.
Knowledge Management in Tech: Types of Knowledge
- What price should I quote for volume purchase of product X?
- What products did this client buy last year?
- How many support calls has client X made last year?
Insights
- What customer segments are unhappy?
- What products are creating the most service issues?
- Why is product X selling better in the US than in Europe?
Policies
- What is the return policy of laptop Model X
- What is covered by the standard warranty for product X?
- What are the requirements for a reseller to maintain tier-1 status?
- What is the support policy for platinum clients?
Procedures
- Returns
- Installation
- Scheduled maintenance
- Upgrades
Expertise
- I have requirements A, B, and C. What laptop model would you recommend?
- My computer will not reboot. How can I fix it?
- How do I apply your technology for use-case X?
Knowledge Management Important in Tech: Benefits
Driving Innovation and Efficiency
In an industry where innovation is currency, effective knowledge management prevents teams from reinventing solutions to problems that have already been solved. When contact center agents handle customer questions, contextual knowledge can be pushed to them in the flow of conversations instead of making them hunt for the “needle in the haystack.” When engineers can access existing research, code solutions, and design patterns, they can focus on creating new value rather than duplicating efforts.
Preventing Knowledge Loss
The tech sector’s high employee turnover means organizations risk losing critical institutional knowledge with each employee departure. Robust knowledge management systems, powered by AI, capture, preserve, and share expertise, reducing the impact of staff changes and accelerating onboarding for new team members.
Breaking Down Silos
Tech companies often develop specialized teams focused on specific technologies or product areas. Without effective knowledge sharing, these groups become isolated silos where valuable insights remain trapped. Cross-functional knowledge platforms enable holistic problem-solving and prevent redundant work. Moreover, trusted content and expertise in these silos can be fed to a central knowledge hub for personalized delivery in the flow of work.
Supporting Remote Work
As distributed teams become increasingly common in tech, knowledge management systems provide the infrastructure needed for effective asynchronous collaboration and knowledge sharing across time zones and locations.
Knowledge Management in Tech Sector: The Challenges
- Documentation Debt
Just as codebases accumulate technical debt, knowledge repositories become outdated without regular maintenance. Documentation frequently lags rapid development cycles, creating reliability issues. - Complex Information Architecture
The highly technical and specialized nature of information in tech companies makes it challenging to organize knowledge in ways that are both comprehensive and accessible. - Cultural Resistance
Engineers and developers often prioritize building new features over documenting their work or sharing knowledge, viewing documentation as administrative overhead rather than core work. - Knowledge Fragmentation
Many tech companies struggle with knowledge scattered across numerous tools and platforms, from wikis and documents to chat applications and code repositories.
How AI Can Help
With AI, knowledge management across industries, tech included, is undergoing rapid transformation. Here are some examples of use-cases:
- Knowledge automation: AI can help automate an array of tasks across the knowledge management process—discover, source, create, curate, publish, and optimize, speeding up the process 10X or more. When information changes, AI systems can automatically identify affected content, flag inconsistencies, and even suggest appropriate modifications.
- Conversational Search and Retrieval: AI-powered conversational search understands technical terminology, recognizes relationships between concepts, and learns from user behavior to deliver relevant results.
- Knowledge Mining: AI can analyze communications, code repositories, and other sources to surface valuable insights that might otherwise remain hidden in unstructured data. By mining conversations the best contact center agents have with customers and internal conversations among experts, AI can identify FAQs and the best answers to seed the knowledge-base and scope it for enhancement.
- Knowledge personalization: AI can understand individual roles and projects to proactively suggest personalized answers and resources.
Knowledge Management in Tech Sector: The Best Practices
Knowledge-Centered Service integrates knowledge management directly into support workflows. Instead of treating documentation as a separate activity, KCS embeds knowledge capture and refinement into problem-solving processes. Support teams document solutions as they resolve issues, creating a continuously improving knowledge base.
Integrate with document management and collaboration
Invest in tools that enable real-time collaborative documentation with features like version control, technical integrations, and robust search functionality. Platforms should support various content types including code snippets, diagrams, and video tutorials. Modern knowledge management systems provide pre-built connectors into document repositories such as SharePoint and collaboration tools like Confluence.
Establish clear governance
Define clear ownership, quality standards, and maintenance schedules for knowledge assets. Create explicit roles for knowledge management and allocate dedicated time for knowledge capture.
Integrate with development workflows
Knowledge management should be embedded in existing development processes rather than treated as a separate activity. Documentation updates should be included in definition-of-done criteria for features and code changes.
Foster a Knowledge-Sharing Culture
Create recognition programs for knowledge contributors, incorporate knowledge sharing into performance reviews, and have leadership visibly model and reward information sharing behaviors.
Knowledge Management in Tech sector: Client Success Stories
Gaming software leader is serving millions of gamers in 32 languages across 10M self-service sessions per year while assisting over 2500 contact center agents with the eGain AI Knowledge Hub.
AI cloud infrastructure provider leverages eGain AI Knowledge Hub to guide 4300 field engineers through processes such as installation, maintenance, and problem resolution across 4000 complex products.
