AI and knowledge management: The future of smarter work
AI is rapidly reshaping the landscape of knowledge management (KM), promising new opportunities for KM but also raising many complex questions.
Incorporating AI and ‘smart’ technology is the number one priority for KM teams in 2025, according to a recent APQC survey.1 In particular, the survey highlights that generative AI for content creation and AI for content recommendation are the top technologies important to KM right now and will remain so for the next three years. AI for scaling KM and delivering more value is seen as the top opportunity for KM, according to the survey.
Artificial intelligence and machine learning are trends that will support automated extraction and personalization of information in KM. This will help ensure employees receive the exact knowledge and information they need for specific tasks, depending on their role and workflow.
AI use cases for KM
However, there is still work to do in defining the use cases for KM. This requires experimentation (within clear ethical boundaries). A starting point is to consider how AI can solve the most common KM problems. Some of these include:
Difficulty in finding relevant content
AI, particularly through large language models (LLMs) and retrieval augmented generation (RAG)2, can summarize and structure content in a way that makes sense, allowing users to quickly get to the content that matters.
Inefficient content curation and tagging
While some traditional manual curation and tagging skills may fade in value, AI can automate the tagging of content, making it a natural part of the process rather than trying to compel users to do it. This addresses the task of manual tagging that often proves unsustainable.
Information overload and difficulty in identifying high-quality knowledge
Through automation workflows, AI can help to purge redundant, outdated or trivial content that ‘pollutes’ an enterprise search or gen AI solution (with human oversight). By curating material, AI implementations can prioritize high-quality results as their primary inputs.
Lack of integration and automation in KM processes
Organizations are exploring options for introducing AI tools (e.g. Microsoft Copilot) to automate key knowledge processes and surface the best content. This includes automating the process for post-project knowledge capture.
Challenges with search effectiveness
AI can address the core search problem in KM. Although AI alone won’t ‘fix KM’, as it can only be effective if the KM foundations (metadata, taxonomy, governance) are strong, the process of preparing to introduce AI will also result in a generally more robust KM setup.
Knowledge intelligence – the next layer
AI for KM requires ‘knowledge intelligence’, i.e. it requires developing a framework where AI systems not only access explicit documentation but also draw on the organizational context and deep expertise that give knowledge its meaning.
Context matters
AI systems work best when they have more than just raw data. They need to understand the ‘why’ behind policies, the nuances embedded in workflows and the implications of past decisions. Contextual enrichment transforms unstructured knowledge into actionable insights.
Structuring the unstructured
Unstructured content – emails, discussions, notes, presentations – is the untapped currency of corporate intelligence. To unveil AI’s full value, organizations must invest in tagging, recognizing patterns and building ontologies3 that convert messy information into structured, machine-readable formats.
The human in the loop: reimagining roles
Human–machine collaboration is the next frontier in knowledge management. AI suggestions can challenge human assumptions, while expert feedback teaches and tunes the AI.
It is essential not to conflate artificial and human intelligence. AI does not equal human intelligence. Machines are powerful at pattern recognition and scale, but miss context, nuance and ethical considerations that only humans bring. The best KM systems will use AI to augment rather than replace human expertise.
Important skills to acquire now are the ability to ask the right questions of AI and to critically interpret its output.
Ethical considerations and bias
Not only should AI outputs be critically assessed, but organizations must safeguard trust, fairness and legal compliance. Knowledge management includes ethically and legally managing content and this is even more important with the introduction of AI. Establishing robust governance is essential for ethical AI use in KM. This includes defining roles and responsibilities, setting usage policies, conducting risk assessments and creating oversight committees to monitor AI performance and compliance.
Users should be able to understand how AI systems arrive at decisions or recommendations. KM platforms should incorporate explainable AI techniques and provide clear documentation to build trust and accountability.
AI models can inadvertently reinforce biases present in training data, leading to skewed recommendations or exclusionary practices. KM teams must audit AI outputs regularly, diversify training datasets and involve cross-functional stakeholders to identify and mitigate bias.
KM systems often handle sensitive organizational knowledge, including proprietary data and personal information. AI tools must comply with data protection regulations (e.g. GDPR) and implement safeguards, such as access controls, encryption and anonymization to prevent unauthorized access or data breaches.
As AI becomes increasingly embedded in KM systems, ethical considerations must be prioritized to ensure responsible and sustainable use.
Conclusion: Embracing the AI-driven knowledge era
Artificial intelligence is rapidly reshaping how organizations capture, share and leverage knowledge. From automating routine tasks to uncovering insights that were previously hidden, AI enhances knowledge management by making information more accessible, actionable and intelligent. Yet, the human element remains essential: people provide context, judgement and ethical oversight that machines cannot replicate.
By thoughtfully integrating AI into knowledge management processes, organizations can unlock new efficiencies, empower their teams and make smarter, faster decisions, but it is also important to ensure that ethical considerations and bias are carefully managed. The future of knowledge management lies in balancing a harmonious collaboration between human expertise and AI-driven intelligence, creating a smarter, more responsive and more equitable workplace.
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Footer notes:
1 APQC. 2025 Knowledge Management Priorities and Trends Survey Report. Jan 23, 2025 (https://www.apqc.org/resource-library/resource-listing/2025-knowledge-management-priorities-and-trends-survey-report).
2 RAG is a technique that combines search with AI-generated summaries.
3 Ontologies are formal representations of sets of concepts and the relationships between them within a specific domain. They help structure and organize knowledge so that it can be easily shared, reused and understood by both humans and machines.
Categorised in: Artificial intelligence and automation, Blog, Knowledge management
